Mechanical Engineering Education Handbook (Education in a Competitive and Globalizing World) 1536177911, 9781536177916

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EDUCATION IN A COMPETITIVE AND GLOBALIZING WORLD

MECHANICAL ENGINEERING EDUCATION HANDBOOK

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EDUCATION IN A COMPETITIVE AND GLOBALIZING WORLD Additional books and e-books in this series can be found on Nova’s website under the Series tab.

EDUCATION IN A COMPETITIVE AND GLOBALIZING WORLD

MECHANICAL ENGINEERING EDUCATION HANDBOOK

CHARLES E. BAUKAL, JR. EDITOR

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NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the Publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book.

Library of Congress Cataloging-in-Publication Data Names: Baukal, Charles E., Jr., 1959- editor. Title: Mechanical engineering education handbook. Description: New York : Nova Science Publishers, Inc., [2020] | Series: Education in a competitive and globalizing world | Includes bibliographical references and index. | Identifiers: LCCN 2020015284 (print) | LCCN 2020015285 (ebook) | ISBN 9781536177916 (hardcover) | ISBN 9781536177923 (adobe pdf) Subjects: LCSH: Mechanical engineering--Study and teaching. Classification: LCC TJ158 .M455 2020 (print) | LCC TJ158 (ebook) | DDC 621.071/1--dc23 LC record available at https://lccn.loc.gov/2020015284 LC ebook record available at https://lccn.loc.gov/2020015285

Published by Nova Science Publishers, Inc. † New York

CONTENTS Preface

ix

Introduction

xxiii Charles E. Baukal, Jr.

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Mechanical Engineering Students’ Learning Preferences Charles E. Baukal, Jr.

1

Leveraging Technology to Elevate Pedagogy in Mechanical Engineering Teaching and Learning Krishna Pakala and Diana Bairaktarova

37

Mastery-Based Learning: From Exposure to Expertise Kurt M. DeGoede and Sara A. Atwood

71

Addressing Multiple Student Outcomes through Teamwork John L. Krohn

89

Applying Team-Based Active Learning to Expose Students to the Aerospace Design Process and Industry Practices Kenneth W. Van Treuren

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vi Chapter 6

Chapter 7

Chapter 8

Chapter 9

Contents Assessment of Conversion from Problem-Based Learning to Entrepreneurially Minded Learning in a Semester-Long Senior/Graduate Mechatronic Design Project James A. Mynderse, Jeffrey N. Shelton and Andrew L. Gerhart

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Integrating Open-Ended Problems in Engineering Courses at All Levels Matthew J. Jensen and Kimberly Demoret

177

Hands-On Design in Mechanical Engineering Education Matthew Cavalli and Dustin McNally

209

Fostering the Development of an Entrepreneurial Mindset in Engineering Experimentation Courses Kimberly E. Bigelow

235

Chapter 10

On Replacing the Steam Tables Smitesh Bakrania

255

Chapter 11

Materials Education for Mechanical Engineers Matthew Cavalli and Surojit Gupta

281

Chapter 12

Reverse Engineering: A Philosophy of Exploratory Problem Solving Dominic Halsmer, P. Wesley Odom, Jessica Fitzgerald and Taylor Tryon

Chapter 13

Chapter 14

Preparing Mechanical Engineering Students for Industry Charles E. Baukal, Jr., Mark Vaccari, Thomas DeAgostino, Carter Stokeld and Courtney Baukal Future Research Areas Charles E. Baukal, Jr.

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353

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Contents

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About the Editor

401

List of Contributors

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Index

417

PREFACE This book is believed to be the first to specifically address mechanical engineering education. It is divided into three sections: pedagogy, curriculum, and future. The pedagogy section contains seven chapters on various aspects of enhancing student learning. Chapter one concerns research regarding mechanical engineering (ME) students’ learning preferences. ME students are much more visual and prefer more problem solving compared to the general population. Chapter two is on leveraging technology to elevate pedagogy. The authors show many different ways of using technologies, such as the use of iMovie and Doceri, to enhance the practice of teaching. Chapter three on mastery-based learning concerns assessing students on what skills they can do well rather than almost solely on how well they do on exams. Chapter four discusses how team-based assignments can be used to meet multiple student outcomes. Examples are given for a fluid mechanics lab and a thermodynamics class. Chapter five describes how team-based active learning can be used to expose students to the aerospace design process and industry practices. Chapter six shows how a problem-based learning approach was converted to an entrepreneurially minded learning approach in a mechatronics design course. The application of the Kern Entrepreneurial Engineering Network (KEEN) framework showed a significant increase in the students’ entrepreneurial mindset. Chapter seven recommends the inclusion of open-

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ended problems in courses at all levels to help prepare students for realworld problems, which often have multiple possible correct solutions. Section two on curriculum has five chapters more specifically on ME courses and programs. Chapter eight advocates incorporating more handson design into the ME curriculum because of its importance in practice. Chapter nine shows an example of how an entrepreneurial mindset can be fostered and developed in an engineering experimentation course. Chapter ten demonstrates how research has shown that replacing thermodynamic tables, which students often struggle to use, with thermodynamic property charts can help students form better mental models. Chapter eleven discusses the use of active learning techniques to more effectively incorporate the teaching of materials in the ME curriculum. Chapter twelve considers how reverse engineering can be incorporated into the ME curriculum. While original design is incorporated into the ME curriculum, reverse engineering of existing designs can be a valuable addition that can help prepare MEs for professional practice. Section three has two chapters related to the future. Chapter thirteen discusses how ME students can be more effectively prepared for their future in the industry, not so much by changing the curriculum, but by changing the teaching approach. Some examples include less theory and more practice, improved problem solving and simulating the industrial work environment. The authors include those who work or have worked full time in industry and work part time or full time in academia, as well as two relatively recent ME graduates. The last chapter discusses possible future areas of research for improving mechanical engineering education. Those areas include, for example, improved course content, curriculum, communication, assessment, virtual reality, codes and standards, multimedia and innovation/entrepreneurship. Chapter 1 - Research has shown that mechanical engineering (ME) students have much different learning strategy and verbal-visual preferences than the general population. They prefer more problem solving and more visual materials. The research-based learning preferences of ME students suggests that ME education should be designed differently than traditional education which is often highly verbal and less visual with little

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problem solving. This chapter will discuss the learning preferences of ME students based on ongoing research and provide recommendations for instructional designers and instructors on how to develop more effective training materials using multimedia. There continue to be calls for improving engineering education. The U.S. National Academy of Engineering established a Committee on Engineering Education to answer the question “What will or should engineering be like in 2020?” The Phase 2 report from that committee titled Educating the Engineer of 2020 calls for the reinvention of engineering education. An important finding of that study was the importance of addressing how students learn in addition to what they learn and recommended more research into engineering education. This included how to better serve students with different learning styles and how to determine pedagogical approaches that excite them. The Journal of Engineering Education recommended further research on how engineering learners develop knowledge. Duderstad recommended “a systematic, research-based approach to innovation and continuous improvement of engineering education.” The U.S. National Academy of Engineering identified 14 grand challenges in engineering. One of those challenges is to advance personalized learning that recognizes individual preferences and aptitudes to help motivate learners to become more self-directed. While that challenge was targeted at the development of learning software by computer engineers, it applies to all types of learning and learners, including engineering students. The purpose of the ongoing research study reported here is to address the current lack of information about learning strategy and verbal-visual preferences of mechanical engineering students by determining those preferences for a sample of those students. The following research questions were considered: (1) What are the learning strategy and verbalvisual preference profiles for mechanical engineering students?, (2) How do the learning strategy and verbal-visual preferences of mechanical engineering students compare to the established norms for the general population?, and (3) What are the relationships of mechanical engineering students’ learning strategy and verbal-visual preferences to the

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demographic variables of gender, age range, class in school, ethnicity, native country, and native language? Chapter 2 - In the last several years we have observed a shift in paradigm to support students meet their educational goals. The old paradigm involved the following: passive learners, exam-driven, rolelearning, content-based, non-negotiable syllabus, objectives addressing the goals of the instructor, behavioral approach to learning and assessment, assessing isolated skills. The old paradigm strongly focused on individual learning. In the last decade, a revolution in the engineering education domain helped to address these archaic approaches. The modern paradigm involves active learners, continuous assessment, connections to real-world examples, supporting innovative and creative instructors, learned-centered outcomes, cognitive approach to learning and assessment, assessing skills, knowledge and abilities, collaborative learning. This modern paradigm of learner-centered higher education ecosystem is providing opportunities for learning and advancement of the individual in our society. We can leverage technology to positively impact the teaching and learning experience for each student in STEM and increase the flexibility with which students pursue their education. Our generation Z students are tech natives, accustomed to everything personalized and education entities need to adapt and meet the needs of this sophisticated generation of students. Elevating pedagogy with technology can vastly improve student learning experience by implementing impactful evidence-based research practices in the learning environment in ways that result in learning experiences becoming more meaningful, engaging, and impactful. Chapter 3 - The chapter presents an overview of the differences between traditional and mastery-based approaches in the context of content-focused mechanical engineering courses that have successfully implemented and refined the mastery-based approach. Rather than assessing the students on how well they performed the many skills studied (traditional grading system), with mastery-based learning (MBL) students are assessed on how many skills they can do well. Proficiency can be demonstrated by exam or other means. Students demonstrate proficiency on additional skills to earn higher grades.

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Research results suggest higher levels of student learning with this grading paradigm. In particular, students earning average or lower grades exit the course with an increased skill level over traditional grading systems. In many traditionally graded courses, the average student can almost apply the material covered in the course but does not reach full mastery of any of these skills. Chapter 4 - Teamwork has always been a double-edged sword in engineering education. Everyone recognizes the need for students to learn to work in teams on projects, as the skills learned are necessary for a successful career and advancement. However, it is inherently difficult to evaluate individual student performance in a group setting. Additionally, all instructors, at virtually any level, are aware that the workload and attainment of desired learning objectives is not distributed evenly among group members. There are, invariably, those students, or, sometimes, that one student, who put forth an inordinate portion of the effort while others simply ride along, sometimes learning by observation the objectives and sometimes not, their lack of attainment masked by the overall performance of the group. Despite this difficulty, team-based assignments can be a valuable learning tool in the classroom and lab and can be used to help assess a number of required student outcomes including some of those less amenable to measure in individual assignments. Obviously, the objectives related to teamwork must be assessed in team-based projects. In this chapter, means to use team-based assignments to assess several other learning objectives, some of which had previously proven difficult to assess by other means, will be described. Two examples, one in a laboratory course and one from a lecture course, will be presented with examples of the various learning objectives addressed and personal experience gained from the use of teams in these classes. Chapter 5 - Aerospace continues to be a popular industry for engineering graduates. Aerospace and Defense (A&D) industries in the United States are expecting to hire over 86,000 people in 2019, a 30% increase over what was initially projected. With the continued increase in retirements of employees over 60, A&D is a great job market for

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graduating engineers. Surveys show among university students that 48% of female, 55% of black, 49% of Latino and 70% of white students are considering a career path in A&D. Students aspiring to enter this field are attracted because of their interest in aircraft/defense/space, the technological challenge, and the opportunity for advancement. More is being done to attract women and minorities. A&D companies also have a strong STEM outreach in the elementary, middle, and high schools to address workforce needs. The jobs will be there for interested students who choose this career path. Aerospace is an important industry for Baylor University and the local community. There are over 30 aviation related business in Waco, TX and three airports. The three higher education institutions, Baylor University, Texas State Technical College and McClennan Community College, have education opportunities in aerospace engineering, aircraft maintenance and airport management. They provide a pipeline of talented workers from the local area for this industry. The Greater Waco Aviation Alliance, connected to the Waco Chamber of Commerce, coordinates activities with these companies and schools, providing scholarships to students who are pursuing degrees in aviation/aerospace or related engineering programs at the three schools. The community is behind local aerospace students who will help build the local workforce. Chapter 6 - Entrepreneurial education in the United States has existed within business schools since the end of World War II, but entrepreneurial education within engineering is now catching up. Among others, the National Science Foundation’s (NSF) Epicenter Pathways Initiative, NSF’s I-Corps Program, and the Kern Entrepreneurial Engineering Network (KEEN), have incentivized engineering faculty training in entrepreneurial education and the number of engineering-specific entrepreneurial education publications is increasing at about the same rate as publications on entrepreneurial education across all disciplines.. At Lawrence Technological University, this shift in engineering curriculum resulted in a multiyear effort to incorporate entrepreneurial education throughout the engineering curriculum.

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Entrepreneurially minded learning (EML) activities, as championed by KEEN, combine problem-based or project-based learning activities with student skills associated with an entrepreneurial mindset. For examples, these additional skills might include integrating information from many sources to gain insight, conveying engineering solutions in economic terms, and identifying unexpected opportunities to create value. EML activities emphasize “discovery, opportunity identification, and value creation with attention given to effectual thinking over causal (predictive) thinking”. As a partner school in KEEN, Lawrence Tech uses the KEEN framework to define an entrepreneurial mindset. The KEEN framework begins with the “three Cs” of Curiosity, Connections, and Creating Value (Kern Entrepreneurial Engineering Network, n.d.). Each element is supported by two example student behaviors that describe typical actions displayed by those operating with an entrepreneurial perspective. For instance, Curiosity is demonstrated by “explore a contrarian view of accepted solutions” and Creating Value is demonstrated by “identify unexpected opportunities to create extraordinary value.” The framework continues from the three Cs to Engineering Thought and Action, Collaboration, Communication, and Character. As with the three Cs, each concept is supported by example student behaviors. In total, the framework includes 18 example student behaviors. Within engineering and the KEEN framework in particular, an entrepreneurial mindset is not the same as entrepreneurship. While business schools have historically focused on wealth-creation or firmcreation, the engineering-specific entrepreneurial mindset is thinking like an entrepreneur. This means the application of the KEEN framework “three Cs” to engineering practice, but not necessarily the creation of new business. Inclusion of entrepreneurial education is a valuable addition to the traditional engineering curriculum and aligns with portions of the previous ABET Criterion 3a-k. In this work, a senior/graduate course in mechatronic design was modified to convert an existing design project to a problem-based learning exercise and then to an EML exercise. While an argument could be made

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that the inclusion of social value in the PBL offerings added components of an entrepreneurial mindset, these PBLs lacked attention to economic value. PBL-to-EML modifications were made to provide a specific customer for design tasks, increase student focus on economic drivers, and provide student teams with ambiguity in stakeholder requirements. This work assesses the student demonstration of sample behaviors associated with an entrepreneurial mindset and provides comparison before and after EML course modifications. Chapter 7 - Real world engineering problems rarely, if ever, have a single correct answer. An automobile can be powered by a gasoline engine, a diesel engine, an electric engine, or even a combination of engine types. None of these solutions are incorrect; they can all provide sufficient power to move a vehicle from point A to point B. As a result, we often evaluate the different solutions based on a set of criteria, thus finding the best solution given constraints such as cost, size, weight, etc. However, the “best” solution can vary based on the person evaluating it, leading to multiple correct answers. Utilizing this type of approach, engineers can end up with sports cars, luxury cars, compact cars, trucks and sport utility vehicles being produced to fulfill the same basic requirement, travel from point A to point B. Mechanical engineering curriculums are heavily rooted in math and science, courses that at their fundamental level solve problems that have one single correct answer. Similarly, most traditional engineering courses heavily emphasize the analysis of existing systems and utilize problems with a single correct answer. Problems with single answers can be useful tools for students and instructors to gauge mastery of concepts, but the curricular emphasis on analytic skills can leave students poorly prepared when they encounter open-ended problems in higher level courses, capstone design, and in the workplace. As a result, open-ended problems may present challenges for students. There are different levels of ambiguity, complexity and choice associated with open-ended problems. In some cases, the requirements are clearly defined, but a design solution must be generated. In other cases, students may have considerable latitude in defining the problem itself.

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Classic engineering design is traditionally described as an iterative cycle of synthesis, analysis, and evaluation. Engineering students can struggle when they are asked to move beyond analysis of an existing system, but developing the design skills required to solve open-ended problems can lead to a greater sense of mastery, and are required by ABET for accredited engineering degrees. This chapter will discuss different types of open-ended problems, as well as how to implement and assess open-ended problems in courses of all levels. Detailed examples will be included to ease potential barriers to adopting open-ended problems for instructors of all levels and backgrounds. Chapter 8 - Most mechanical engineering graduates will spend a majority of their careers in jobs that require them to regularly apply the concept of design, i.e., to identify a set of requirements that need to be met and then to specify a process or product that can satisfy them. While the concept of design is straightforward in theory, the implementation can be quite complex and a wide variety of approaches to design exist. How best, then, to teach the skill of designing to undergraduate mechanical engineers? This chapter will explore the advantages of hands-on design experiences in the mechanical engineering curriculum and provide ideas for how best to support the professional development of novice designers. For the authors’ purposes, ‘hands-on’ is assumed to be a process in which students are expected to create and test or evaluate one or more physical models or prototypes, in addition to any paper- or computer-based analytical work. Chapter 9 - This chapter describes the growing attention to and interest in incorporating entrepreneurially-minded learning (EML) across the engineering curriculum. While EML is most commonly incorporated into courses focused on engineering design, this chapter provides an example of how EML can be successfully and impactfully integrated into an engineering experimentation course. The chapter utilizes the EML framework as introduced by the Kern Entrepreneurial Engineering Network (KEEN). The chapter first introduces a background on EML and the benefits of integrating it into the mechanical engineering curriculum.

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The chapter then details the opportunities that exist to bring EML into experimentation and lab-based classes and provides details about the changes to course structure that were made to an existing engineering experimentation course (required junior-level mechanical engineering course) to better infuse EML throughout the semester. The chapter concludes by discussing the outcomes of this initiative and providing recommendations for other instructors wishing to adopt a similar approach. Chapter 10 - Steam tables have been an essential mechanical engineering tool for solving common power cycles within engineering thermodynamics. Nearly all thermodynamics textbooks include several pages of tabulated properties of water known as the steam tables. Specifically, the steam tables present key thermodynamic properties as a function of pressure P and temperature T. The tables are conventionally divided by mixture and vapor phases of water and further subdivided by pressures. Instructors must teach this tool to study power generation cycles, such as Rankine Cycles. Students use this tool to retrieve state properties and compute changes within thermodynamic systems. Often, students find the table-based retrieval process challenging to master. The retrieval process can be involved with multiple interpolation steps to identify the correct state properties before solving the engineering problem at hand. To develop mastery, instructors typically dedicate both time and effort for students. The rationale for continuing to teach the use of steam tables is steeped in tradition. Its continued use within the Fundamentals of Engineering (FE) exam reinforces its relevance. As a result, steam tables continue to occupy the precious textbook real-estate even in the digital age. Naturally, students, as resourceful as they are at reducing hardship, quickly realize that there are easier alternatives to retrieving state properties from the tables. The same algorithm that allows our students to retrieve table properties can be employed by a computer algorithm. Thus, numerous digital and online alternatives exist that rapidly supply state properties of water. These include computer applications (e.g., EES), algorithms (e.g., X Steam for MATLAB, PyroMAT for Python), web-based tools (e.g., NIST Webbook, IRC Fluid Property Calculator, & SmoWeb Property Calculator), or mobile apps (e.g., International Steam Tables & Steam

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Tables). These computer-based alternatives of the traditional paper-based steam tables promptly deliver the unknown property given two known properties of a state without having to go through the tortuous process by hand. The computer-based alternatives are also part of the standard practice within professional engineering where it is part of a larger system of design tools. Under the circumstances, should educators replace the steam tables with computer-based tools to better align with engineering practice? This chapter details an effective instructional framework for integrating property charts within thermodynamics courses to reinforce water property relations. Instructional framework and relevant tools are presented to facilitate adoption of this visual approach and advance thermodynamics instruction to match the current educational needs. The framework is supported by effectiveness studies and student feedback on the practice. A summary of the evidence is presented within this chapter. The outcomes are overwhelmingly positive and easily transferable to motivate broad pedagogical change within engineering thermodynamics. Chapter 11 - Every mechanical engineering design requires the use of real materials. Understanding how those materials behave is an essential consideration in creating safe, effective, and efficient machines and structures. Many mechanical engineering curricula have a single required course that focuses on materials behavior. Other materials-focused courses may be offered as electives. Materials-related topics are sprinkled throughout a number of additional courses such as mechanics of materials, heat and mass transfer, and thermodynamics. What are effective techniques for teaching the concepts of materials behavior (and their relationship to mechanical engineering design) in that limited amount of time? This chapter will investigate the teaching of materials science to mechanical engineering students via the implementation of various active learning techniques including distance learning options. The authors will also touch on contemporary topics in materials education including entrepreneurship and sustainable materials in support of Circular Economy. Chapter 12 - The practice of engineering has been characterized in a multitude of ways, as seen in the fascinating compilation of twenty-one definitions of engineering by Landis, Peuker and Mott. But if engineering

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generally involves the design of a system to solve a problem or serve a purpose, then reverse engineering is simply the exploration of an already existing engineered system to better understand the thinking that went into its design. Landis, Peuker and Mott define reverse engineering as “the process of taking apart a device, object or system to see how it works in order to duplicate or enhance it.” Reverse Engineering projects may be undertaken for a variety of reasons, from realizing the benefits that accrue from a deeper knowledge of a competitor’s product, to the satisfaction of your own curiosity about how something works. Chapter 13 - There can often be a rude awakening for engineering students transitioning from academia to the work world. Teachers can help make that transition smoother by preparing students for full time employment which is often significantly different than academia in some important ways. An important lesson learned from an extensive study of engineering education is the “imperative of teaching for professional practice”. Van Treuren et al. wrote, “As educators, the foremost goal is to graduate students technically prepared to fulfil their degree requirements. While they may be technically competent, certified by diploma, have we as educators prepared our students to meet the challenges in the workplace, whatever they may be?” They further wrote, “the transition of our students into being a productive adult in this field, beyond technical competency, still lies with the faculty and institution.” This chapter will look at some of the differences between the theory learned in school compared to actual practice, consider a range of topics related to problem solving, look at some key differences between the industrial work environment and academia, get perspectives from some newer engineering graduates and from instructors with ample industry experience, and finish with some recommendations. Chapter 14 - There continue to be calls for improving engineering education. The U.S. National Academy of Engineering established a Committee on Engineering Education to answer the question “What will or should engineering be like in 2020?” The Phase 2 report (National Academy of Engineering) from that committee titled Educating the Engineer of 2020 calls for the reinvention of engineering education. An

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important recommendation of that study was the importance of academic management such as engineering deans endorsing engineering education. This includes studying how undergraduate engineering students learn to determine how to better serve students with different learning styles and what pedagogical approaches excite them. The report notes that while progress has been made in determining best practices for engineering education, much remains to be done. The Journal of Engineering Education recommends five research areas for engineering education: engineering epistemologies, engineering learning mechanisms, engineering learning systems, engineering diversity and inclusiveness, and engineering assessment. Duderstad recommends “a systematic, research-based approach to innovation and continuous improvement of engineering education.” The U.S. National Academy of Engineering identified 14 grand challenges in engineering. One of those challenges is to advance personalized learning that recognizes individual preferences and aptitudes to help motivate learners to become more self-directed. While that challenge was targeted at the development of learning software by computer engineers, it applies to all types of learning and learners, including engineering students. This chapter focuses on potential future mechanical engineering education research topics, although many of them will also apply to other engineering disciplines. It is not intended to be exhaustive or even comprehensive. It is clearly influenced by the author’s experiences and biases. It is intended to present a number of topics that in the opinion of the author could benefit from disciplined research study.

INTRODUCTION Charles E. Baukal, Jr. John Zink Hamworthy Combustion, Tulsa, OK, USA Oklahoma State University Tulsa, OK, USA Oral Roberts University Tulsa, OK, USA University of Tulsa Tulsa, OK, USA

Keywords: literature review; engineering education; organizations; journals; workshops

INTRODUCTION Engineering is often described as problem solving (Shepherd et al. 2009, p. 3). The National Academy of Engineering report (2004, p. 43) The Engineer of 2020 describes engineering as “problem recognition, formulation, and solution.” Jonassen (2014, p. 103) writes, “Learning to solve workplace problems is an essential learning outcome for any engineering graduate.” A key ABET (2020) student outcome is “an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.” In a report

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published by UNESCO (2010, p. 24), engineering is described as “the field or discipline, practice, profession and art that relates to the development, acquisition and application of technical, scientific and mathematical knowledge about the understanding, design, development, invention, innovation and use of materials, machines, structures, systems and processes for specific purposes.” The report (p. 125) describes mechanical engineering as “one of the oldest and most diverse branches of engineering covering the design, production and use of tools, machines and engines, and can therefore be considered a central feature of the transition from ape to tool-designing and tool-using human.” Aubrey Burstall (1963) has written a useful history of mechanical engineering from before 3000 BC to 1960 which is generously illustrated with 290 figures. Over time, people have discovered ways to make their jobs easier and faster by designing and building devices and machines. Many of the early advancements concerned how to make and use materials such as metals and woods. Many of those advancements are impressive because they came before the advent of electricity and internal combustion engines for generating power. There are only brief discussions of education starting with a school in Paris founded in 1795, polytechnic schools in some German cities by the 1830s, and engineering programs in Glasgow by 1840. Many “engineers” in the early years were self-taught or learned through apprenticeships. Zhang and Yang (2020) have written a history of mechanical engineering. They begin their discussion with the development of copper vessels about 3800 BC. Primitive ploughs date back to at least 3500 BC. Most of the early devices centered around agriculture, war, and textiles. A pulley was first documented about 1500 BC. The book contains numerous images and references. Mechanical engineering had a significant impact on the various industrial revolutions. Some major inventions from the First Industrial Revolution in the second half of the 18th century include the steam engine, Stirling engine, submarine, hydraulic press, and milling machines. Some significant discoveries in the Second Industrial Revolution in the second half of the 19th century include electricity, the internal combustion engine, the automobile, and the airplane. The

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development of some important ME subjects such as mechanics of materials and fluid mechanics are discussed. The authors trace the establishment of mechanical engineering as a discipline to the first half of the 19th century. The birth of modern engineering education is traced to 1794 at École Polytechnique in Paris. The first ME society was the Institution of Mechanical Engineers (IMechE) which was formed in 1847. The two basic approaches to education are the general (e.g., U.S.) and the broad specialization (e.g., Germany). The Third Industrial Revolution began after WWII and included the development of nuclear power, the computer, and rockets. Mechanical engineering continues to play an important role in society into the 21st century. When the American Society for Mechanical Engineers (ASME) started in 1880, mechanical engineering was already an established profession (Gianniny, 1979). It had its origins in the metal-working machine shop (Calvert, 1967). Mechanical engineering education had its roots in mechanics’ institutes which started in the 1820s. New programs specifically in mechanical engineering saw significant growth after the passage of the Morrill Land-Grant Colleges Act of 1862. Mechanical engineering became a separate curriculum at the Massachusetts Institute of Technology in 1865. Stephan et al. (2018) describe a mechanical engineer as involving “areas related to machine design, manufacturing, energy production and control, materials, and transportation.” ASME hosted its first education conference in 1973 at Iowa State University.

LITERATURE REVIEW While much has been written about engineering education, very little has been written specifically about mechanical engineering education. This section will give a comprehensive but not exhaustive review of engineering education publications that are primarily books and reports. The references are presented in chronological order. Walter Vincenti (1990) has written a book on engineering knowledge development and used historical developments in the field of aeronautics to

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illustrate this. From 1908 – 1950, these developments include the Davis wing, flying-quality specifications for American aircraft, control-volume analysis, air-propeller testing by Durand and Lesley, and the innovation of flush riveting in airplanes. He argued engineering development is not dependent on but rather separate from that in science. Design in engineering is a critical difference compared to science. ABET Student Outcome number two is directly related to engineering design: “an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.” Lawrence Grayson (1993) has written a book on the history of engineering education in the U.S. and Canada. A consistent theme is the interplay between broad social change and the resulting stresses that helped shape engineering education. The two World Wars and the space race had significant impacts. The book starts in 1893 which is the year the Society for the Promotion of Engineering Education (SPEE) which later became known as the American Society for Engineering Education (ASEE) was started but does consider earlier times as well including the Revolutionary War. The book is generously illustrated with 335 photos of the many engineering projects that have been completed over the years and many of the institutions involved in engineering education. It also includes brief biographies of some of the key people in the history of engineering education. The keystone recommendation was that the National Science Foundation should expand and reinvigorate its efforts to stimulate and disseminate innovation in engineering education. Fox and Hackerman (2003) edited a book on excellence in teaching undergraduate science, mathematics, engineering, and technology. They present rigorous methods for evaluating engineering education because the perception of many faculty at research institutions is that research is valued above teaching in part because there is no rigorous way to evaluate teaching and learning. A rigorous and fair method of evaluation is needed to properly reward faculty and to drive curriculum changes. Most faculty who teach undergraduate STEM disciplines receive little formal training in

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teaching and assessment techniques. Overall recommendations of the study included: 

 

 

Teaching effectiveness should be judged by the extent of student learning, not on the specific styles or methods used as many may be effective Scholarly activities related to improving teaching and learning should be recognized and rewarded Summative assessments of teaching should rely not only on student evaluations but also on peer evaluations and teaching portfolios Faculty should be rewarded for improving student learning Faculty should accept the responsibility to improve their teaching skills

The National Academy of Engineering Committee on Engineering Education (2004) issued a report looking at the future of engineering and recommends changes to engineering education to meet the forecast changes. The task force attempted to answer the question, “What will or should engineering be like in 2020” (p. 2). A companion report by the National Academy of Engineering Committee on Engineering Education (2005) gave specific recommendations on how the undergraduate engineering curriculum should be re-engineered (pp. 2-3):     

B.S. degree should be considered as a pre-engineering or “engineering in training” degree M.S. degree should be accredited and recognized as the engineering professional degree Students should be introduced to the essence of engineering early in the undergraduate program Engineering education research should be recognized and endorsed by colleges and universities Institutions should teach students how to be lifelong learners

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  

Charles E. Baukal, Jr. Interdisciplinary learning should be encouraged Four-year institutions should work to develop articulation agreements with two-year community colleges engineering programs Institutions should encourage U.S. students to obtain M.S. and Ph.D. degrees Efforts should be encouraged to improve the public’s understanding of engineering National Science Foundation should collect or assist collection of data on program approaches and student outcomes to help freshmen better understand available engineering baccalaureate programs.

Reinventing the engineering curriculum requires that industry and academy work together. Gretar Tryggvason and Diran Apelian (2006) wrote in a journal opinion piece that engineering education needs to be re-engineered to meet the challenges of the 21st century. They noted that a country’s prosperity is due in part to the education of its engineers. They believe the entrepreneurial engineer can find information quickly, understands engineering basics, has effective communication skills and understands global and current issues, and has the imagination and managerial skills to identify and meet needs with new solutions. The National Science Board (2007) issued a report detailing findings and recommendations for the National Science Foundation to support innovations in engineering education programs. It is the result of two public workshops which had the goal of “moving forward the national conversation on engineering issues by calling attention to how engineering education must change in light of changing workforce demographics and needs” (p. 1). Three key challenges for engineering education were identified: responding to the changing needs for engineers, changing the perception of engineering, and retaining top students. A report by the Royal Academy of Engineering (2007) is the result of a working group commissioned to address educating engineers in the 21st

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century. It notes the growing concern in the UK that the education system responsible for producing new generations of engineers is failing to keep pace with the rapid changes in society and technology and the increasing need for engineers. Nine key messages from the study are: 1. There will be an increasing shortage of high caliber engineering graduates entering industry 2. More funding is needed to ensure effective engineering education courses 3. Ensure the UK MEng qualification is not undermined by the development of the proposed European Qualifications Framework under the Bologna agreement 4. Engineering courses need to adapt to changing industry requirements 5. Teaching prestige has been compromised by a disproportionate emphasis on research 6. More effective interaction between industry and university engineering departments is needed 7. Core engineering courses need to continue to be emphasized and not diluted by peripheral subject matter 8. Accreditation should inform the development of course content to ensure graduates meet industry needs 9. More needs to be done to ensure engineering is perceived as an exciting, stimulating, and well-paid career. Specific recommendations are given to government, engineering institutions, and industry to improve engineering education. James Duderstadt (2008), former president of the University of Michigan, wrote a report as part of the Millennium Project designed to be a roadmap to the future of engineering practice, research, and education. Of the context he writes, “The changing workforce and technology needs of a global knowledge economy are dramatically changing the nature of engineering practice, demanding far broader skills than simply the mastery of scientific and technological disciplines. The purpose of the study was to

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pull together the principal findings and recommendations of the various reports concerning the profession of engineering, the technology and innovation needs of the nation, and the role played by human and intellectual capital, into an analysis of the changing nature of engineering practice, research, and education. The following are the key conclusions: 







The transformation of knowledge into products, process, and services is critical to competitiveness, long-term productivity growth, and the generation of wealth American engineers must be able to add significantly more value than their counterparts abroad through their great intellectual span, their capacity to innovate, their entrepreneurial zeal, and their ability to address the grand challenges facing the world It is essential to elevate the status of the engineering professions, providing it with the prestige and influence to play the role it must in an increasingly technology-driven world while creating sufficiently flexible and satisfying career paths to attract a diverse population of outstanding students Engineering education should encompass a broader educational experience with more liberal arts in the undergraduate education which is common in other professions such as law and medicine

Patricia Galloway, past and first woman president of the American Society for Civil Engineers, has written a book (2008) calling for reform in engineering education. Her premise is that engineering education has not kept pace with the numerous changes that have occurred in technology and society. She believes engineers need to be better communicators and better prepared to work in a global economy. She also discusses the importance of ethics, professionalism, diversity, leadership, and the engineer’s role in public policy. She also proposes a Master of Professional Engineering Management. Spurlin, Rajala, and Lavelle (2008) have edited a book on effective assessment of engineering education programs designed primarily for engineering faculty and department chairs as part of the ABET

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accreditation process. The book is divided into four sections: assessment basics, barriers and challenges, learning along the continuum of the educational experience, and the future. The book is tailored to the ABET requirements, is generously referenced, includes examples, and also includes rubrics. There is also a chapter on using assessment in graduate education programs. A key barrier to assessment is overcoming the resistance to change. An important element requiring assessment is the capstone project. A useful glossary of terms is also included. Zawojewski, Diefes-Dux, and Bowman (2008) have edited a book on modeling in engineering education. The objective is to bring real-world modeling into the classroom. A process is described that includes the following model-eliciting activities (MEAs): construction, reality, selfassessment, documentation, shareability and reusability, and effective prototype. Chapters are given of applying the process to a first-year and upper-level engineering courses. Chapters on learning from student surveys, interviews, discussion boards, designing in small group mathematical modeling, by observation, from teaching assistants, and from a faculty self-study are also presented. The final chapter concerns future research which focuses on three problems in engineering education research: course quality, diversity, and emerging technology. Shepherd, Macatangay, Colby, and Sullivan (2009) have written a book sponsored by the Carnegie Foundation for the Advancement of Teaching which contains twenty-two chapters divided into six parts: preparing the new-century engineer, a foundation to build on, a place to explore, a way to create, affecting the world, and bringing professional practice forward. The primary goal of the book was to “understand, through field research, how the educational practices of the schools from future engineers” (p. xix). The primary finding was “that in the midst of a profound, worldwide transformation in the engineering profession, undergraduate engineering education in the United States is holding on to an approach to problem solving and knowledge acquisition that is consistent with practice the profession has left behind” (p. xxi). While there are pockets of innovation, much more needs to be done to reform the traditional curriculum. Five attributes are listed for the new-century

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engineer: strong analytical skills, practical ingenuity, creativity, communicator, and master of principles of business and management (p. 9). The authors found a widespread lack of enthusiasm for reforming the engineering education curriculum and call for a reformed curriculum that makes a much stronger connection between what is taught in the classroom and what is practiced in the profession. Ryan Davidson (2010) reported on a 1.5 day workshop sponsored by the National Science Foundation to consider how engineering curricula could be enhanced to better prepare future engineers. General themes of the workshop included: focusing more on inductive teaching and learning which is more learner-centered, integrating relevant topics across STEM fields, and making more extensive use of learning technologies. Suggestions for promoting curricular innovation included: expanding academic communication networks, increasing faculty incentives, and promoting interactions among all engineering education stakeholders. Manjit Sidhu (2010) has written a book on computer aided learning applied to engineering education. It includes chapters on multimedia and learning styles, user interface design, hardware and software for multimedia design, evaluating interactive multimedia packages, and various aspects of the TAPS (technology-assisted problem solving) packages. There is a chapter specifically on the use of TAPS in a mechanical engineering course. UNESCO (2010) issued an extensive (392 pages) report with the premise that “mobilizing the engineering community to become more effective in delivering real products and services of benefit to society, especially in the developing world, is a vitally important international responsibility” (p. 5). The purpose of the report is “to contribute to greater international understanding of the issues, challenges and opportunities facing engineering, with a particular focus on contributions of our discipline to sustainable development” (p. 5). While it considers all aspects of engineering, the discussion on transforming “engineering education, curricula and teaching methods to emphasize relevance and a problemsolving approach to engineering” (p. 6) is particularly relevant. Chapter seven focuses on transforming engineering education and training with

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specific discussions of problem-based learning, sustainability, curriculum renewal, environmental engineering education, and engineering education research. The ASME Center for Education (2011) issued a report for a vision of mechanical engineering education in the year 2030. The task force commissioned by ASME had two primary objectives (pp. 6-7): “help define the knowledge and skills that mechanical engineering or mechanical engineering technology graduates should have to be globally competitive, and, to provide, and advocate for their adoption, recommendations for mechanical engineering education curricula, with the goal of providing graduates with improved expertise for successful professional practice.” A key finding of the study is that curricula change slowly but there are pathways for change. The task force recommended making the undergraduate mechanical engineering education curriculum that inspires innovation and creativity, is more flexible, offers more authentic practicebased experiences, better develops students’ professional skills, attracts a more diverse student body, and focuses on specialization for post-graduate education. Ruth Graham (2012) authored a report sponsored by the Royal Academy of Engineering that reiterates the numerous calls to reform undergraduate engineering education but notes the challenge in making that happen. The focus of the report was on how successful change can be initiated, implemented, and sustained. The report was based on interviews with 70 experts from 15 countries with experience in successful engineering education curriculum changes. Four common features were identified from these changes: 



Successful systemic changes often resulted from circumstances, such as significant threats in the marketplace to a school or program, where change was unavoidable Changes needed to be driven across the entire curriculum and by the entire faculty; those only involving a handful of courses and faculty were not typically sustainable

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Charles E. Baukal, Jr. Support from the department and department head were critical factors in successful change Problems were often encountered after successful implementation due to a slow drift back to the traditional curriculum

Singer, Nielsen, and Schweingruber (2012) reported on two workshops in 2008 that looked at promising practices, particularly research-based instructional strategies, in undergraduate engineering (along with science, technology, and mathematics). One aspect of the study was to look at instructional practices shown to increase the performance of specific student groups, especially low socioeconomic status, minority, and female students. A more student-centered instructional approach is advocated where there is more interaction, student participation is encouraged, and more student collaboration activities. Unfortunately, national surveys show that current engineering faculty are the least likely to use student-centered or collaborative instruction and rely primarily on lectures. Crawley, Malmqvist, Östlund, Brodeur, and Edstrom (2014) have written a book advocating for reforms in engineering education using the CDIO (conceive-design-implement-operate) approach. They, like many others, argue there is need to reform engineering education and propose that the CDIO approach is the best way to accomplish this. They list the following professional aspects as the context for engineering education:       

Focus on the needs of customers, Delivery of products, processes, and systems Incorporation of new inventions and technologies Focus on the solution, not disciplines Working with others Effective communication Working within resources

Examples are given of the implementation of CDIO at multiple institutions under a variety of situations ranging from introductory courses to capstone projects.

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Paulo Davim (2014) has edited a book on curriculum, pedagogy, and didactic aspects of engineering education. There are chapters on the influences of personality traits on academic performance through imaginative capability, developing a personalized and adapted curriculum through an ambient intelligence environment, evaluation supporting stakeholder-centered design and continuous quality improvement, software engineering education, central occupation requirements for engineering jobs, and energy engineering as an emerging discipline. Goldberg and Sommerville (2014) tell the stories of the Olin College of Engineering (Needham, MA) and the iFoundry incubator at the University of Illinois which both have developed innovative engineering education curricula that have the potential to significantly improve student learning and effectiveness. Olin was initially founded specifically to develop a radically different engineering education curriculum. iFoundry was started with a grant from the National Science Foundation’s Engineering Research Center program. Olin and iFoundry have partnered on innovating the engineering curriculum. The authors argue engineering education has missed three important revolutions: entrepreneurial, quality, and information technology. They advocate for a change from a carrot and stick approach to teaching and learning to one where students are intrinsically motivated to learn where the professor is more of a coach than an expert. While change is certainly needed, it will not be easy. Johri Olds (2014) has edited an extensive book on engineering education research with numerous international authors, thirty-six chapters, and six parts: engineering thinking and knowing, engineering learning mechanisms and approaches, pathways into diversity and inclusiveness, engineering education and institutional practices, research methods and assessment, and cross-cutting issues and perspectives. A very wide range of aspects of engineering education research is considered including, for example, learning theories, problem solving, problem- and project-based learning, curriculum design, engineering design, retention, social justice and inclusion, community engagement, research methods, assessment, communication, global issues, ethics, and interdisciplinarity. Numerous references are provided.

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Flumerfelt, Kahlen, Alves, and Siriban-Manalang (2015) have written a book on lean engineering education designed to drive content and competency mastery specifically for mechanical engineering students. It continues the discussion of the ASME 2030 (2011) vision. It calls for plans to improve the future of mechanical engineering education to prepare graduates for job opportunities in 2030. It identifies the key stakeholders as students, faculty, employers, and society who all need to have input into improving mechanical engineering education. Competencies that will be needed include systems, sustainability, and ethics. A suggested lean process for improving ME education is the Plan, Do, Check, Act process suggested by the Toyota Education Model. Many examples are presented for content- and competency-based lean engineering education processes. Linda Kober (2015) has written a book sponsored by the National Research Council on research-based instructional methods for undergraduate science and engineering students. Its primary audience is those teaching those students with the goal of more student-centered instruction to promote more learning. Learning goals should be set for each course to drive instruction. Instructors can collaborate with fellow instructors to form learning communities. Learning workshops are recommended before implementing a new instructional method. Problem solving is a key skill for engineers which can be taught. A wide range of instructional techniques and strategies are presented including example implementations including making lectures more effective. Collaborative peer learning, supplemental tutorials, and authentic experiences are examples of research-based methods that have been proven to enhance learning. Assessments should be used to improve instructional methods. While not all methods will be effective in every course, instructors are encouraged to try new techniques that have the potential to significantly increase student learning. Strong institutional support is needed to make many of the changes recommended to improve engineering education. Valenzuela-Valdés and Fernández (2015) have edited a book on project-based learning (PBL) including an introduction and foundations of PBL. One chapter gives an example of PBL applied to telematics engineering and another on information and communications technology

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(ICT). All of the authors were from Spanish institutions except for one from Switzerland. Annetta and Minogue (2016) have edited a book on connecting science and engineering education practices. While science and engineering are distinct disciplines, they are both important in engineering education. There are chapters on educational games, museum design experiences, principles for supporting design activity, enhancing integrative STEM literacy through engineering design, an integrated STEM instructional model (iSTEM), a STEAM-based curricula for robotics, educational games for learning biology, a language of design within science and engineering, teaching with design thinking, elementary school engineering for fictional clients in children’s literature, teaching engineering design in elementary science methods classes, infusing engineering concepts into high school science, and using mathematics in design-based lessons about a biological process. One chapter discusses the following grand design challenges in engineering education: 1. 2. 3. 4. 5. 6. 7. 8.

Explaining technology Explaining what engineers do Developing new curriculum materials Teaching the design process Developing assessments Teaching the teachers Balancing technical and academic subjects Engaging technology and career and technology education (CTE) teachers 9. Teaching the teacher educators Frerich, Meisen, Richert, Petermann, Jeschke, Wilkesmann, and Takkaya (2016) have edited a nearly one thousand page book reporting on the results of a project entitled Excellent Teaching and Learning in Engineering Sciences sponsored by a consortium of RWTH Aachen University, Ruhr-Universität Bochum, and TU Dortmund University which is where the authors came from.

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There are many chapters divided into the following sections: virtual learning environments, mobility and internationalization, student lifestyle, and professional competency. Some of the chapters are very specific such as the self-optimized assembly planning for a ROS-based robot cell and bitmap-based online analytical processing of time interval data. Some are more general such as a chapter on massive open online courses in engineering education, incorporating the virtual theatre into engineering education, virtual and remote labs, and educating engineers for a globalized world. Most of the chapters are written in English but some are in German. John Heywood (2016) has written a book about assessing learning in engineering education. An objective of the book is to show how proper assessment of courses and programs can lead to needed improvements. He provides nuts and bolts for how to do assessments including examples and case studies. The book includes discussions of topics such as emotional intelligence, peer and self-assessments, and portfolios. It also includes generous references and is well-documented. The impact of accreditation on assessment is also considered. Guerra, Ulseth, and Kolmos (2017) have edited a book on problembased learning (PBL) in engineering education from an international perspective. The book provides specific examples of how PBL has been used at various institutions. The book shows the diversity of ways PBL may be used in an engineering curriculum. Heywood (2018) has written a book on empowering professional teaching in engineering as part of the annual Synthesis Lectures on Engineering started in 2006. The book covers a wide range of topics such as teaching research, objectives and outcomes, problem solving, critical thinking, decision making, intellectual development, and concept learning. Mutis, Fruchter, and Menassa (2018) have edited a book on transforming engineering education, with specific emphasis on civil engineering. It includes chapters on foundational courses such as teaching computers to civil engineering students, building information modeling (BIM) curriculum, and augmented and virtual reality applications.

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Plato Kapranos (2019) has edited a book on the interdisciplinary future of engineering education. The first chapter makes the case for new pedagogies. The second chapter describes the past and future of engineering education. The third chapter considers the pedagogical and cost advantages of a multidisciplinary approach to delivering practical teaching. Chapter four argues for a more liberal engineering program with almost no lectures and more student-centered learning including problemand project-based methods. Chapter five concerns interdisciplinary project weeks. Chapter six discusses improving engineering education in the United Kingdom. Chapter seven is on teaching core knowledge efficiently and employability competencies in chemical engineering education. Chapter eight concerns the importance of teaching personal and professional skills. Chapter nine argues for more liberal studies in engineering education. Chapter ten suggests methods of teaching to enhance innovation. Chapter eleven discusses the value in enterprising experiences outside the classroom. Chapter twelve shows how group creativity can be enhanced and managed through off-task breaks. Kellam, Coley, and Boklage (2019) have edited a book on transformative teaching in engineering as part of the annual Synthesis Lectures on Engineering started in 2006. Excellent engineering educators tell their stories of how they developed their exemplary pedagogies. These educators come from a variety of institutions and teach a variety of courses in multiple disciplines. There are chapters discussing vulnerability and empowerment in teaching, going from the armed forces to the classroom, engaging students through service learning and innovation, engaging students in hands-on learning, and creating a community of collaborators to achieve curriculum change. Kelly and Green (2019) have edited a book on sociocultural research in science and engineering education with international authorship. The book (p. 1) explores “how science and engineering concepts, processes, and practices are socially constructed through coordinated interactions among students, teachers, curricula, texts, and technologies.” The chapters related to engineering education include multimodal analysis of decision making, learning through improvement from failure in

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elementary engineering design projects, and research methods for the advancement of possibility knowledge and practice in engineering education. Mohsen, Ismail, Parsaei, and Karwowski (2019) have edited a book with ten chapters with international authorship related to global advances in engineering education. There are general chapters on the history of engineering education dating back to ancient Egypt and Greece, strategies and challenges of engineering education, globalization issues related to engineering education, and augmented reality. There are also some specific chapters on safety engineering and RLaaS-Frame for remote laboratories. Roberta Powell (2019) has edited a book covering a wide range of topics related to curriculum and pedagogy in engineering education with an international authorship. Some of the 26 chapters are fairly specific such as a chapter on inductive teaching of a remote lab. Others are more general such as going from STEM to STEAM to enhance engineering and technology education. It even includes a chapter on medical engineering education. Rahman and Ilic (2019) have edited a book on blended learning in engineering education with international authorship. Blended learning includes prescribed learning both inside and outside the classroom, where lectures are typically recorded and watched outside of the classroom while classroom time is dedicated to hands-on activities and working problems. It also includes other innovative teaching methods such as hybrid learning, online learning, experiential learning, gamification, and learning spaces. A sampling of chapters includes PBL applied to statistical hydrology and applied fluid flow, along with gamification to increase student engagement.

SOURCES FOR FURTHER INFORMATION This section is intended to be representative but not exhaustive. Links are provided for further information.

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ORGANIZATIONS ABET 1 is a worldwide accreditor of engineering programs whose purpose is “to assure confidence in university programs in STEM (science, technology, engineering, and mathematics) disciplines.” At the time of this writing it had accredited 4,144 programs at 812 institutions in 32 countries. ABET consists of four accreditation commissions: Applied and Natural Science, Computing, Engineering, and Engineering Technology. The Engineering Accreditation Commission (EAC) is of specific interest here. At the time of this writing, the following are the criteria used by the EAC for Baccalaureate programs:2 1. 2. 3. 4. 5. 6. 7. 8.

Students Program Educational Objectives Student Outcomes Continuous Improvement Curriculum Faculty Facilities Institutional Support

ABET is a major driving force for engineering curriculum content and requires the following:   

1

Minimum of 30 semester credit hours (or equivalent) of a combination of college-level mathematics and basics sciences Minimum of 45 semester credit hours (or equivalent) of engineering courses Broad general education component (no specific number of credit hours)

https://www.abet.org https://www.abet.org/accreditation/accreditation-criteria/criteria-for-accrediting-engineering-pro grams-2020-2021/ 2

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Culminating major design experience incorporating appropriate engineering standards and multiple constraints and is based on knowledge and skills acquired in previous courses

ABET partners with relevant societies in the accreditation process. For mechanical engineering, ABET partners with the ASME which supplies the program evaluators during accreditation visits. The American Society for Mechanical Engineers or ASME 3 is an international society for mechanical engineers. It was established in 1880 and had over 100,000 members in over 140 countries at the time of this writing. It sets standards, provides training, sponsors conferences, publishes journals and papers, and offers certifications. There are numerous mechanical engineering societies in various countries such as the Institution of Mechanical Engineers or IMECHE 4 in the UK which is believed to be the oldest such society having been formed in 1847 (Zhang and Yang, 2020). The American Society for Engineering Education or ASEE 5 is an international society dedicated to engineering education. Its mission is to advance “innovation, excellence, and access at all levels of education for the engineering profession.” It provides training, sponsors conferences, publishes journals and papers, and recommends public policy. The Mechanical Engineering (ME) Division6 is one of the largest within ASEE. The goal of this division is “the advancement of education in all of its functions which pertain to mechanical engineering, including the processes of teaching and learning, research, professional interactions, extension services and public relations.” The International Federation of Engineering Education Societies or IFEES7 was founded in 2006. Note there are many individual engineering societies for a variety of countries and regions which are not specifically discussed here. The purpose of IFEES is to encourage collaboration among 3

https://www.asme.org/ https://www.imeche.org/ 5 https://www.asee.org/ 6 http://mechanical.asee.org/ 7 http://www.ifees.net/ 4

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the many engineering education societies. IFEES annually sponsors the World Engineering Education Forum held at different locations around the world. The Institute of Electrical and Electronics Engineers or IEEE has an education branch called IEEE Education Society 8 which is “an international organization that promotes, advances, and disseminates stateof-the-art scientific information and resources related to the Society’s field of interest and provides professional development opportunities for academic and industry professionals.” It publishes the journal IEEE Transactions on Education. U.S. National Academy of Engineering or NAE9 was founded in 1964 and provides engineering leadership to the US. Its mission is “to advance the well-being of the nation by promoting a vibrant engineering profession and by marshalling the expertise and insights of eminent engineers to provide independent advice to the federal government on matters involving engineering and technology.” It has more than 2,000 peer-elected members. They have published many reports relevant to engineering education. The United Nations Educational, Scientific and Cultural Organization or UNESCO 10 was formed in 1945 and looks to “build peace through international cooperation in education, the sciences and culture.” UNESCO has published some reports on engineering education.11 In partnership with UNESCO is the World Federation of Engineering Organizations or WEFO 12 whose primary function is to represent engineering worldwide and to encourage humanitarian efforts. It also has an educational component. The Royal Academy of Engineering or RAE 13 is the UK national academy for engineering and technology. Engineers are elected to become fellows and serve to advance and promote engineering excellence to 8

http://ieee-edusociety.org/ https://www.nae.edu/ 10 https://en.unesco.org/ 11 http://www.unesco.org/new/en/natural-sciences/science-technology/engineering/engineering-e ducation/ 12 https://www.wfeo.org/ 13 https://www.raeng.org.uk/ 9

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benefit society. The RAE has issued a wide range of reports related to engineering including those related to education at all levels of schooling. They conduct education research and provide policy guidance to the UK government. Center for the Integration of Research, Teaching, and Learning or CIRTL14 “seeks to enhance excellence in STEM undergraduate education through development of a national faculty committed to implementing and advancing evidence-based teaching practices for diverse learners. It was founded in 2003 and consists of a network of nearly 40 research universities in the U.S. and Canada. Some of its key values include learnercentered education, equity and inclusion, diversity and representation, collaboration, intellectual generosity, inclusive excellence and innovation, and reflective decision-making. CIRTL offers a range of courses for training engineering faculty.

JOURNALS This section is not intended to be exhaustive but to present some of the more common journals dedicated to engineering education. There are some others dedicated to specific countries (example, Argentina, India, Japan, Korea, and Taiwan) or regions (e.g., Latin America and the Caribbean). The journals are considered in alphabetical order. Advances in Engineering Education (AEE)15 is a peer-reviewed, online journal published two or three times a year by the American Society for Engineering Education. Its mission is “to disseminate significant, proven innovations in engineering education practice, including those that are enhanced through the creative use of multimedia.” Australasian Journal of Engineering Education (AJEE)16 is published by Taylor and Francis under the auspices of the Australasian Association

14

https://www.cirtl.net/ https://advances.asee.org/ 16 https://www.tandfonline.com/loi/teen20 15

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for Engineering Education (AAEE). It supports research designed to improve engineering education by informing practice, policy, and research. Chemical Engineering Education (CEE)17 is a quarterly, international, peer-reviewed journal dedicated specifically to chemical engineering education which originated in 1962. The readership is predominantly chemical engineering faculty. It is published by the Chemical Engineering Division of the American Society for Engineering Education. European Journal of Engineering Education (EJEE)18 is published by Taylor & Francis and sponsored by the European Society for Engineering Education (SEFI). 19 It is “a forum for scholarly dialogue to further engineering education” that “embraces multiple perspectives to examine relevant conditions, developments, approaches, methods and experiences relevant for the education of engineers.” The focus is on higher engineering education. IEEE Transactions on Education20 is a publication of the Institute for Electrical and Electronic Engineers (IEEE) which publishes “significant and original scholarly contributions to education in electrical and electronics engineering, computer engineering, computer science, and other fields within the scope of interest of IEEE.” The International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL) 21 is published by Inderscience on a quarterly basis. It is a, open-access premier journal for continuing engineering education, lifelong learning, and professional development for engineers and technologists. The International Journal of Electrical Engineering & Education (IJEEE)22 is published by Sage. It originated in 1948 as the International Journal of Electrical Engineering Education. It provides a “showcase for international developments in the undergraduate teaching of electrical engineering and electronics, from power systems to nanotechnology. 17

http://ww2.che.ufl.edu/cee/ https://www.tandfonline.com/loi/ceee20 19 https://www.sefi.be/ 20 https://site.ieee.org/review-criteria-toe/ 21 https://www.inderscience.com/jhome.php?jcode=ijceell 22 https://journals.sagepub.com/home/ije 18

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International Journal of Engineering Education (IJEE) 23 is an independent, peer-reviewed journal “serving as an international archival forum of scholarly research related to engineering education.” It is published six times a year on a wide range of topics related to engineering education. International Journal of Mechanical Engineering Education (IJMEE)24 is an open-source, peer-reviewed journal published by Sage. It is aimed at “teachers and trainers of mechanical engineering students in higher education and focuses on the discussion of the principles and practices of training professional, technical and mechanical engineers and those in related fields.” It includes topics such new experimental methods and laboratory techniques, book reviews, and highlights of recent articles in the field. Journal of Engineering Education (JEE)25 is a premier, quarterly, peerreviewed, international journal published by the American Society for Engineering Education. Its mission is to “cultivate, disseminate, and archive scholarly research in engineering education. It includes quantitative, qualitative, and mixed methods studies. Studies in Engineering Education (SEE)26 is a new journal that was launched in June 2019. Its mission is “to expand the conversations among engineering education researchers.” It is an international, open-access, peer-reviewed journal.

WORKSHOPS National Effective Teaching Institute or NETI (https://www.asee. org/education-careers/continuing-education/courses-and-workshops/neti) are semi-annual three-day workshops established in 1991 to familiarize engineering faculty members with student-centered teaching strategies.

23

https://www.ijee.ie/ https://journals.sagepub.com/home/ijj 25 https://www.asee.org/papers-and-publications/publications/jee 26 https://www.seejournal.org/ 24

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Based on a survey of participants, NETI has achieved its goal of improving the instructional methods of engineering faculty (Felder & Brent, 2010). Frontiers in Engineering Education or FIE27 runs an annual conference with published proceedings and has a vision “to advance and re-define engineering and computing education to ensure that all students receive the best possible preparation for their future.” FIE is sponsored by the ASEE Educational Research and Methods Division and the IEEE Computer and Education Societies. ExCEEd 28 is a six day workshop offered multiple times each year sponsored by the American Society of Civil Engineering (ASCE) designed to improve engineering education faculty skills and abilities. There are also two 1.5 day refresher workshops: one is a refresher of the full six day workshop and the other is a condensed version of the full workshop. Those interested in these workshops must apply and be approved to attend.

REFERENCES ABET, Criteria for Accrediting Engineering Programs, 2020-2021, https://www.abet.org/accreditation/accreditation-criteria/criteria-foraccrediting-engineering-programs-2020-2021/, accessed January 10, 2020. Annetta, Leonard and James Minogue (eds.), Connecting Science and Engineering Education Practices in Meaningful Ways, Heidelberg: Springer, 2016. ASME (American Society for Mechanical Engineers), Vision 2030: Creating the Future of Mechanical Engineering Education, Phase 1 – Final Report, Center for Education, New York: ASME, 2011. Burstall, Aubrey F., A History of Mechanical Engineering, London: Faber and Faber, 1963.

27 28

http://fie-conference.org/ https://www.asce.org/exceed/

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Calvert, Monte, The Mechanical Engineer in America, 1830-1910: Professional Cultures in Conflict, Baltimore: Johns Hopkins Press, 1967. Crawley, Edward, Johan Malmqvist, Sören Östlund, Doris Brodeur, and Kristina Edstrom, Rethinking Engineering Education: The CDIO Approach, 2nd edition, Heidelberg, 2014. Davim, J. Paulo (ed.), Engineering Education: Curriculum, Pedagogy and Didactic Aspects, Oxford: Chandos Publishing, 2014. Davison, Ryan, Engineering Curricula: Understanding the Design Space and Exploiting the Opportunities: Summary of a Workshop, Washington, DC: National Academies Press, 2010. Duderstadt, J.J. (2008). Engineering for a changing world: A roadmap to the future of engineering practice, research, and education. Ann Arbor, Michigan: The Millennium Project, The University of Michigan. Retrieved from: http://milproj.dc.umich.edu/. Felder, R.M., R. Brent, and M. Prince, Engineering Instructional Development: Programs, Best Practices, and Recommendations, Journal of Engineering Education, Vol. 100, No. 1, pp. 89-122, 2011. Flumerfelt, Shannon, Franz-Josef Kahlen, Anabela Alves, and Anna Bell Siriban-Manalang, Lean Engineering Education: Driving Content and Competency Mastery, New York: ASME Press, 2015. Fox, Marye and Norman Hackerman (eds.), Committee on Recognizing, Evaluating, Rewarding, and Developing Excellence in Teaching of Undergraduate Science, Mathematics, Engineering, and Technology, Washington, DC: National Academies Press, 2003. Frerich, S., T. Meisen, A. Richert, M. Petermann, S. Jeschke, U. Wilkesmann, and A. Takkaya (eds.), Engineering Education 4.0: Excellent Teaching and Learning in Engineering Sciences, Cham, Switzerland: Springer, 2016. Galloway, Patricia, The 21st-Century Engineer: A Proposal for Engineering Education Reform, Reston, VA: ASCE Press, 2008. Gianniny, Omer, Mechanical Engineering Education in America: Its First Century, New York: ASME Press, 1979.

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Goldberg, David and Mark Sommerville, A Whole New Engineer: The Coming Revolution in Engineering Education, Douglas, MI: Threejoy, 2014. Graham, Ruth, Achieving Excellence in Engineering Education: The Ingredients of Successful Change, London: Royal Academy of Engineering, 2012. Grayson, Lawrence, The Making of an Engineer: An Illustrated History of Engineering Education in the United States and Canada, New York: John Wiley & Sons, 1993. Guerra, Aida, Ronald Ulseth, and Anette Kolmos (eds.), PBL in Engineering Education: International Perspectives on Curriculum Change, Rotterdam: Sense Publishers, 2017. Heywood, John, The Assessment of Learning in Engineering Education: Practice and Policy, New York: IEEE Press, 2016. Heywood, John, Empowering Professional Teaching in Engineering: Sustaining the Scholarship of Teaching, Morgan & Claypool Publishers, 2018. Johri, Aditya and Barbara Olds, Cambridge Handbook of Engineering Education Research, New York: Cambridge University Press, 2014. Jonassen, D.H., “Engineers as Problem Solvers,” Chapter 6 in Cambridge Handbook of Engineering Education Research, edited by A. Johri and B.M. Olds, Cambridge University Press, New York, 2014. Kapranos, Plato (ed.)., The Interdisciplinary Future of Engineering Education: Breaking Through Boundaries in Teaching and Learning, London: Routledge, 2019. Kellam, Nadia, Brooke Coley, and Audrey Boklage, Transformative Teaching: A Collection of Stories of Engineering Faculty’s Pedagogical Journeys, Morgan & Claypool Publishers, 2019. Kelly, Gregory and Judith Green (eds.), Theory and Methods for Sociocultural Research in Science and Engineering Education, New York: Routledge, 2019. Kober, Linda, Reaching Students: What Research Says About Effective Instruction in Undergraduate Science and Engineering, Washington, DC: National Academies Press, 2015.

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Mohsen, J.P., Mohamed Ismail, Hamid Parsaei, and Waldemar Karwowski (eds.), Global Advances in Engineering Education, Boca Raton, FL: CRC Press, 2019. Mutis, Ivan, Renate Fruchter, and Carol Menassa (eds.), Transforming Engineering Education: Innovative, Computer-Mediated Learning Technologies, Reston, VA: ASCE Press, 2018. National Academy of Engineering, The Engineer of 2020: Visions of Engineering in the New Century, Washington, DC: National Academies Press, 2004. National Academy of Engineering, Educating the Engineer of 2020: Adapting Engineering Education to the New Century, Washington, DC: National Academies Press, 2005. National Science Board, Moving Forward to Improve Engineering Education, Washington, DC: National Science Foundation report NSB-07-122, November 19, 2007. Powell, Roberta (ed.), Engineering Education: Curriculum and Pedagogy, Forest Hills, NY: Willford Press, 2019. Rahman, Ataur and Vojislav Ilic (eds.), Blended Learning in Engineering Education: Recent Developments in Curriculum, Assessment and Practice, Boca Raton, FL: CRC Press, 2019. Royal Academy of Engineering, Educating Engineers for the 21st Century, London: Royal Academy of Engineering, June 2007. Shepherd, Sheri, Kelly Macatangay, Anne Colby, and William Sullivan, Educating Engineers: Designing for the Future of the Field, San Francisco: Jossey-Bass, 2009. Sidhu, Manjit, Technology-Assisted Problem Solving for Engineering Education: Interactive Multimedia Applications, Hershey: Engineering Science Reference, 2010. Singer, Susan, Natalie Nielsen, and Heidi Schweingruber (eds.), Discipline-Based Education Research: Understanding and Improving Learning in Undergraduate Science and Engineering, Washington, DC: National Academies Press, 2012. Spurlin, Joni, Sarah Rajala, and Jerome Lavelle (eds.), Designing Better Engineering Education Through Assessment: A Practical Resource for

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Faculty and Department Chairs on Using Assessment and ABET Criteria to Improve Student Learning, Sterling, VA: Stylus, 2008. Stephan, Elizabeth, David Bowman, William Park, Benjamin Sill, and Matthew Ohland, Thinking Like An Engineer: An Active Learning Approach, New York: Pearson, 2018. Tryggvason, Gretar and Diran Apelian, “Re-Engineering Engineering Education for the Challenges of the 21st Century, Journal of the Minerals, Metals and Materials Society, Vol. 58, No. 10, pp. 14-17, 2006. United Nations Educational, Scientific and Cultural Organization (UNESCO), Engineering: Issues, Challenges and Opportunities for Development, Paris: UNESCO Publishing, 2010. Valenzuela-Valdés, Juan & Pedro Pado Fernández (eds.), Project Based Learning on Engineering: Foundations, Applications and Challenges, New York: Nova Publishers, 2015. Vincenti, Walter, What Engineers Know and How They Know It: Analytical Studies from Aeronautical History, Baltimore: Johns Hopkins University Press, 1990. Zawojewski, Judith, Heidi Diefes-Dux, and Keith Bowman (eds.), Models and Modeling in Engineering Education: Designing Experiences for All Students, Rotterdam: Sense Publishers, 2008. Zhang, Ce and Jianming Yang, A History of Mechanical Engineering, Singapore: Springer, 2020.

In: Mechanical Engineering Education … ISBN: 978-1-53617-791-6 Editor: Charles E. Baukal, Jr. © 2020 Nova Science Publishers, Inc.

Chapter 1

MECHANICAL ENGINEERING STUDENTS’ LEARNING PREFERENCES Charles E. Baukal, Jr. John Zink Hamworthy Combustion, Tulsa, OK, USA Oklahoma State University Tulsa, OK, USA Oral Roberts University Tulsa, OK, USA University of Tulsa Tulsa, OK, USA

Keywords: mechanical engineering students, learning strategy preferences, verbal-visual preferences

INTRODUCTION Research has shown that mechanical engineering (ME) students have much different learning strategy and verbal-visual preferences than the general population (Baukal and Ausburn 2014). They prefer more problem solving and more visual materials. The research-based learning preferences of ME students suggests that ME education should be designed differently than traditional education which is often highly verbal and less visual with

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little problem solving. This chapter will discuss the learning preferences of ME students based on ongoing research and provide recommendations for instructional designers and instructors on how to develop more effective training materials using multimedia. There continue to be calls for improving engineering education. The U.S. National Academy of Engineering (2004) established a Committee on Engineering Education to answer the question “What will or should engineering be like in 2020?” The Phase 2 report (National Academy of Engineering 2005) from that committee titled Educating the Engineer of 2020 calls for the reinvention of engineering education. An important finding of that study was the importance of addressing how students learn in addition to what they learn and recommended more research into engineering education. This included how to better serve students with different learning styles and how to determine pedagogical approaches that excite them. The Journal of Engineering Education (Anonymous 2006) recommended further research on how engineering learners develop knowledge. Duderstad (2008) recommended (p. v) “a systematic, researchbased approach to innovation and continuous improvement of engineering education.” The U.S. National Academy of Engineering (2008) identified 14 grand challenges in engineering. One of those challenges is to advance personalized learning that recognizes individual preferences and aptitudes to help motivate learners to become more self-directed. While that challenge was targeted at the development of learning software by computer engineers, it applies to all types of learning and learners, including engineering students. The purpose of the ongoing research study reported here is to address the current lack of information about learning strategy and verbal-visual preferences of mechanical engineering students by determining those preferences for a sample of those students. The following research questions were considered: (1) What are the learning strategy and verbalvisual preference profiles for mechanical engineering students?, (2) How do the learning strategy and verbal-visual preferences of mechanical engineering students compare to the established norms for the general population?, and (3) What are the relationships of mechanical engineering

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students’ learning strategy and verbal-visual preferences to the demographic variables of gender, age range, class in school, ethnicity, native country, and native language?

LEARNER PREFERENCES Learning strategy and verbal-visual preferences are briefly discussed in this section. More detailed discussions of these are available elsewhere (Baukal 2017).

Learning Strategy Preference One way to address individual differences in how students learn and to personalize learning options is through the concept of learning style. Learning style, also referred to as psychological type (McCaulley 1976; McCaulley, Godleski, Yokomoto, Harrisberger, and Sloan 1983) refers to how students preferentially perceive (e.g., sensory vs. intuitive), how information is most effectively perceived (e.g., verbally or visually), how information is preferentially organized (e.g., inductive vs. deductive), how information is processed (e.g., actively vs. reflectively), and how understanding progresses (e.g., sequentially vs. globally) (Felder and Silverman 1988). These styles are relatively stable and concern cognitive, affective and psychological behaviors about how learners perceive, interact with, and respond to a learning environment (Felder and Brent 2005). Numerous previous studies have considered learning styles for engineering students. One example is a study of a small sample of engineering students at the University of Texas (Stice 1987). In that study, Kolb’s (2005) Learning Style Inventory (LSI) consisting of four learning styles (convergent, divergent, assimilation, and accommodation) (Kolb 1984) was used to determine the students’ learning styles. The overwhelming majority was almost equally split between convergers (learning style characterized by problem solving, decision-making, and practical

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application of ideas) and assimilators (learning style characterized by inductive reasoning and the ability to create theoretical models). Another example study was done at the University of Cincinnati under a grant from the U.S. National Science Foundation (Rutz and Westheider 2006). Again, most engineering students were found to be assimilators or convergers. This was comparable to other studies that found the learning styles of engineering students were statistically significantly different than the learning styles of the general population. Another example study using Kolb’s LSI to determine the learning styles of engineering students at Atilim University in Turkey found that assimilators were predominant (Cagiltay 2008). In another study that also used Kolb’s LSI, engineering students at Morgan State University were predominantly assimilators (Hargrove, Wheatland, Ding, and Brown 2008). Larkin-Hein and Budny (2001) gave specific instructional design recommendations for each type of learning style for engineering students. However, Holvikivi (2007) argued that despite its popularity, the use of learning styles testing in engineering education is poorly understood. Another problem with learning styles is that they have been defined and tested in a variety of ways which makes it difficult to compare studies and generalize results (Ausburn and Brown 2006).

Figure 1. Learning strategy preference distribution for the general population (N = 3070).

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A potentially beneficial alternative to the standard definitions and assessments for learning styles is known as learning strategies. Learning strategy preferences, like traditional learning styles, are important characteristics that vary among learners. Conti and Fellenz (1991, p. 1) defined learning strategies as “techniques or skills that an individual elects to use in order to accomplish a learning task.” Learning styles are believed to be stable and deeply ingrained processes for processing information (Ausburn and Ausburn 1978; Kramer 2002). In contrast, learning strategies are believed to be less rigid and are more related to personal preferences and choices made by learners during learning tasks (Conti and Kolody 1995; Fellenz and Conti 1993; Smith 1982). Learning strategy preference is a potentially important learner variable (Ausburn 2004) that could be used by instructors to enhance students’ learning experiences (Ausburn and Brown 2006). Learning strategy preferences were not found to have been previously measured for engineering students before this ongoing study was initiated. Through a complex and lengthy process, Conti and his associates (Conti and Fellenz 1991; Fellenz and Conti 1993; Conti and Kolody 1995; Conti 2009) developed and validated the instrument known as Assessing The Learning Strategies of AdultS or ATLAS. An important advantage of this instrument is that it is simple to administer and is currently the generally-accepted method for measuring learning strategy preferences (Ausburn and Brown 2006). Three distinct learning strategy groups were identified: Navigators, Problem Solvers, and Engagers (Conti 2009). Navigators plan their learning and focus on completing the necessary activities to achieve their goals. Order and structure are important to these learners, who tend to be logical, objective, and perfectionists. They want clear objectives and expectations at the beginning of a course and in advance of activities, such as in an explicit and detailed syllabus. Problem Solvers are critical thinkers who like to explore multiple alternatives. For them, the process is important, so they need flexibility in completing learning activities. They may have difficulty making decisions because they must choose among multiple alternatives and because the exploration process which they enjoy must come to an end. This may cause them to

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appear to procrastinate in making decisions because they do not want the process to end. Engagers are more affective learners who enjoy learning they perceive to be fun or personally beneficial. They are interested in building relationships with both teachers and fellow students during learning, which means they typically enjoy group activities. The emotional aspect of learning is important to Engagers. The distribution of the three ATLAS strategy preferences in the general population is relatively evenly distributed (Ausburn and Brown 2006) as shown in Figure 1. Different professions may have different learning strategy preference profiles. For example, Birzer and Nolan (2002) found that law enforcement had a distinctive profile compared to the general population in a comparison of known population norms to the preferred learning strategies of urban police in a Midwestern city. They found there were some differences between those working in community policing environments and those who did not. Police involved in community policing tended to be Problem Solvers. Ausburn and Brown (2006) studied career and technical education students and found that most were Engagers. Before the study discussed here, no studies were found to determine the ATLAS-defined learning strategy preferences of engineers, the occupational group of interest here.

Verbal-Visual Preference A major dimension of cognitive style is the verbalizer-visualizer dimension (Paivio 1971; Riding 2001). Unfortunately, there is no consensus on terminology for this dimension as it has been called a cognitive style, a learning style, and a learning preference (Plass, Chun, Mayer, and Leutner 1998). “Visualizers tend to think more concretely, use imagery, and personalize information. While learning they prefer graphs, diagrams, or pictures added to text-based material. Verbalizers prefer to process information from words, either by reading or listening, rather than through images” (Jonassen and Grabowski 1993, p. 191). Learners who have no strong preference for either verbal or visual processing are referred

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to as flexible stylists, also called bimodal or mixed processors (Ong and Milcech 2004). More visual learners may approach learning tasks with visual learning strategies, while more verbal learners may use more verbal strategies (Kirby, Moore, and Schofield 1988). When given a choice, verbalizers tend to select more verbal content and visualizers tend to select more visual content (Riding and Watts 1997). Many instruments have been developed to measure this cognitive style. Richardson (1977) developed a 15-item questionnaire called VVQ (verbal and visual questions). His research showed 15 to 25% of people tested fell into what he called either habitual verbalizers or habitual visualizers, with the balance in between. He recommended using 15% verbalizers and 15% visualizers with the balance in between for research purposes. Felder and Silverman (1988) wrote a highly cited paper on learning and teaching styles in engineering education. One of the five dimensions they discussed included visual-auditory. An instrument was developed that is a selfscoring 44-item questionnaire called the Index of Learning Styles or ILS (Solomon and Felder 2013). Montgomery (1995) used the ILS instrument to sample the learning styles of 143 students in an introductory sophomore-level chemical engineering class. She found that 69% were visual and 30% were verbal (1% were reported as None). Multimedia software was developed for the course, in part because multimedia software favors visual learners which were the overwhelming majority of the students. Rosati (1999) used the ILS to sample a large group (N = 858) of engineering students at the University of Western Ontario and found that 80% were visual (89% of males were visual, 69% of females). The verbal-visual preference of the balance of the 20% of the participants was not reported. Kirkham, Farkas, and Lidstrom (2006) found that 85% of the University of Washington engineering students taking a particular class were visual as determined using the ILS. The verbal-visual preference of the balance of the 15% of the participants was not reported. Figure 2 shows a comparison of the results for these studies.

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Figure 2. Verbal-visual preference distribution (frequency) by native country (N = 270).

The verbalizer-visualizer preference, as measured by the Verbal-Visual Learning Style Rating (VVLSR) established by Mayer and Massa (2003), represents the perceptual cognitive aspect of adult learning styles. This instrument was validated against a number of other instruments and was used here because of its simplicity (a single question). It was used in this study to examine possible relationships between perceptual/cognitive learning preferences and demographics.

METHODOLOGY This study used a quantitative descriptive design based on survey methodology, which uses instruments such as questionnaires to collect information from one or more groups of subjects (Ary, Jacobs, Razavieh, and Sorensen 2006). Starting in the semesters beginning with the fall of 2012 and continuing to the fall of 2019, a total of 290 mechanical engineering students from two private and one public Midwestern universities were sampled to determine their learning strategy preferences and verbal-visual cognitive styles. Three instruments were used in that study: a demographics questionnaire, ATLAS, and the VVLSR. The demographics questionnaire was used to collect information such as

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gender, age range, year in college, major, ethnicity, native language, and native country. The results of only the learning strategy preferences for all engineering majors for data collected in 2012-2013 are reported elsewhere (Baukal, Ausburn, Matsson and Price 2013). The surveys were completely voluntary and anonymous.

RESULTS AND DISCUSSION The learning strategy and verbal-visual preferences of the participants sampled are given next.

Demographics The gender frequency distribution is given by frequency in Figure 3 and by percentage in Figure 4, both by school and overall. Females represented 16% and males 84% of the total sample.

Figure 3. Participant distribution (frequency) by school and overall total based on the students’ gender (N = 290).

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Figure 4. Participant distribution (percentage) by school and overall total based on the students’ gender (N = 290).

The age range frequency distribution is given by frequency in Figure 5 and by percentage in Figure 6, both by school and overall. The highest proportions of subjects were juniors and seniors. The overall distribution was: 8.6% freshmen, 15.9% sophomores, 43.1% juniors, and 32.4% seniors. The overall participants’ age range and gender distribution is given by frequency in Figure 7 and by percentage in Figure 8, both by school and overall. As shown in Figure 8, the percentage of participants by gender and age range were comparable.

Figure 5. Age range distribution (frequency) by school and overall total based on the students’ class (N = 288).

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Figure 6. Age range distribution (percentage) by school and overall total based on the students’ class (N = 288).

Figure 7. Overall participant distribution (frequency) by age and gender (N = 288).

The class in school distribution is given by frequency in Figure 9 and by percentage in Figure 10, both by school and overall. The distribution varied significantly by school as shown in Figure 10 where there were more juniors, sophomores, and seniors for schools 1, 2, and 3, respectively.

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Figure 8. Overall participant distribution (percentage) by age and gender (N = 288).

Figure 9. Participant distribution (frequency) by school and overall total based on the students’ class (N = 290).

Figure 10. Participant distribution (percentage) by school and overall total based on the students’ class (N = 290).

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Figure 11. Participant ethnicity distribution (frequency) by school and overall total based on the students’ class (N = 290).

Figure 12. Participant ethnicity distribution (percentage) by gender (N = 290).

The ethnicity distribution of the participants is given by frequency in Figure 11 and by percentage in Figure 12. While there was some variation based on school, most participants were Caucasian/White with Other/Multiple and Hispanic/Latino the next most reported. In the Other/Multiple category, of those that specified, students reported (9) African American, (9) Native American and (17) Middle Eastern. Seven students did not report their Other/Multiple ethnicity.

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Figure 13 shows the ethnicity frequency distribution by gender and Figure 14 shows the ethnicity percentage distribution by gender. There were higher percentages of African American, Asian, and Multiple females compared to males. There was a higher percentage of Hispanic/Latino males compared to females.

Figure 13. Participant ethnicity distribution (frequency) by gender (N = 290).

Figure 14. Participant ethnicity distribution (percentage) by school and overall total based on the students’ class (N = 290).

Figure 15 shows the frequency of participants whose were born in the U.S.A. compared to those who were not (“Other”) by school and overall.

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Figure 16 shows the percentage of those born in the U.S.A. and outside the U.S.A. by school and overall. The vast majority of the students were born in the U.S.A. School 2 had almost as many born in Other as in the U.S.A. In the Other category, students reported native countries of (1) Angola, (4) China, (2) Columbia, (3) Ethiopia, (1) Germany, (1) India, (1) Indonesia, (1) Kenya, (4) Mexico, (2) Namibia, (2) Nigeria, (5) Oman, (2) Pakistan, (3) Russia, (13) Saudi Arabia, (2) Tanzania, (4) Vietnam, and (2) Zimbabwe. Two did not specify their Other country.

Figure 15. Participant native country distribution (frequency) by school and overall total based on the students’ class (N = 290).

Figure 16. Participant native country distribution (percentage) by school and overall total based on the students’ class (N = 290).

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Figure 17 shows the frequency and Figure 18 shows the percentage of participants whose native language was either English or Other by school and overall. For the vast majority of the students, English was their primary language. From School 2, there were almost as many non-native English speakers compared to native English speakers. In the Other language category, of those that specified, students reported (3) Amharic, (18) Arabic, (4) Chinese, (1) Dutch, (1) German, (1) Indonesian, (1) Portuguese, (1) Russian, (1) Shona, (9) Spanish, (2) Swahili, (2) Urdu, and (4) Vietnamese. Two did not specify their Other language.

Figure 17. Participant native language distribution (frequency) by school and overall total based on the students’ class (N = 290).

Figure 18. Participant native language distribution (percentage) by school and overall total based on the students’ class (N = 290).

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Learning Strategy Preference Conti (2009) compiled a large database of 3070 subjects from 36 dissertations using the ATLAS instrument and found the distribution to be: 36.5% Navigators, 31.7% Problem Solvers, and 31.8% Engagers. Birzer and Nolan (2002) specifically sampled police officers from a particular police force and found the following distribution: 23.8% Navigators, 50.0% Problem Solvers, and 26.2% Engagers. The distribution for the mechanical engineering students sampled here was 30.7% Navigators, 42.6% Problem Solvers, and 26.7% Engagers. The mechanical engineering distribution generally fell between the large sample, referred to here as the General Population, and the police officers, referred to here as Birzer and Nolan as shown in Figure 19. A chi-square analysis of the learning strategies for mechanical engineering students assuming the expected frequencies equal to that for the general population did present a statistically significant difference (χ2 = 14.797, df = 2, p = 0.001). All of the statistical analyses considered here are at the 95% confidence level. Figure 20 shows the frequency and Figure 21 shows the percentage distribution of learning strategy preferences for mechanical engineering students by institution and overall.

Figure 19. Learning strategy preference distribution (percentage) for the general population, Birzer & Nolan, and this study.

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Figure 20. Learning strategy preference distribution (frequency) for this study by institution and overall (N = 270).

Figure 21. Learning strategy preference distribution (percentage) for this study by institution and overall (N = 270).

Figure 22 shows the frequency and Figure 23 shows the percentage distribution of learning strategy preferences by gender for this study. There was a statistically significant difference in the distribution of learning strategy preferences for females (χ2 = 54.019, df = 2, p = 0.000) and males (χ2 = 15.719, df = 2, p = 0.000) compared to the general population. There were no statistical difference between males and females.

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Figure 22. Overall learning strategy preference distribution (frequency) for this study by gender (N = 270).

Figure 23. Learning strategy preference distribution (percentage) for this study by gender (N = 270).

Figure 24 shows the frequencies and Figure 25 shows the percentages for the learning strategy preference as a function of age group. There was no statistically significant difference for the 17-18 year olds (χ2 = 0.675, df = 2, p = 0.714), the 19-20 year olds (χ2 = 0.875, df = 2, p = 0.646), or those 23 years old or more (χ2 = 4.484, df = 2, p = 0.106) but there was for the 21-22 year olds (χ2 = 16.165, df = 2, p = 0.000).

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Figure 24. Overall learning strategy preference distribution (frequency) for this study by age group (N = 268).

Figure 25. Learning strategy preference distribution (percentage) for this study by age group (N = 268).

Figure 26 shows the frequency and Figure 27 shows the percentage distributions of learning strategy preferences by ethnicity. There was a statistically significant difference (χ2 = 18.281, df = 2, p = 0.000) for the Caucasian students. There were not enough in the other categories for a statistical analysis. Figure 28 shows the frequency and Figure 29 shows the percentage of learning strategy distributions by native country. There was a statistically

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significant difference (χ2 = 14.281, df = 2, p = 0.001) for those born in the U.S.A. but not for those born outside the U.S.A. (χ2 = 4.425, df = 2, p = 0.109) compared to the general population. Figure 30 shows the frequency and Figure 31 shows the percentage for the native language of the participants. There was a statistically significant difference (χ2 = 13.717, df = 2, p = 0.001) for those whose native language was English but not for those whose native language was not English (χ2 = 3.619, df = 2, p = 0.164) compared to the general population.

Figure 26. Overall learning strategy preference distribution (frequency) for this study by ethnicity (N = 270).

Figure 27. Overall learning strategy preference distribution (percentage) for this study by ethnicity (N = 270).

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Figure 28. Overall learning strategy preference distribution (frequency) for this study by native country (N = 270).

Figure 29. Overall learning strategy preference distribution (percentage) for this study by native country (N = 270).

Felder and Silverman (1988) recommended that teachers use instructional techniques to address a range of learning styles for engineering students to enhance learning. Rutz and Westheider (2006) recommended that teachers use a variety of instructional methods to engage all learners. Because the learning strategy preference profile measured in this study was approximately evenly distributed among the three preference categories, it is recommended that instructors use a variety of instructional techniques to meet the entire range of student preferences.

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Figure 30. Overall learning strategy preference distribution (frequency) for this study by native language (N = 270).

Figure 31. Overall learning strategy preference distribution (percentage) for this study by native language (N = 270).

Verbal-Visual Preference Figure 32 shows a comparison of the verbal-visual preferences for Richardson (1977), Montgomery (1995), and this study. There was a statistically significant difference for this study compared to Richardson (1977) (χ2 = 326.577, df = 2, p = 0.000) and Montgomery (1995) (χ2 = 4515.257, df = 2, p = 0.000).

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Figure 32. Verbal-visual preference distribution (percentage) for Richardson (1977), Montgomery (1995), Rosati (1999), and this study.

Figure 33 shows the frequency and Figure 34 shows the percentage of the distribution of verbal-visual preferences by institution. There was a statistically significant difference (χ2 = 238.102, df = 2, p = 0.000) between Richardson’s (1977) study and School 1 in this study. There were not enough participants in Schools 2 and 3 for a statistical comparison.

Figure 33. Verbal-visual preference distribution (frequency) for this study by institution (N = 278).

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Figure 34. Verbal-visual preference distribution (percentage) for this study by institution (N = 278).

Figure 35 shows the frequency and Figure 36 shows the percentage for the verbal-visual preferences by gender for this study. There were strong statistical differences in the distribution of verbal-visual preferences of females (χ2 = 54.019, df = 2, p = 0.000) and males (χ2 = 272.701, df = 2, p = 0.000) compared to Richardson’s study. There was no statistically significant difference between the female and male participants in the study (χ2 = 1.515, df = 2, p = 0.469).

Figure 35. Verbal-visual preference distribution (frequency) for this study by gender (N = 278).

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Figure 36. Verbal-visual preference distribution (percentage) for this study by gender (N = 278).

Figure 37 shows the frequency and Figure 38 shows the percentage distribution of verbal-visual preferences by age group. There were strong statistical differences in the distribution of verbal-visual preferences of 1920 year olds (χ2 = 154.110, df = 2, p = 0.000), 21-22 year olds (χ2 = 134.922, df = 2, p = 0.000), and 23 years old and older (χ2 = 39.606, df = 2, p = 0.000) compared to Richardson (1977). There was not quite a statistical difference between the 19-20 and 23+ year olds (χ2 = 5.876, df = 2, p = 0.053), nor between the 19-20 and 21-22 year olds (χ2 = 2.529, df = 2, p = 0.282), nor between the 21-22 and 23+ year olds (χ2 = 4.750, df = 2, p = 0.093).

Figure 37. Verbal-visual preference distribution (frequency) for this study by age group (N = 276).

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Figure 38. Verbal-visual preference distribution (percentage) for this study by age group (N = 276).

Figure 39 shows the frequency and Figure 40 shows the percentage for the verbal-visual preference distributions by ethnicity. The only group with enough participants for a valid statistical analysis was the Caucasian/White group which was strongly statistically different than Richardson’s (χ2 = 240.193, df = 2, p = 0.000) profile. Figure 41 shows the frequency and Figure 42 shows the percentage of verbal-visual distributions by native country. There was a strongly statistically different difference for those MEs born in the U.S. (χ2 = 320.322, df = 2, p = 0.000) and those born outside the U.S. (χ2 = 20.718, df = 2, p = 0.000) compared to Richardson’s profile. There was also a statistically significant difference between those born in the U.S. and those born outside the U.S. (χ2 = 10.275, df = 2, p = 0.006).

Figure 39. Verbal-visual preference distribution (frequency) by ethnicity (N = 278).

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Figure 40. Verbal-visual preference distribution (percentage) by ethnicity (N = 278).

Figure 41. Verbal-visual preference distribution (frequency) by native country (N = 278).

Figure 42. Verbal-visual preference distribution (percentage) by native country (N = 278).

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Figure 43 shows the frequency and Figure 44 shows the percentage of the verbal-visual preferences based on the native language (English or not English) for the participants. The profile for those ME students whose native language was English (χ2 = 312.561, df = 2, p = 0.000) and not English (χ2 = 22.962, df = 2, p = 0.000) were strongly statistically different than Richardson’s. There was not quite a statistically significant difference between native English speakers and non-native English speakers (χ2 = 5.925, df = 2, p = 0.052).

Figure 43. Verbal-visual preference distribution (frequency) by native language (N = 278).

Figure 44. Verbal-visual preference distribution (percentage) by native language (N = 278).

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Based on the visual-verbal preference profile measured in this study, ME students were found to be statistically significantly more visual than the general population. This suggests instructional content should be highly visual.

CONCLUSION The overall learning strategy preference profile for mechanical engineering students was statistically significantly different from the established general population norms. Conti (2009) reported that no statistically significant differences were found to be associated with any demographic variables such as gender or race. Similarly, for this study, no relationship was found between learning strategy preferences and gender. There were not enough participants of non-Caucasians for a statistical comparison of ethnicity. The verbal-visual preference for mechanical engineering students was statistically significantly different than the general population. The higher proportion of problem solvers is consistent with the importance of problem solving in engineering practice (Kober, 2015). ME students are much more visually-oriented compared to the general population. The results of this study have implications for the instructional strategies used to teach engineering students or the how to teach and not what to teach. This study suggests that a range of techniques should be used as the ME students were comparably divided among the three learning strategy preferences. Instructional content should be highly visual which is the preferred verbal-visual style for most ME students. Instructors must be careful not to disproportionately design instructional materials and methods for their own learning strategy and verbal-visual preferences and instead should use a variety of techniques to address the preferences of all students. Instructors should consider administering the ATLAS instrument at the beginning of a course, both to find out the learning strategy profile of those enrolled in the class and so the students themselves find out their own

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preference and understand the other preferences. It may be helpful to discuss at the beginning of a course that activities targeted for one learning strategy preference may be less than desirable for those students with other preferences. For example, Navigators prefer more efficient activities (e.g., the instructor directly gives them the answer) while Problem Solvers prefer to explore solutions on their own. Another example is that Navigators often prefer to work by themselves because they have more control over the process, whereas Engagers prefer to work in groups because of the interaction. Since ME students will normally go into the workforce after graduation, they need to be prepared to work with people having all three learning strategy preferences. While they may not themselves prefer certain types of activities, they should at least be able to tolerate them as they may have to experience them in the work environment. Through knowledge of the learning strategies concept and the ATLAS instrument, it may be possible to improve instructional practice in engineering education and to better prepare MEs to engage effectively with their colleagues in the classroom and later in the workplace. Increasing visual learning content should be more effective in helping ME students learn new content.

REFERENCES Anonymous. (2006). Special Report: The Research Agenda for the New Discipline of Engineering Education, Journal of Engineering Education, 95(4), 259-261. Ary, D., Jacobs, L. C., Razavieh, A. & Sorensen, C. (2006). Introduction to Research in Education, Thomson Wadsworth, Belmont, CA. Ausburn, L. J. (2004). Course Design Elements Most Valued by Adult Learners in Blended Online Education Environments: An American Perspective, Educational Media International, 41(4) 327-337. Ausburn, L. J. & Ausburn, F. (1978). Cognitive styles: Some information and implications for instructional design, Educational Communication and Technology, 26(4), 337-354.

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Ausburn, L. J. & Brown, D. (2006). Learning Strategy Patterns and Instructional Preferences of Career and Technical Education Students, Journal of Industrial Teacher Education, 43(4), 6-39. Baukal, C. E. (2017). Learner Preferences for Continuing Engineering Education, Lambert, Mauritius. Baukal, C. E. & Ausburn, L. J. (2014). Learning Strategy and VerbalVisual Preferences for Mechanical Engineering Students, 2014 American Society for Engineering Education Annual Conference & Exposition, paper 8682, Indianapolis, IN, June 15-18, 2014. Baukal, C. E., Ausburn, L. J., Matsson, J. E. & Price, G. L. (2013). Engineering Students’ Learning Strategy Preferences, 2013 ASEE Midwest Section Conference, Kansas State University (Salina, KS), September 18-20, 2013. Birzer, M. L. & Nolan, R. E. (2002). Learning strategies of selected urban police related to community policing, Policing, 25(2), 242-255. Cagiltay, N. E. (2008). Using learning styles theory in engineering education, European Journal of Engineering Education, 33(4), 415424. Conti, G. J. (2009). Development of a user-friendly instrument for identifying the learning strategy preference of adults, Teaching and Teacher Education, 25, 887-896. Conti, G. J. & Fellenz, R. A. (1991). Assessing adult learning strategies, retrieved from ERIC database (ED339847). Conti, G. J. & Kolody, R. C. (1995). The use of learning strategies: An international perspective, Proceedings of the 36th Annual Adult Education Research Conference, Edmonton, Alberta, Canada, 77-82. Duderstadt, J. J. (2008). Engineering for a changing world: A roadmap to the future of engineering practice, research, and education. Ann Arbor, Michigan: The Millennium Project, The University of Michigan. Retrieved from: http://milproj.dc.umich.edu/. Felder, R. M. & Brent, R. (2005). Understanding Student Differences, Journal of Engineering Education, 94(1), 57-72. Felder, R. M. & Silverman, L. K. (1988). Learning and Teaching Styles in Engineering Education, Engineering Education, 78(7), 674-681.

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Fellenz, R. A. & Conti, G. J. (1993). Self-knowledge inventory of lifelong learning strategies (SKILLS) manual, Center for Adult Learning Research, Bozeman, MT. Hargrove, S. K., Wheatland, J. A., Ding, D. & Brown, C. M. (2008). The Effect of Individual Learning Styles on Student GPA in Engineering Education at Morgan State University, Journal of STEM Education, 9(3/4), 37-46. Holvikivi, J. (2007). Learning styles in engineering education: the quest to improve didactic practices, European Journal of Engineering Education, 32(4), 401-408. Jonassen, D. H. & Grabowski, B. L. (1993). Handbook of Individual Differences, Learning, and Instruction, Lawrence Erlbaum, Hillsdale, NJ. Kirby, J. R., Moore, P. J. & Schofield, N. J. (1988). Verbal and visual learning styles, Contemporary Educational Psychology, 13(2), 169184. Kirkham, P., Farkas, D. K. & Lidstrom, M. E. (2006). Learning styles data and designing multimedia for engineers, Proceedings of the International Professional Communication Conference, 2006 IEEE, pp. 57-67. Kober, Linda. Reaching Students: What Research Says About Effective Instruction in Undergraduate Science and Engineering, Washington, DC: National Academies Press, 2015. Kolb, A. & Kolb, D. A. (2005). The Kolb Learning Style Inventory – Version 3.1 2005 Technical Specifications, HayGroup (available at www.hayresourcesdirect.haygroup.com). Kolb, D. A. (1984). Experiential Learning, Prentice-Hall, Englewood Cliffs, NJ. Kramer, C. (2002). Success in On-Line Learning, Delmar, New York. Larkin-Hein, T. & Budny, D. D. (2001). Research on Learning Style: Applications in the Physics and Engineering Classrooms, IEEE Transactions on Education, 44(3), 276-281.

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Mayer, R. E. & Massa, L. J. (2003). Three facets of visual and verbal learners: Cognitive ability, cognitive style, and learning preference, Journal of Educational Psychology, 95(4), 833-846. McCaulley, M. H. (1976). Psychological Types in Engineering: Implications for Teaching, Engineering Education, 66(7), 729-736. McCaulley, M. H., Godleski, E. S., Yokomoto, C. F., Harrisberger, L. & x Sloan, L. (1983). Applications of Psychological Type in Engineering Education, Engineering Education, 73(5), 394-400. Montgomery, S. M. (1995). Addressing diverse learning styles through the use of multimedia, Frontiers in Education Conference, Proceedings, November 1995, Atlanta, GA, pp. 3a2.13 - 3a2.21. National Academy of Engineering. (2004). The Engineer of 2020: Visions of Engineering in the New Century, National Academies Press, Washington, DC, 2004. National Academy of Engineering. (2005). Educating the Engineer of 2020: Adapting Engineering Education to the New Century, National Academies Press, Washington, DC. National Academy of Engineering. (2008). Grand Challenges for Engineering, National Academy of Science, Washington, DC. Ong, Y. W. & Milcech, D. (2004). Comparison of the Cognitive Styles Analysis and the Style of Processing Scale, Perceptual and Motor Skills, 99, 155-162. Paivio, A. (1971). Imagery and Verbal Processes, Holt, Rinehart & Winston, New York. Plass, J. L., Chun, D. M., Mayer, R. E. & Leutner, D. (1998). Supporting visual and verbal learning preferences in a second-language multimedia learning environment. Journal of Educational Psychology, 90(1), 25-36. Richardson, A. (1977). Verbalizer-visualizer: A cognitive style dimension, Journal of Mental Imagery, 1, 109-126. Riding, R. J. (2001). The nature and effects of cognitive style. In R. J. Sternberg & L. Zhang (Eds.), Perspectives on thinking, learning, and cognitive styles, (pp. 47-72), Erlbaum, Mahwah, NJ.

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Riding, R. J. & Watts, M. (1997). The effect of cognitive style on the preferred format of instructional material, Educational Psychology, 17(1-2), 179-183. Rosati, P. (1999), Specific differences and similarities in the learning preferences of engineering students, Presented at the 29th ASEE/IEEE Frontiers in Education Conference, November, San Juan, Puerto Rico, pp. 12, c1-17 – 12c1-22. Rutz, E. & Westheider, V. (2006). Learning Styles of Engineering & Engineering Technology Students – Similarities, Differences and Implications for Effective Pedagogy, paper 2006-419, Proceedings of the American Society for Engineering Education Annual Conference & Exhibition, Chicago, IL, June 18-21, 2006. Smith, R. M. (1982). Learning How to Learn: Applied Theory for Adults, Prentice Hall, Englewood Cliffs, NJ. Solomon, B. A. & Felder, R. M. (2013). Index of Learning Styles Questionnaire, 1991. Online: http://www.engr.ncsu.edu/learningstyles/ ilsweb.html. Accessed August 16, 2013. Stice, J. E. (1987). Using Kolb’s Learning Cycle to Improve Student Learning, Engineering Education, 77(5), 291-296.

In: Mechanical Engineering Education … ISBN: 978-1-53617-791-6 Editor: Charles E. Baukal, Jr. © 2020 Nova Science Publishers, Inc.

Chapter 2

LEVERAGING TECHNOLOGY TO ELEVATE PEDAGOGY IN MECHANICAL ENGINEERING TEACHING AND LEARNING Krishna Pakala1 and Diana Bairaktarova2 1

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Boise State University, Boise, ID, USA Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

INTRODUCTION In the last several years we have observed a shift in paradigm to support students meet their educational goals. The old paradigm involved the following: passive learners, exam-driven, role-learning, content-based, non-negotiable syllabus, objectives addressing the goals of the instructor, behavioral approach to learning and assessment, assessing isolated skills. The old paradigm strongly focused on individual learning. In the last decade, a revolution in the engineering education domain helped to address these archaic approaches. The modern paradigm involves active learners, continuous assessment, connections to real-world examples, supporting innovative and creative instructors, learned-centered outcomes, cognitive

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approach to learning and assessment, assessing skills, knowledge and abilities, collaborative learning. This modern paradigm of learner-centered higher education ecosystem is providing opportunities for learning and advancement of the individual in our society (King & South, 2017). We can leverage technology to positively impact the teaching and learning experience for each student in STEM and increase the flexibility with which students pursue their education (King & South, 2017). Our generation Z students are tech natives, accustomed to everything personalized and education entities need to adapt and meet the needs of this sophisticated generation of students. Elevating pedagogy with technology can vastly improve student learning experience by implementing impactful evidence-based research practices in the learning environment in ways that result in learning experiences becoming more meaningful, engaging, and impactful (Cepeda et al., 2006; Smith & Rothkopf, 1984; Soderstrom & Bjork, 2015).

WHY MOBILE DEVICES? Mobile devices facilitate learning that can happen anytime, anywhere (Foti & Mendez, 2014) blurring the boundaries between formal and informal learning (Falloon, 2015). Mobile devices like smartphones, tablets, and tablet computers have an immense potential for improving student learning (Riley, 2013; Welsh et al., 2015; Zhen, 2017). Learning can happen in collaborative (Brandon van der et al., 2016; Falloon, 2015) and authentic settings (Naylor & Gibbs, 2015), using active learning approaches (Gibeault, 2015). Mobile devices have been found to facilitate enhanced learning when students created digital learning content individually (Bose & Pakala, 2015; Liao & Humphreys, 2014) and in teams (Bose & Pakala, 2014; Ke & Hsu, 2015), as well as learned collaboratively with others (Kukulska-Hulme & Viberg, 2018). Serdyukov (2017) while talking about the role of innovation in the US education system, makes the important observation that the primary focus of educational innovations should be on teaching and learning theory and

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practice. Other identified areas where innovation is needed include human factors like students, parents, community, society, and culture. Technology applications need to have a solid theoretical foundation which is based on deliberate, systemic research, and sound pedagogy (p. 4). Since mobile devices and their use in education is a relatively new occurrence, there has been some debate on whether mobile learning is significantly different from current learning to warrant its unique learning theory (MacCallum & Parsons, 2016). While hypothesizing on what can constitute a theory of mobile learning, some early theorists have stated that what differentiates mobile learning from other forms of learning is the assumption that learners are continually on the move (Sharples, Taylor, & Vavoula, 2005). Learning occurs across space, time, and topic, such that learning, and skills can be transferred across contexts and life transitions. New technologies are increasingly being designed for a society of people on the move, trying to fit learning into the gaps of their usual life functions. Since a lot of learning occurs outside of physical and online classrooms, a challenge is to understand how learners may be engaging with their environments to create spontaneous sites of learning. Typically, a mobile learning opportunity constitutes a variety of learning elements and combinations, such that it is difficult to isolate a specific learning theory associated with it (MacCallum & Parsons, 2016). Certain affordances of mobile learning and devices such as flexible use, continuity of use, timely feedback, personalization, socialization, selfevaluation, active participation, peer coaching, outdoors, and cultural authenticity, suggest that a social-constructivist approach may be an appropriate theory of learning which can support mobile learning (Kukulska-Hulme & Viberg, 2018, p. 207; Sharples, Taylor, & Vavoula (2005). Mobile learning can be best understood through the lens of a social-constructivist approach. A social-constructivist approach views learning as “an active process of building knowledge and skills through practice within a supportive community. It comprises not only a process of continual personal development and enrichment, but also the possibility of rapid conceptual change” (p. 8).

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With the connectivity, convenience, and learning opportunities enabled by mobile devices, comes the notion of the “problematic use” of such devices (Kim, 2018, p. 390) such that some have questioned whether mobile learning supports or endangers learning in classrooms (Pedro, Barbosa, & Santos, 2018). Moreover, though smartphones and internet access are commonly found infrastructures in many academic campuses, there is little empirical research which reports in detail, the ways in which students use these technologies for learning (Barden & Bygroves, 2017). Much depends on how the mobile device is used for teaching and learning in any setting (Montrieux, Vanderlinde, Schellens, & De Marez, 2015). Sometimes, the best educational outcomes are achieved when mobile learning tools like tablets are used to complement traditional learning tools like paper and pencil (Souleles, 2017). The key to successful technology integration lies in the instructor’s use of instructional strategies and pedagogical supports, not in the use of the tool in itself (Bebell & Kay, 2010; Kay & Lauricella, 2011; Montrieux et al., 2015; Okojie, Olinzock, & Okojie-Boulder, 2006; Pane et al., 2017). As indicated by educational scholars, learning still must be situated in authentic contexts (Brown, Collins, & Duguid., 1989; Lave & Wenger, 1990), and the core of the technology enhanced lesson needs to remain focused on the achievement of the desired learning outcomes (Linnenbrink & Pintrich, 2003). While the use of tablets to facilitate learning and communication has been studied in many fields like medicine (Ji-Hyun et al., 2013; LangiusEklöf et al., 2017; Mallet et al., 2016), intergenerational social development (Amaro, Oliveira, & Veloso, 2017), and early childhood education (Jauck, & Peralta, 2016), there is still limited evidence on how it affects student learning in higher education (Wakefield, Frawley, Tyler, & Dyson, 2018). An essential next step yet to be taken is to find optimum ways in which emerging technologies can be transformed into learning tools, by creating high quality teacher-designed content and applications (Scott, 2015). Capitalizing on the current generations’ interest in mobile technologies remains essential, as the instructors’ role transforms from being content conveyors to that of content curators (p. 9). Extensive discipline specific empirical studies need to be done to determine if the use

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of learning technologies like mobile devices can engage learners, as well as extend and enhance their learning (Kolb, 2017). Technology integration frameworks like the Triple E Framework emphasizes how instructors need to move away from arbitrary use of technology to more thoughtful ways of adding value to student learning. Innovation involves inventing or developing a new way of doing something from how it is currently being done (Serdyukov, 2017). Educational innovations are often intended to raise productivity and efficiency of learning, and or improve learning quality (p. 8). While using mobile devices to teach and learn is not anymore, an innovative practice, finding increasingly efficient and novel ways to use mobile and other educational technology to enhance learning is an ever-persistent goal. Further, pedagogy needs to be rethought for 21st century learners, as we continue to identify new competencies that they need to master (Scott, 2015). Traditional methods of instruction like didactic lectures are still prevalent in many engineering courses (Alsop, 2015; Fredriksen, 2015; Kinnari-Korpela, 2015; Kunioshi, Noguchi, Tojo, & Hayashi, 2016; Pomorski & Prokopow, 2018), though research has shown that in comparison, learner-centered, active learning (Auyuanet, et al., 2018; Kinoshita, Knight, & Gibbes, 2017) methods like problem (Johnson & Hayes, 2015) and game-based (Braghirolli, Ribeiro, Weise, & Pizzolato, 2016) learning, have significantly greater benefits. Learners need to transition from being spectators to truly become active learners (Scott, 2015). Therefore, there is still a burning need to improve the current pedagogical practices used in engineering instruction and to prepare faculty to use evidence-based methods for teaching and learning. The current generation of traditional students are often described as the “Generation Z.” The assumption is that people of this generation are digital natives who have spent their entire lives immersed in technologies, especially mobile devices and applications. But are all learners of this generation equally equipped and knowledgeable of how to use digital technology for academic learning? What is the impact of advances in technology like Artificial Intelligence (AI) that make optimum use of learner analytics with the goal of making learning super personalized?

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Institutions of higher education remain hugely responsible for not only teaching students digital literacy but also responsible digital citizenship. As college and university leaders continue to face the challenges posed by lack of finances, reduced enrollment, and retention of students and employees, embracing mobile friendly platforms may be an option that can help retain more students (Ballard, 2018), especially online learners (Zimmerman, 2018). Some large universities like Ohio State University (O’Hara, 2018) and programs like Aviation Technology at Purdue University (Templeton, 2019), have provided students with Apple iPads and Pencils to enable them to connect with peers, instructors, and learning content. However, providing technology devices in the hands of learners is not enough. Training instructors to appropriately integrate that technology into teaching and learning is often a challenge yet to be met. Steps in this direction include efforts made at Boise State University by Krishna Pakala, who has since spring 2013, been using an Apple iPad to hold virtual office hours (Phillips, 2019), known as “Happy Hour,” to make use of the anytime anywhere access capacities of a mobile device. Currently, Pakala uses the video chat platform Zoom, to connect with students who login through Blackboard. Twice-a-week students participate in a Happy Hour where they are able to synchronously watch, participate, and comment (using the chat feature), to engage with peers and the instructor over engineering problem solving activities.

A CASE STUDY OF TEACHING AND LEARNING OF MECHANICAL ENGINEERING COURSES IN A MOBILE ENVIRONMENT Various elements of good instructional design contribute to making a course a pedagogically effective learning platform for students. These elements include but are not limited to -- clearly presented course goals and objectives, content, ample opportunities for learner engagement (Chickering & Gamson, 1987), and the appropriate use of educational technologies within and outside the learning management system. We

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begin our case study with a description of a course design and teaching practices in undergraduate level engineering Fluid Mechanics and Thermodynamics. These courses allowed students to engage with the instructor and peers through in-built opportunities to interact and collaborate. Learning driven assessment strategies were used in these courses with the aim of supporting learning and not merely testing course completion outcomes.

Integration of Mobile Devices to Support Teaching and Learning The instructor not only used several instructionally sound pedagogical practices to deliver the course(s), but also included the use of educational technology like mobile devices for teaching and learning. Students used mobile devices for note-taking, to access course content anytime, anywhere, and to create digital materials which supported and demonstrated their learning of course content. Students created content for both their individual as well as group electronic portfolios. This case study focuses on the instructor’s use of mobile devices for teaching and learning, drawing on prior published data from two Institutional Review Board (IRB) approved studies (Bose & Pakala, 2014; Bose & Pakala, 2015) conducted to measure the impact of mobile device use in teaching and learning of course content. The first study hypothesized that students will find the use of mobile learning strategies and devices to be efficient means of collaboratively creating electronic content for inclusion in engineering team e-portfolios (Bose & Pakala, 2014). The second study sought to determine whether the use of mobile learning strategies and devices supported students to perceive themselves as efficient creators of electronic content for inclusion in their individual e-portfolios (Bose & Pakala, 2015). Currently, these courses continue to take advantage of mobile learning. Most students use their own devices but students who do not own mobile devices can borrow a device from the campus-based IDEA Shop, which is a unit of the Center for Teaching and Learning. The courses are hosted in

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Blackboard; however, content is also made platform-independent such that the learners could review the online course materials and can complete their homework/quizzes/exams using personal mobile devices/iPads. The instructor teaches this course in hybrid, flipped, online and face to face formats, making sure the solid pedagogical design is executed in all modes of course delivery. The courses support easy accessibility through recorded captioned videos, which students can review as many times as needed. Also, the course accessibility features are evaluated using Blackboard Ally. The embedded quizzes in the recorded online videos are geared to help students stay on track and to engage in the active application of learning. This repeated anytime, anywhere accessible content, helps students to learn more efficiently. Students can review the content at different points in their learning process. Students with special learning needs or English as second language learners can re-watch at their own pace, until the content is clear. Students are provided strong peer to peer interaction supported by a learning assistant in the learning assistant sessions. Virtual office hours which are known as “Happy Hours” in these courses, are facilitated seamlessly using Zoom which students can access via Blackboard, that has been shown to increase attendance compared to traditional office hours and can provide students and the instructor with more flexibility in meeting times and locations, content delivery, and enhanced interaction (Pakala, Bairaktarova, & Schauer, 2019). Currently, the courses use active learning methods through intentional design features which enable ample opportunities for 1) faculty-student, 2) student-student and 3) student-content interactions. Multiple learning opportunities that encourage peer learning and tutoring also exist. In summer 2019, the instructor did a pilot study to foster learning and implement evidence-based teaching and learning practices that leverage technology, specifically using Apple Pencil enabled iPads.

Quantitative Evidence and Metrics IRB approved research studies (Bose & Pakala, 2014; Bose & Pakala, 2015) were co-conducted by the instructor (in collaboration with

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instructional designers from the onsite Center for Teaching and Learning) to measure the impact of mobile device use in teaching and learning of course content. The first study students enrolled in an undergraduate 300-level engineering Thermodynamics class, created multimedia videos and produced content demonstrating course content summaries, problem solving techniques, and written work on concept question solutions (Bose & Pakala, 2014). A post course completion anonymous survey and focus group meeting was conducted at the end of the semester, to document student perceptions on the efficacy of using mobile learning strategies and devices to create electronic content for inclusion in the team e-portfolios. In the survey, 64% of the students either Agreed or Strongly Agreed that mobile device use helped them to be more organized for class (Bose & Pakala, 2014). Eighty percent of the students either Agreed or Strongly Agreed that mobile devices helped them to access multiple information resources during learning, while fifty six percent of the students reported that the mobile device helped them to become a better note-taker through use of various note-taking and annotation applications. Sixty percent of the participants either Agreed or Strongly agreed that use of the mobile device helped them to turn in homework, assignments, and tests more efficiently, while eighty seven percent thought that it enabled them to receive instructor feedback more quickly and efficiently. Sixty seven percent of the participants either Agreed or Strongly Agreed that use of the mobile device, helped them to create content demonstrating learning more efficiently. Sixty percent of the participants either Agreed or Strongly Agreed that using a mobile device to create video content demonstrating learning, enabled them to master the content better than they would be able to do otherwise. Fifty five percent of the participants either Agreed or Strongly Agreed that the mobile device helped them to communicate more easily with the instructor, through use of online office hours. Sixty two percent of the participants either Agreed or Strongly Agreed that use of the mobile device helped them to work collaboratively in groups during problem solving, while sixty nine percent reported that it helped them to prepare better for examinations.

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A follow-up study was conducted to determine whether the use of mobile learning strategies and devices supported students to perceive themselves as efficient creators of electronic content for inclusion in their individual e-portfolios (Bose & Pakala, 2015). Results from the survey indicated that at least 51% of the participants either Agreed or Strongly Agreed that the mobile device supported them in the completion of various aspects of the e-portfolio content creation process (Bose & Pakala, 2015). Moreover, at least 60% of the participants thought the same, when it came to decide whether the use of a mobile device supported an efficient teaching and learning strategy. In general, students perceived that the use of an iPad to create content increased their engagement with course materials. They thought more deeply about the content and retained longer. At least 80% of the participants reported that use of the iPad enabled them to be better managers of information (more organized for class; access multiple sources of information). Students were able to receive feedback more frequently from their instructor (turn in homework, assignments, tests; receive instructor feedback). However, even though students had access to a mobile device, it was not necessarily used to communicate with the instructor, with only 58% of the participants reporting that it made communication easier. Preparation for examinations for 84% of the participants was facilitated through use of mobile devices. Only 69% of the participants felt that content creation and mastery of concepts was facilitated through use of mobile devices.

Data Collection and Results In the first study, during the course of the 16 week semester, students enrolled in the course were placed in groups where they collaborated with each other to create multimedia videos - produced content demonstrating course content summaries, problem solving techniques, and written work on concept question solutions (Bose & Pakala, 2014). At the end of the semester, students were invited to participate in a voluntary and anonymous online survey (21 item Likert scale instrument) regarding their

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perceptions of the efficacy of using mobile learning strategies and devices to create electronic content for inclusion in the engineering team eportfolios. At the end of the semester, students were invited to participate in a voluntary focus group meeting. Five students out of a class of 64, volunteered to participate. The focus group meeting discussion was guided by six pre-determined questions designed to elicit student perceptions on the use of mobile learning strategies and devices. Though most students found that the use of the iPad increased their interaction with the course instructor, some participants found the use of the device for content creation, to be technologically challenging (Bose & Pakala, 2015). This had a positive impact on student-instructor communication, in that it encouraged some students to contact the instructor for help with using the device. While the use of the iPad was found to be advantageous for most students, some reported certain disadvantages. It was often time consuming to use some of the applications to create videos, which time could have been better utilized studying. Students who had never used an iPad before, experienced a steep learning curve. Also, the easy access to the internet through the device was often a source of distraction from focusing on course materials. Most students mentioned that they would continue to use mobile devices in future for educational, entertainment, as well as for professional purposes. One of the suggestions to improve this class included more time dedicated to teaching students how to use the device and the applications.

Significance and Impact Based on the observances made in two IRB-approved research studies, recommendations were made on how to use mobile learning strategies and devices most effectively for teaching and learning. These recommendations are geared to be useful for faculty who are currently or planning to implement the use of mobile devices in their classes, while also using an online learning management system to deliver a pedagogically well-designed course.

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Fostering a Mobile Learning Community A third IRB-approved study was also conducted (Bose, Pakala & Grover, 2020). The purpose of this study was to document student perceptions regarding changes in digital fluency and communication after participation in a Living Learning Community (LLC) based Mobile Learning Community (MLC). Results of this study indicated that over time, students perceived that their mobile device digital fluency and communication skills changed as a result of having personal access to a mobile device. Also, students thought that mobile devices supported their learning and they intended to use mobile devices for future courses and tasks. Based on the findings of this study, some recommendations were made which might be useful for faculty aiming to use mobile devices to support teaching and learning.

Mobile Learning App Highlights Notability Interactive/digital note taking has been one of the most impactful practices for our engineering students. Due to the content-heavy nature of an engineering course, many students were missing important parts of the lecture while trying to take notes. When the practice of interactive notetaking was thoughtfully combined with the capabilities of digital notetaking, it made a significant impact on student success. Faculty also use Notability to teach wireless in class (formula solving. etc.) and to easily catalog and share their notes with the class. Example: 70% of the lecture slide content is provided to students on PDF, which they then uploaded into Notability prior to class. However, 30% of the highest priority content is missing and open for students to fill as they take notes during the course. Students then save their notes directly into their Google Drive folder, creating a library of notes over the course of the semester.

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Zoom Cloud Meetings Cloud-based meeting platforms, like Zoom, have allowed our faculty to hold virtual office hours. Students can easily access the office hours from their iPads, and iPhones. As a result (in combinations with a thoughtfully planned session), there has been a significant uptake in student attendance at office hours.

Explain Everything Using Explain Everything, students narrated their solution process of an assigned homework formula. This allowed both the instructor and student to identify hidden gaps in knowledge. Learning videos were developed using mobile devices which were popular among students and had a significant impact on student learning. Explain Everything has also been used by our faculty to create instructional problem-solving videos for a flipped classroom.

Google Drive Using Google Drive our faculty have created an all-digital workflow for student assignments and exams. Each student has an individual folder where they can access all of their homework assignments and exams. Exams and assignments are returned much more quickly allowing students to more immediately reflect and make necessary adjustments.

Socrative Socrative has been used frequently for in-class quizzes and quick formative assessments of understanding.

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iMovie Faculty use iMovie to create engaging class introduction trailers, which help set the tone for the semester

Doceri Doceri has been used for wireless teaching with real-time annotation of PPT slides and easy to access whiteboards for formula solving.

Piazza Students use Piazza as a collaboration space and to post questions for faculty and TAs.

Teaching Assistants TA’s use the iPads in a variety of ways, to communicate with students, schedule study sessions, and grade assignments.

TEACHING AND LEARNING OF THERMODYNAMICS IN AN ONLINE ENVIRONMENT Thermodynamics is among the most difficult engineering courses to teach (Hall et al., 2010; Reardon, 2001). This section describes the design and assessment of an online Thermodynamics (Yang & Pakala, 2017a, b) using the Blackboard Exemplary Course Program rubric (Blackboard, 2017). The Exemplary Course Program recognizes instructors and course designers whose courses demonstrate best practices in four major areas:

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Course Design, Interaction & Collaboration, Assessment, and Learner Support.

Course Design Course Design encompasses elements as structure of the course, learning objectives, organization of content, and instructional strategies.

Goals and Objectives The course learning goals focused on several levels of Bloom’s taxonomy and are listed in the syllabus. The course goals and objectives are clearly written and can be easily located. They are available in several locations like the syllabus and in each learning unit. Learners are introduced to the learning objectives (LOs) in each module, so that they know what they are expected to be able to do after completing the module. The instructor has taught this course in multiple modes 13 times for over 500 students. End-of-semester course evaluations show that 433/479 (over 90%) of the students either agreed or strongly agreed that the objectives of the course were clearly explained to them. An instructor created course preview video (https://youtu.be/tMbQ86ISZ1w) serves as a motivation for students to learn Thermodynamics. Another video created by the instructor provides tips to achieve course goals (https://youtu.be/zkoUjHCliX0).

Content Presentation The course was delivered in seven manageable modules. Students were provided a general course overview, schedule of activities, instructions guiding exploration of the course website, and indications on what to do first, in addition to detailed navigational instructions for the whole course. The headings and titles of various course materials and the

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setting/organization were designed to provide students easy access to course components using no more than three clicks to reach a course element. Each module has lectures, recitation videos and notes (pdfs). Clickable table of contents and embedded quizzes are a unique feature of the lecture videos. Students are provided instructions for navigation in the Videos content area.

Learner’s Engagement Each module of the course contains activities for each week, clearly stating the week’s expectations from the learner, in terms of readings, lecture/recitation videos, homework, learning assistant sessions and virtual office hours that need to be completed. Also, it clearly states how content area (materials covered in the course) topics are connected to LOs. This serves as a guidance for students on how their engagement with course content leads them to reach course LOs. The instructor meets students virtually to provide additional learning support. Learners engage with content first through instructor created videos delivered through the LMS, and then participate in group problem solving in the virtual office hour. Student engagement is supported by the instructor and learning assistant. Additional resources such animated engines, real world examples and a study guide are provided.

Technology Use Native Blackboard tools like Collaborate was used extensively to host the virtual office hours. Creative use of existing mobile technology was made when the instructor shared the iPad screen with students so that they could engage in problem solving while the instructor wrote on the iPad. No-cost access to learning was enabled through the live sessions which were recorded and posted on YouTube (https://youtu.be/A1v7XZQvpIg). Timely feedback was facilitated using free technology platforms like

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Google Drive, which was used to return graded exams/quizzes. Instructor used iMovie creatively to produce a trailer for his course (https://youtu.be/M3EzFzNIG5w).

Interaction and Collaboration Interaction and Collaboration should be in different forms and an adequate amount of interaction and collaboration within an online environment is essential. This Interaction could be a communication between and among students and instructors, synchronously or asynchronously. Collaboration is an integral part of this interaction in which groups works independently to achieve a desired outcome. Fostering a vibrant learning community in the online courses is critical as this will help students develop a sense of belonging to a group and improves their self-efficacy.

Communication Strategies Thermodynamics course was taught with many opportunities for synchronous and asynchronous interaction. Students meet the instructor and peers during virtual office hours while they engage with content online. This opportunity for virtual communication enables discussion, group work, and direct individual verbal response exchange. Prompt questions in the virtual office hours are provided by the instructor. Students also interact with peers during Learning Assistant (LA) sessions. Students are welcome to email the instructor or the LA with any questions. Students have the option of attending 3 LA sessions (1.5 hours each) and two virtual office hours (1 hour each) every week.

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Development of a Learning Community As described in the “Learner Engagement” section of this chapter, students are encouraged to ask questions to their peers, the instructor, and the Learning Assistant (LA). Since this course follows the hybrid model of learning, students engage with content by watching instructional content videos, take notes, and identify questions. During virtual office hours, students get the opportunity to initiate contact with the instructor and peers as they engage in reinforcing course content. Students engage in collaboration as they work in teams at every LA session, supported by the LA who supports learners to problem solve. Thermodynamics end-ofsemester course evaluations show that 441/479 (92%) of the students either agreed or strongly agreed that the instructor fostered learning.

Interaction Logistics Communication in this course occurs through multiple means - online (email, virtual office hour, announcement) as well as face-to-face (LA session). The instructor uses the Announcement section of the LMS to communicate and remind students of important course related information. Though there is no formal rubric detailing interaction expectations, the instructor provides verbal scaffolding during virtual office hours to elicit feedback from students. A mid-term anonymous evaluation survey provides data on state of interaction. Solutions to In-class problems, Homework, pre-tests, quizzes and exams are posted. Thermodynamics mid-semester course evaluations show that 90% of the students said the Learning Assistant (LA) interactions were either extremely useful/very useful for their learning. The syllabus indicates the LA session participation requirements and also highlights how those sessions will benefit them.

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Assessment Assessment focuses on instructional activities that foster measuring learner progress toward achieving course goals, effective feedback. This section describes the quality and type of student assessments within the course.

Expectations There are a variety of assessments (quiz and exam as formal assessment; homework and virtual office hours as informal assessment) included in the course. The assignments and the feedback received both from the course instructor and the Learning Assistant (LA) helps the students engage and reflect on their learning. All the major components of the course are integrated. That is, the learning goals, the materials, the teaching/learning activities, and the feedback and assessment are closely aligned with and support each other, which was critical to achieve the learning goals and support student success. The course objectives reflect different levels of learning that are aligned with appropriate assessments. The learning activities provide students the opportunity to engage and reflect (such as the peer tutoring activity). The assessments allow students to further reflect and self-assess themselves (such as non-graded homework and recitation videos). Thermodynamics end-of-semester course evaluations show that 414/479 (over 86%) of the students either agreed or strongly agreed that the assessment methods used in the course were clearly explained to them. This alignment is clearly presented to the learners. All quizzes and exams are paired with point-based rubrics to clarify the expectations of content mastery.

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Assessment Design The instructor believes that assessment drives learning. The course has timely and frequent formative and summative assessments. The course also has couple of projects which provides students to engage in problem-based learning, thus transferring the concepts from their Thermodynamics course to real world problems. While assessments measure both higher and lower levels of learning, learners in this course often engage in higher order skill like application, analysis, synthesis, evaluation and work with problems they are likely to encounter as future engineers. Formative assessment occurs frequently and are interspersed strategically throughout the course progression. Assessment types vary as students engage in individual assignments, quizzes, and homework as well as group problem-solving and test re-dos. Students get access to assessments (homework) online through the LMS enabling anytime, anywhere access. Student are given the opportunity to use the assessment as a learning opportunity, supported by peer instruction and one-on-one instructor coaching. Thermodynamics endof-semester course evaluations show that 414/479 (over 86%) of the students either agreed or strongly agreed that the overall, the quality of the course was excellent.

Self-Assessment Students are provided with lecture assignments and homework solutions which allow them to self-assess their work and approach the instructor or the LA during virtual office hours for further clarifications/feedback. Also, self-assessment opportunity is provided to students via exam re-dos. Thermodynamics end-of-semester course evaluations show that 352/391 (90%) of the students either agreed or strongly agreed that by taking this course, they gained skills and knowledge that will help them achieve their career goals.

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Learner Support This section describes resources available to students taking the course that may be accessible within or external to the course environment.

Orientation to Course and LMS Module 00: Getting Started is clearly labeled and provides general structure of this course and instructor expectations. It also clearly informs students about the required technology needed to succeed in this class. In addition, this module directs them all to valuable student services and resources. A specific video is provided (https://youtu.be/-3jfiHSHXNE). Another video is provided to understand how to use formulae sheet and other materials provided to students during quizzes and exams (http://youtu.be/sH1lUddEetM). Supportive Technologies The course allows different scientific calculators, mobile devices, Google Drive, Blackboard Collaborate. The syllabus clearly states the resources needed. A technology help is also provided for Google Drive use. Instructor’s Role and Information The course syllabus provides detailed information on instructor contact information and availability. Multiple ways of contacting the instructor are available (phone, email, virtual office hours). The Learning Assistant is also identified. The instructor is available to students on Sundays and in the evening. Suggestions for appropriate email communication etiquettes and expected email response time are clearly indicated in the syllabus. Students are requested sign up and share the google drive folder by the first day of class which is used for sharing graded work with the students by the instructor/grader.

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Course/Institutional Policies & Support Policies regarding attendance, make-up policy, types of calculator, student etiquette, electronic device use and plagiarism and intellectual honesty are clearly stated in the syllabus. Information leading students to other university resources on the topic of Academic Honesty is also provided. The Library Resources tab gives easy access to library resources (https://guides.boisestate.edu/mbe) specifically meant for engineering students. Technical Accessibility Captioned streaming videos are provided on YouTube which deliver lesson content to enable learners of various ability levels to access the course content. All documents accessed through the LMS are electronic and as such, accessible using a document reader. Accommodations for Disabilities The syllabus encourages students requiring accommodations to contact the Educational Access Center. A link is provided in the syllabus (https://www.boisestate.edu/eac/) which details the accommodation process. Multiple means of accessing content and demonstration of learning are possible through use of instructor created video content, synchronous virtual office hours, peer learning, learning assistant support. Feedback Learners can directly talk with the instructor during virtual office hours/in person/email/phone or with the learning assistant. The instructor administers a mid-semester evaluation in addition to end of semester course evaluations to provide feedback both of which are anonymous. The mid-semester evaluation provides the instructor to make appropriate changes in the course design and delivery for which feedback is received in the final course evaluations.

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Few “Stand-Out Practices” in the Course Elevating Pedagogy with Technology The thermodynamics course is hosted in Blackboard, which is the university’s Learning Management System. The content is also made platform-independent such that the learners could review the online course materials and can complete their homework/quizzes/exams using personal mobile devices/iPads. The instructor teaches this course in hybrid, flipped, online and face to face formats, making sure the solid pedagogical design is executed in all modes of course delivery. For the recorded captioned videos, the students can review the videos as much as needed. There are also embedded quizzes in the recorded online videos to help students stay on track and to engage students in actively applying their learning. This repeated anytime, anywhere accessible content, helps students to learn more efficiently. (Students can review the content at different points in their learning process. Students with special learning needs or English as second language learners can re-watch at their own pace, until the content is clear.) Students are then provided strong peer to peer interaction supported by a learning assistant in the learning assistant sessions. Graded work is electronically turned into students using a shared google drive. Also, the instructor has created quizzes which help with the formative assessment of learning. The design of the assessments used in the course supports learning. Virtual hours are facilitated seamlessly using Blackboard Collaborate with increased attendance compared to traditional office hours. The course demonstrates sound instructional design principles by clearly defining Learning Objectives (LOs), then explicitly showing the alignment between its instructional material (videos and readings), assignments and assessments to these LOs. Videos cover LOs and explicitly tie them to the course content. At multiple points in the course (Syllabus, Schedule, Modules) this alignment is clearly shown to the students. The course is presented in the form of clearly delineated content area(s) called Modules thus providing students with structured learning and access to materials in chunks.

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Online/Virtual Office Hours Office hours that happen in a virtual environment are called as virtual office hours which can easily be hosted from a faculty computer. With an invitation, students can login to the online session and join their instructor and peers in a virtual space. This course used mobile technologies, where students could join virtual office hours from a variety of locations including, the library, outdoors, on the commute ride home, while caring for children, eating dinner, and even while grocery shopping! Virtual office hours as used in this course allowed the instructor to connect with students anytime and anywhere. Virtual office hours were used as an alternative to traditional office hours. The virtual environment can provide students and the instructor with more flexibility in meeting times and locations, content delivery, and types of interaction. The virtual office space provided a lowstakes platform for discussions, allowing students to better articulate their thought processes. Successful virtual sessions are dependent on thoughtful design. Questions which encourage deeper thinking, problem-solving, and critical analysis, essential to student engagement and to the success of a virtual session were used in this course. Careful design and implementation of virtual office hours has been highly valuable for Thermodynamics and this experience enhanced students’ learning experience. In an effort to reconceptualize the virtual office hours to occur in a more informal setting, where student-student, student-content, student-instructor interactions can be enhanced, the instructor named them “Happy Hour”. Intentional Use of Active Learning in Course Design Thermodynamics I has been designed so that there is ample opportunities for 1) faculty to student, 2) student to student and 3) student to content interactions. The course was designed enabling multiple learning opportunities and encourage peer learning and tutoring. These include students’ review of pre-recorded videos, recitation videos, online discussions, peer learning, peer tutoring, and Learning Assistant (LA) sessions. These learning opportunities helped students not only learn but also built a great learning community. “Happy Hour”- a virtual office hour hosted by the instructor was another key element. The course connected

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students to Everyday Examples in Engineering (E³s), engineering concepts to which students can readily relate. Some E3s used were: Using a tire gauge to measure the pressure in a bicycle tire, using mobile devices to find the current outdoor temperature, and then converting that reading to different temperature scales, discussing open and closed systems and the properties of pure substances while brewing and drinking coffee, demonstrating a steam engine to explain energy conversion. These E3s were small demonstrations that were done in the traditional class but were made available in the lecture videos for the online class. The course also included active learning activities such as applying the learned principles/knowledge in helping a peer in peer learning groups and peer tutoring sessions. The LA sessions were scheduled time slots when students were encouraged to come to a study area and work in small groups on assignments while an LA or instructor was present to help. All students were not only encouraged to come to the LA sessions but also encouraged to lead the LA session by peer tutoring or explaining a problem to other students. Students who peer tutored or took the lead in explaining a problem to his/her peers would be awarded a peer tutor certificate. A peer tutor certificate was accounted five points (a very small percentage) toward the final grade.

CONCLUSION In this chapter, we presented the full integration of mobile devices in several undergraduate mechanical engineering courses. We discussed the pedagogical approaches utilized to leverage technology for the benefit of students learning. In this integration, we considered both teaching and learning perception. Based on the instructors and students’ experiences, we are optimistic that the use of mobile devices as a learning tool can enhance learning when the pedagogy is sound and when there is a good match of technology, instructional methods and learning objectives. We advocate for leveraging technological tools for pedagogy more aggressively in the engineering classroom. Further, we call for more empirical studies

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investigating the use of technological tools in various STEM classrooms and their benefits to learning. The ease of availability of technology in modern times and its effectiveness proven by several studies, including the ones presented in this chapter, shows promise for integrating technological tools widely in engineering classrooms in the near future. This chapter adds to the growing body of research on how current technology can be applied in education from the perspective of both instructors and learners; additionally, it shifts the conversation from the engineering educators of the future to the close reality of growing community of innovators who are dynamic and adaptable to novel technologies for the benefit of our engineering graduates.

REFERENCES Alsop, S. (2015). The ‘Humour’ element in engineering lectures across cultures: An approach to pragmatic annotation. Language & Computers, 79, 337–361. Amaro, A. C., Oliveira, L. & Veloso, A. I. (2017). Intergenerational and collaborative use of tablets: «in-medium» and «in-room» communication, learning and interaction. Observatorions (OBS*), 11(1), 83-94. Auyuanet, A., Modzelewski, H., Loureiro, S., Alessandrini, D. & Míguez, M. (2018). FísicActiva: applying active learning strategies to a large engineering lecture. European Journal of Engineering Education, 43(1), 55–64. Ballard, D. (2018, December 11). Universities cannot afford to overlook technology as a retention tool. [Blog post]. Retrieved from https://edtechmagazine.com/higher/article/2018/12/universities-cantafford-overlook-technology-retention-tool#7Ye0F1fPWpioUgdW.03 Blackboard. (2017). https://www.blackboard.com/resources/catalystawards/Bb_Exemplary_Course_Rubric_Apr2017.pdf. Bose, D. & Pakala, K. (2015). Use of mobile learning strategies and devices for e-portfolio content creation in Engineering

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Thermodynamics and Fluid Mechanics classes: Student perceptions. In Proceedings of the ASEE Annual Conference. Seattle, WA, USA: American Society for Engineering Education (ASEE). Retrieved January 14, 2019 from https:// scholarworks.boisestate.edu/ cgi/ viewcontent.cgi?article=1058&context=mecheng_facpubs. Bose, D. & Pakala, K. (2014). Learner perceptions on the use of mobile learning strategies and devices for team e-portfolio content creation. In T. Bastiaens (ed.), Proceedings of World Conference on E-Learning (pp. 242-251). New Orleans, LA, USA: Association for the Advancement of Computing in Education (AACE). Retrieved January 14, 2019 from https://www.learntechlib.org/primary/p/149027/. Bose, D., Pakala, K. & Grover, L. (2020). A Mobile Learning Community in a Living Learning Community: Impact on digital fluency and communication. The Online Journal of New Horizons in Education, 10(1), 1-22. Barden, O. & Bygroves, M. (2017). ‘I wouldn’t be able to graduate if it wasn’t for my mobile phone. The affordances of mobile devices in the construction of complex academic texts. Innovations in Education and Teaching International, 55(5), 555-565. Bebell, D. & Kay, R. (2010). One to one computing: A summary of the quantitative results from the Berkshire Wireless Learning Initiative. Journal of Technology, Learning, and Assessment, 9(2) [Online journal]. Retrieved from http://escholarship.bc.edu/cgi/ viewcontent.cgi?article=1222&context=jtla. Brandon van der, V., Richard, N., Lise, B. & Alan, G. (2016). The role of the iPad in collaborative learning in a large-enrollment first-year physics module. Physics Education, 51(4), 1. Braghirolli, L. F., Ribeiro, J. L. D., Weise, A. D. & Pizzolato, M. (2016). Benefits of educational games as an introductory activity in industrial engineering education. Computers in Human Behavior, 58, 315-324. Brown, J. S., Collins, A. & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18 (1), 32-42.

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Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T. & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological bulletin, 132(3), 354. Chickering, A. W. & Gamson, Z. F. (1987). Seven principles for good practice in undergraduate education. AAHE bulletin, 3, 7. Falloon, G. (2015). What’s the difference? Learning collaboratively using iPads in conventional classrooms. Computers & Education, 8462-77. Foti, M. K. & Mendez, J. (2014). Mobile learning: How students use mobile devices to support learning. Journal of Literacy and Technology, 15(3), 58-78. Fredriksen, H. (2015). A case study of an experiment using streaming of lectures in teaching engineering mathematics. Teaching Mathematics & Its Applications, 34(1), 44–52. Gibeault, M. J. (2015). Using iPads to facilitate library instruction sessions in a SCALE-UP classroom. College & Undergraduate Libraries, 22(2), 209-223. Hall, S., Amelink, C. T. & Conn, S. S. (2010). A case study of a thermodynamics course: Informing online course design. Journal of Online Engineering Education, 1(2), 201. Kolb, L. (2017). Learning first, technology second: The educator’s guide to designing authentic lessons. Portland, Oregon: International Society for Technology in Education. Kunioshi, N., Noguchi, J., Tojo, K. & Hayashi, H. (2016). Supporting English-medium pedagogy through an online corpus of science and engineering lectures. European Journal of Engineering Education, 41(3), 293–303. Montrieux, H., Venderlinde, R., Schellens, T. & DeMeres, L. (2015). Teaching and learning with mobile technology: A qualitative explorative study about the introduction of tablets devices in secondary education. PloS One, 10 (2), e0144008. Lave, J. & Wenger, E. (1990). Situated Learning: Legitimate Peripheral Participation. Cambridge, UK: Cambridge University Press.

Leveraging Technology to Elevate Pedagogy in Mechanical …

65

Linnenbrink, E. A. & Pintrich, P. R. (2003). The role of self-efficacy beliefs in student engagement and learning in the classroom. Reading and Writing Quarterly, 19, 119-137. O’Hara, A. (2018, October 25). Massive iPad deployment at OSU paying long-term dividends in higher education. [Blog post]. Retrieved from https://appleinsider.com/articles/18/10/25/massive-ipad-deploymentat-osu-paying-long-term-dividends-in-higher-education. Okojie, M. C. P. O., Olinzock, A. A. & Okojie-Boulder, T. C. (2006). The pedagogy of technology integration. Journal of Technology Studies, 32(2), 66-71. Pakala, V. K. C., Bairaktarova, D. & Schauer, S. (2019). Happy Hours are a Godsend. In Proceedings of ASEE Annual Conference, June 16 – 19, 2019, Tampa, FL. Pane, J., Steiner, E., Baird, M., Hamilton, L. S. & Pane, J. D. (2017). Informing Progress: Insights on Personalized Learning Implementation and Effects. RAND Cooperation. Retrieved from https://www.rand.org/pubs/research_reports/RR2042.html. Phillips, B. (2019, January 9). Boise State researcher breaks down learning barriers with virtual office space. [Blog post]. Retrieved from https:// www.boisestate.edu/news/2019/01/09/boise-state-researcher-breaksdown-learning-barriers-with-virtual-office-space/. Pomorski, K. & Prokopow, P. (2018). On the Engineering Aspects of Statistical Physics and Theory of Phase Transitions: Lecture 1. Bulletin de La Société Des Sciences et Des Lettres de Lodz, 68(3), 113–139. Jauck, D. & Peralta, O. (2016). Early symbolic comprehension of a digital image as a source of information and as a mean of communication. Anales De Psicología, 32(3), 886-892. Ji-Hyun, K., Hee-Jin, P., Yong-Hwan, J., So-Yeon, L., Eun-Chul, C., HaeWon, P. & Sam Soo, K. (2013). Survey of the use pattern and satisfaction of mobile picture archiving and communication system users with tablet personal computer. Journal of The Korean Society of Radiology, 68(1), 71-76. Johnson, M. & Hayes, M. J. (2016). A comparison of problem-based and didactic learning pedagogies on an electronics engineering course.

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International Journal of Electrical Engineering & Education, 53(1), 3–22. Kay, R. H. & Lauricella, S. (2011). Unstructured versus structured use of laptops in higher education. Journal of Information Technology Education: Innovations in Practice. Retrieved from http:// www.jite.org/documents/Vol10/JITEv10IIPp033-042Kay840.pdf. Kim, J. (2018). Psychological issues and problematic use of smartphone: ADHD’s moderating role in the associations among loneliness, need for social assurance, need for immediate connection, and problematic use of smartphone. Computers in Human Behavior, 80390-398. King, J. & South, J. (2017). Reimagining the Role of Technology in Higher Education. Retrieved January 15, 2019 from https:// tech.ed.gov/files/2017/01/Higher-Ed-NETP.pdf. Kinnari-Korpela, H. (2015). Using short video lectures to enhance mathematics learning - experiences on differential and integral calculus course for engineering students. Informatics in Education - An International Journal, 1, 69-83. Koretsky, M. D. & Magana, A. J. (2019). Using Technology to Enhance Learning and Engagement in Engineering. Advances in Engineering Education. Kukulska-Hulme, A. & Viberg, O. (2018). Mobile collaborative language learning: State of the art. British Journal of Educational Technology, 49(2), 207-218. Langius-Eklöf, A., Crafoord, M., Christiansen, M., Fjell, M. & Sundberg, K. (2017). Effects of an interactive mHealth innovation for early detection of patient-reported symptom distress with focus on participatory care: protocol for a study based on prospective, randomised, controlled trials in patients with prostate and breast cancer. BMC Cancer, 171-10. Liao, T. & Humphreys, L. (2014). Layar-ed places: Using mobile augmented reality to tactically reengage, reproduce, and reappropriate public space. New Media & Society, 17(9), 1418-1435. Mac Callum, Kathryn. & Parsons, David. (2016). A Theory-ology of Mobile Learning: Operationalizing Learning Theories with Mobile

Leveraging Technology to Elevate Pedagogy in Mechanical …

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Activities. In Conference Proceedings of 15th World Conference on Mobile and Contextual Learning. Retrieved from https:// unitec.researchbank.ac.nz/handle/10652/3861. Mallet, K. H., Shamloul, R. M., Corbett, D., Finestone, H. M., Hatcher, S., Lumsden, J. & Dowlatshahi, D. (2016). Recover Now: Feasibility of a mobile tablet-based rehabilitation intervention to treat post-stroke communication deficits in the acute care setting. Plos ONE, 11(12), 112. Montrieux, H., Vanderlinde, R., Schellens, T. & De Marez, L. (2015). Teaching and learning with mobile technology: A qualitative explorative study about the introduction of tablet devices in secondary education. Plos ONE, 10(12), 1-17. Naylor, A. & Gibbs, J. (2015). Using iPads as a learning tool in crosscurricular collaborative initial teacher education. Journal of Education for Teaching, 41(4), 442-446. Pedro, L. F. M. G., Barbosa, C. M. M. & Santos, C. M. (2018). A critical review of mobile learning integration in formal educational contexts. International Journal of Educational Technology in Higher Education, 15(10). Reardon, F. H. (2001). Developing problem-solving skills in thermodynamics courses. 2001 ASEE Annual Conference and Exposition, Albuquerque, New Mexico. Riley, P. (2013). Teaching, learning, and living with iPads. Music Educators Journal, 100(1), 81-86. Scott, C. L. (2015). The futures of learning 3: What kind of pedagogies for the 21st century? Paris, France: UNESCO Education Research and Foresight, ERF Working Paper Series, No. 15. Serdyukov, P. (2017). Innovation in education: What works, what doesn’t, and what to do about it? Journal of Research in Innovative Teaching & Learning, 10(1), 4-33. Sharples, M., Taylor, J. & Vavoula, G. N. (2005). Towards a Theory of Mobile Learning. Retrieved from https://www.semanticscholar.org/ paper/Towards-a-Theory-of-Mobile-Learning-SharplesTaylor/0315fe693611ad72810a9d0238c32d6ee8edb207.

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Smith, S. M. & Rothkopf, E. Z. (1984). Contextual enrichment and distribution of practice in the classroom. Cognition and Instruction, 1(3), 341-358. Soderstrom, N. C. & Bjork, R. A. (2015). Learning versus performance: An integrative review. Perspectives on Psychological Science, 10(2), 176-199. Souleles, N. (2017). iPad versus traditional tools in art and design: A complementary association. British Journal of Educational Technology, 48(2), 586-597. Templeton, M. (2019). Purdue Aviation Technology students receive iPads, Apple Pencils. [Blog post]. Retrieved from https:// polytechnic.purdue.edu/newsroom/purdue-aviation-technologystudents-receive-ipads-apple-pencils. Wakefield, J., Frawley, J. K., Tyler, J. & Dyson, L. E. (2018). The impact of an iPad-supported annotation and sharing technology on university students’ learning. Computers & Education, 122243-259. Welsh, K. E., Mauchline, A. L., Powell, V., France, D., Park, J. R. & Whalley, W. B. (2015). Student perceptions of iPads as mobile learning devices for fieldwork. Journal of Geography in Higher Education, 39(3), 450-469. Yang, D. & Pakala, V. K. C. (2017a). Building an effective online thermodynamics course for undergraduate engineering students, Proceedings of ASEE Annual Conference, June 25 – 28, 2017, Columbus, OH. Yang, D. & Pakala, V. K. C. (2017b). The Design and Implementation Outcome of an Online Undergraduate Thermodynamics Class. Journal of Online Engineering Education, 8(2). Zhen, C. (2017). Improving early reading skills in young children through an iPad app: Small-group instruction and observational learning. Rural Special Education Quarterly, 36(2), 101-111. doi:10.1177/ 8756870517712491. Zimmerman, E. (2018, July 13). More students rely on mobile devices to complete online classes. [Blog post]. Retrieved from https://

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In: Mechanical Engineering Education … ISBN: 978-1-53617-791-6 Editor: Charles E. Baukal, Jr. © 2020 Nova Science Publishers, Inc.

Chapter 3

MASTERY-BASED LEARNING: FROM EXPOSURE TO EXPERTISE Kurt M. DeGoede, PhD and Sara A. Atwood, PhD Elizabethtown College, Elizabethtown PA, US

Keywords: mastery-based assessment

learning,

competency-based

learning,

INTRODUCTION The chapter presents an overview of the differences between traditional and mastery-based approaches in the context of content-focused mechanical engineering courses that have successfully implemented and refined the mastery-based approach. Rather than assessing the students on how well they performed the many skills studied (traditional grading system), with mastery-based learning (MBL) students are assessed on how many skills they can do well. Proficiency can be demonstrated by exam or other means. Students demonstrate proficiency on additional skills to earn higher grades.

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Research results suggest higher levels of student learning with this grading paradigm. In particular, students earning average or lower grades exit the course with an increased skill level over traditional grading systems. In many traditionally graded courses, the average student can almost apply the material covered in the course but does not reach full mastery of any of these skills.

BACKGROUND ON THE USE OF MASTERY BASED LEARNING Pedagogical Foundations Mastery-Based Learning (MBL) traces a linage back to the work of Benjamin Bloom assuming all students could master the learning objectives in a curriculum if given enough time (Bloom, 1976). In traditional grading systems for educational environments with finite time spans, the curriculum fixes a number of topics, and students are assessed on how well they perform across all those topics. In an MBL assessment structure, the number of topics may vary from student to student over a fixed time period, but all students are expected to demonstrate full mastery of each topic, before moving to the next (Henri, 2017; Tyler, 2013; Bloom, 1984). Thomas Guskey explored the best practices in MBL systems of assessment and found several themes:    

Introducing new concepts and skills in unison to the entire student population Providing frequent formative assessment opportunities Creating opportunities for students to learn from unsuccessful assessment attempts Individually coaching students through learning.

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“Compared with students in traditionally taught classes, students in well-implemented mastery learning classes consistently reach higher levels of achievement and develop greater confidence in their ability to learn and in themselves as learners” (Guskey, 2010). While Mastery-Based assessment does not require it, the model outlined in this chapter utilizes an active learning classroom environment. Active learning teaching styles have been shown to increase student mastery of STEM topics (Freeman, 2014; Prince, 2004).

MBL in Engineering Usage of MBL and similar pedagogies has increased in engineering curricula in recent years (Henri, 2017; Felder, 2011; Froyd, 2012). Specific examples include restructuring a two-year AS Engineering Technology program to an open entry/open exit format centered on self-paced Competency-Based assessment (Roe & Bartelt, 2015). Others have approached assessment of program level learning outcomes as competencies, i.e., Engineering Knowledge, Innovation, Communication, and Planning, assessed through specific course level assignments and digital portfolios (Brumm, 2006). Standards-based grading has been used for formative and summative assessment in project and design-based courses (Carberry, 2016; Atwood, 2014; Lee, 2018). Others have applied the approach within individual courses: engineering statics (Craugh, 2017), fluid mechanics (Post, 2017), thermodynamics (Mendez, 2018; Mendez, 2018), and dynamics (DeGoede, 2018). In this chapter, we will focus on this type of course level implementation. As with other fields of study, MBL has been demonstrated to increase student learning. In the engineering statics course, students in one section of the course had mastery-based assessments, while a control of traditional exams was set in other sections (Craugh, 2017). The students took common final exams, and their data illustrated that students entering with lower predictors of success (i.e., lower incoming GPA) performed better on the final exam, “after being forced to demonstrate competency

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throughout the semester instead of taking their (grading) lumps and moving on.” The students entering with higher predictors of success performed similarly on the final exam. Mendez has implemented MBL grading in thermodynamics, with an eye toward the on-line learning environment. In her course, across topics on which students were required to demonstrate mastery to earn a course grade of C or higher, mastery rates went from 47, 67, 93 and 53% to 100% for all four (Mendez, 2018). Similarly, in the dynamics course on skills required to earn a course grade of C-, student performance increased sharply (DeGoede, 2018) (Figure 1).

Figure 1. Comparing proficiency rate between traditional (Trad), Mastery (MBL) offerings of a Dynamics Course. +Chi-Squared p < 0.02 between Trad and MBL1, # p < 0.02 between MBL1 and MBL2, indicating a significant difference between the two years. Chi-Squared p = 0.08 for skill S2.2 (Trad, MBL1). Data are not available for Skill AB2 in Trad. The C skills were required for a student to earn a C in the MBL courses and included deriving equations of motion for a rigid body undergoing general motion in the plane. The AB skills included supplemental skills such as 3D analysis and oblique impact with varying coefficient of restitution (DeGoede, 2018).

This is not magic. Our traditional grading structures encourage students to practice a new skill for a fixed amount of time, then move on to

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the next topic no matter how well they mastered the previous topic. The MBL grading paradigm requires mastery before moving forward. Therefore, students spend more time on fundamental topics, and as a result demonstrate higher levels of mastery of those key topics. To earn a C in a traditionally graded course typically requires students to participate in weekly graded homework assignments (often a smaller but significant part of the course grade), and measures ability primarily through 2-4 in class exams. Students often earn a C through partial credit, knowing something about completing an analysis, but falling short of correctly completing any of the required problems. In the mastery-based approach presented here, students earning a C have demonstrated a complete ability to perform the subset of skills identified as essential by the instructor.

Implementation of MBL across the Curriculum At Elizabethtown College, we have implemented MBL in a variety of content-focused mechanical engineering courses ranging from 100-level to 400-level. These courses include an applied fundamental mathematics course and engineering science courses in statics, dynamics, and vibration analysis. Our experience of using MBL over several years with multiple cohorts of students has resulted in two refined models of implementation: a prioritized skill model, and a prioritized breadth model. In 2014, we had become frustrated with performance of some students at the end of courses. We observed that many of these students could not yet demonstrate an ability to entirely complete what we considered essential analyses in the area of study of the course. These students could get started on an analysis, perhaps identifying the correct equations to use, but then struggled to correctly complete the work. We looked to create a grading framework to ensure that all students had demonstrated an ability to complete these key analyses at least one time in a proctored exam setting.

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Prioritized Skill Model The approach we have used most often can be described as prioritizing the specific learning outcomes, or skills, in a particular course and then assigning grades based on how many skills the students master. Often the grade in the course moves up 1/3 of a letter grade for each skill mastered; the skills are also grouped into tiers that students must ‘unlock’ before moving on. This structure allows the most important skills to be prioritized over other skills that are considered interesting but not as important. Grade distributions among our students have remained constant, but the understanding of what a grade of C or B means has changed dramatically (Figure 2).

Figure 2. Grade distribution across traditional offerings and Mastery based offerings of dynamics, 2 sections of each (DeGoede, 2018).

One simple model for implementing mastery-based grading is described by the flow outlined in Figure 3. To start, identify the specific topics that are taught in the course, and convert those into measurable skills. These can be specific analytical analyses, laboratory techniques, communication activities, whatever the instructor wants the student to be able to do when they leave the course. The structure in Figure 3 assumes a grading system where each skill is worth 1/3 of a letter grade at the end of the semester; for example, mastering one additional skill moves a grade up from a B- to a B. With 12 skills, the grades start at F after the first skill. We name the most-important-tier skills as Foundational or Primary, and they are prerequisite material for the other skills. The system could be

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modified to support more skills, by requiring more than one skill per grade step. We recommend using tiered skills, requiring students to demonstrate all the skills at one tier-level before moving on to the next set of skills. This allows the instructor to prioritize the content in the course. Skills deemed as essential to the course or required for subsequent courses are given priority over the other skills. It is tempting to say that all the skills are essential, but this is not realistic. The A students will master all of the skills, just as they did in the traditional approach; the tiered prioritization allows instructors to choose which skills the weaker students are able to leave the class mastering. Some students will need additional practice to master important skills and will not have enough time in a semester to master all the skills in the course.

Figure 3. Process Flow for setting up a Mastery-Based Grading system.

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Figure 4. Prioritized skills model: skill prioritization and corresponding grading scheme for Statics course.

We use the same types of problems on our skill assessments that we used on traditional exams. In engineering science courses, a skill assessment is typically one textbook style problem. To demonstrate mastery the student must get the problem entirely correct, with allowance for a minor (non-conceptual) error. This roughly translates into what we would have previously scored at the A or A- level on a traditional exam. For the (less important) required or supplemental skills, we also use out of class project assignments, laboratory investigations, simulations, designs, or real-world analyses to assess skill mastery. As a model of how we have prioritized our skills and related them to a grading schema, we have provided our list of skills for both our Statics course (Figure 4) and our Dynamics course (Figure 5).

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Foundational: F1 – Rotate vector quantities between various coordinate systems and take derivatives in a moving CS. Primary Skills (Must master F1 before earning credit for P skills): P1 – Kinematics of 2 connected bodies: determine the accelerations of the CM of any one or two connected bodies in the plane: Find all 𝑎⃗𝐺 and 𝛼⃗ of any system in Ch7.3. {EXAM1} P2 – Construct appropriate FBDs of any system in Ch7.1,2,3 P3 – Apply Newton’s laws of motion to set up equations of motion for a system of 1 or 2 rigid bodies moving in the plane, using skills F1, P1 and P2 (Ch7.1, 2 and 3). {EXAM 2} Required Skills (Must master P skills before earning credit for R skills): R1 – Apply ode45 to solve equations of motion of a rigid body in the plane (App A). {Must submit brief technical report on an individually assigned problem and demonstrate competency on a written quiz, must submit report within 6 class sessions of completing P3}. R2 – Utilize principles of energy and momentum to solve for the motion of a system of 1 or 2 bodies (Ch7.4 and 7.5). R3 – Kinematics: calculate the velocities and accelerations (linear and rotational) of 2 or 3 or more interconnected rigid bodies (Ch6.3 and 6.4). {EXAM 3} Supplemental Skills (Must pass all R skills before earning credit on S skills): S1 – Analyze oblique impact between two rigid bodies (Ch3.8). S2 – Solve for the kinetics of mass flow systems (Ch5.4 and 5.5). S3 – Determine the equation of motion of a rigid body moving in 3D (Ch8.8). {EXAM 4} Take-Home Mini Projects (Online tutorials available in Canvas; must submit a brief technical report on individual assigned problem by 5:00 PM on 6 Dec): S4 – Construct a model and perform analysis of an assigned 2D 4-bar system with SimMechanics {must be completed within 6 class sessions of completing R1}. {Must demonstrate competency with SimMechanics – 15 minute practicum} S5 – Complete an experimental vibrational analysis of single-degree-of-freedom system {Must submit brief technical report} (Ch9).

Figure 5. Prioritized skills model: skill list for Dynamics course (DeGoede, 2018).

PRIORITIZED BREADTH MODEL A mastery-based approach that prioritizes breadth can be implemented if the goal is to have all students to develop a core competency in all topics, while allowing advanced students to explore more challenging applications. Correctly completing fundamental level analyses for all topics positions a student for a grade of C, and higher grades are achieved by correctly completing more advanced mastery level analyses. This model requires a less radical transition from traditional offerings of a course. For each exam think of how students could demonstrate

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fundamental analyses on one topic. These might be level-one-type problems in a typical engineering science text. Fundamental level problems tend to be straightforward applications of the principles and theories studied. The exams then also include mastery-level problems requiring students to solve open-ended or more advanced problems that require higher levels of understanding of the topic.

Figure 6. Prioritized Breadth Model: Skills and Grading for Analytical Mechanics and Vibration Analysis course.

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We have implemented this approach in a 400-level Vibration Analysis course. In this course, the student earns higher grades by demonstrating competency on fundamental-level problems on each of the four unit-exams in the course. Students can improve their grade by demonstrating an ability to solve more challenging applications of the material on mastery-level problems. On each exam, students can make another attempt on previous topics by working new fundamental-level problems. Mastery-level test problems for any of the topics can be retaken on the final exam date. Finally, projects focused on experimental investigation and analysis of vibratory systems provide a final opportunity for students to improve their grades. The prioritized-breadth grading scheme is summarized in Figure 6.

Classroom Sessions Our courses meet for twenty-eight 80-minute sessions during the semester. For these courses, each skill is introduced with one dedicated class day on the syllabus, using active learning techniques including “gapped” notes and breaking the lecture into 5-7 minute mini-lectures interspersed with groupwork on example problems. Approximately nine to twelve topic introduction sessions are interspersed with coaching sessions throughout the semester. Active learning techniques and results in engineering education have been extensively reviewed elsewhere (Prince, 2004). These active learning techniques were also used in the courses in years prior to the MBL approach. Brief conceptual and reading comprehension problems can be used effectively to introduce new topics before a topic introduction session. A selection of short videos introducing topics and working example problems allows students coming back to a skill at a later date to revisit the introduction of the theory. Coaching sessions are held during class after one to two skills have been introduced. At the coaching session, students are grouped based on their progress in the course. The students self-select into two groups: 1) working on new material, and 2) working on old material. Within those groups, they may also be seated with other students who have passed the

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same number of skills. This arrangement creates groups of peers working at the same level – those who can solve problems quickly and challenge one another with difficult material, and those who are struggling to grasp older concepts. Each group is given 2-3 pre-selected problems to work on during the coaching session appropriate for the skills of focus for the group. The instructor alternates between the groups answering questions and aiding the students in working through any conceptual struggles. These pre-selected problems can be part of the assigned homework problems. This arrangement allows the instructor to spend a bit more time in small groups coaching the students who are struggling. We also employ one undergraduate student as a TA during each coaching session. This has been implemented successfully in sections of up to 35 students. Students may not complete all the coaching problems during the class session, but are typically engaged and working actively on problem solving the entire time. Attendance during the class sessions is incorporated into the grading schema only as a penalty: a deduction of 1/3rd of a letter grade occurs if students miss more than a specified number of sessions.

Homework Homework assignments are presented as a tool for learning the skills in the course. They are not designed to assess student learning of that material, as assessment is only done through quizzes/exams or in a few take-home projects. In the traditional course model that has existed for decades, extensive homework assignments were intended to provide a vehicle for students to spend many hours engaging the material by struggling with challenging problems and figuring them out in study groups, with TAs, or during office hours. Since students were spending a great deal of time solving challenging problems on homework, it was often treated as part of summative course assessment and given a relatively large portion of the overall course grade. However, with the availability of homework solutions online and sold through services such as Chegg®, as well as the standardized testing mentality emphasizing ‘getting the right

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answer’ rather than learning the material, we found that our office hours were rarely utilized and that students were not spending time or learning the material engaging homework assignments. MBL addresses these challenges in part through a new homework structure. We have used two homework models to facilitate a mindset shift from a summative assessment tool assigned a relatively large percentage of the course grade in the traditional grading model, to a formative practice tool that students are self-motivated to engage with. The first model leverages developments in online homework platforms such as WileyPlus® and WeBWorK®. For each skill, every student is assigned one online problem set of approximately 5 problems ranging from introductory to challenging. In addition, a problem bank is used to generate up to 5 additional problem sets for each skill that students may elect to do for practice. Students are given access to the full solutions after a specified number of attempts. The second model utilizes meta-cognition in the form of reflectionbased homework assignments. Solutions are provided up-front and students submit a discussion of their work rather than turning in answers to the problems. Students are asked to reflect on new understandings, or continuing points of difficulty. This immediately shifts the focus to learning and mastery and away from getting answers. These homework models facilitate a formative mindset toward the homework as students get immediate feedback and are given access to hints, tutorials, and solutions. Students must complete some level of participation with these activities or another grading penalty (-1/3 of a letter grade) is applied to the final grade. In both models, the expectation is that every student will work one problem set on each skill, and students self-select to work additional practice problems as needed. Some students will be able to master the skill after working only 5 problems; others may need to work 30 problems. This model customizes the way students are spending their time and they quickly grasp the idea that learning is not unlike practicing an instrument or a sport and that everyone has a different ‘set point’ for how much practice they need on each skill. It is common for students to come to office hours with questions on homework that was

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already due and graded – because they know they need to understand the material.

Skill Assessment Mastery of skills is primarily determined through direct testing and occasionally through a project assignment. To demonstrate mastery, students must work one representative problem correctly, with only minor, non-conceptual, errors (for example, miscopying a value). Much like in a traditional course, 2-4 ‘exam’ days are identified in the syllabus and all students test in-class on those days. In the MBL approach, however, each distinct skill is mapped directly and mutually exclusively to one test problem. Students self-select to attempt as many of these problems as they feel ready to tackle – typically two to four. We structure problems that students can complete in 20 minutes, so in 80 minute periods, more than 4 skills assessments would be difficult. If students don’t pass the skill on the exam day, numerous assessment opportunities are available. These assessment days can be part of the normal class session (in a parallel room on coaching days) or at a proctored out-of-class session. The courses typically fit in about 12-15 testing opportunities during the semester, for about 11-12 skills. Students sign-up for tests at least one day in advance, and ‘build-a-test’ from stacks of copied problems for each skill when they enter the room. The problems are always new and distinct. The level of the problem is aimed at a medium level of difficulty, but because there are so many testing opportunities we don’t worry about slight variations in difficulty. While the MBL approach requires more time to generate test problems, time is often saved in other areas. We don’t agonize over finding ‘just the right problem’ for the test, and grading the tests is quick. Problems are either correct (with minor errors) or incorrect. Students often self-assess a problem and then write ‘not ready this time’ or ‘need to work more problems’ rather than trying to put down as much partial credit as they can. We post the solutions to the tests, sometimes with explanatory videos,

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shortly after testing is complete. Overall, the total time dedicated to student assessment is similar to before using MBL. Because there are more frequent assessments, individual tests are lower stakes and anecdotally seem to decrease student testing anxiety. Students know that if they have a bad day or can’t solve the problem, they can come to office hours, work more practice problems, and test again – often the next week. Additionally, if students miss an exam day due to illness or other conflicts, they can simply make it up at the next testing opportunity. With the MBL approach, students have more control over the pace of the testing, are tested over smaller portions of material, and are able to test multiple times to demonstrate mastery. All of these features seem to lead to decreased student anxiety and we are currently testing this hypothesis. On a related note, the MBL testing approach works well considering students with disability accommodations. Instead of giving students extra time on exam days, which led to difficulty in proctoring and in keeping those students’ accommodations confidential, we now offer those students additional testing opportunities (such as during office hours) if needed. Students with accommodations often elect to attempt only one skill per testing opportunity. In four years of implementation, students with accommodations have not requested to arrange additional testing opportunities, as the limitation is typically their level of understanding rather than their disability. One criticism of the MBL testing approach is often a negative reaction to the fact that students demonstrate mastery by working only one representative problem for a skill. However, in mechanical engineering content-based courses, often skills build on one another. Students may get an ‘equations of equilibrium’ problem that is a bit easy and that they happen to be able to solve; however, they will need to use equations of equilibrium to also pass the skills on truss analysis, internal forces in beams, etc. In addition, in the traditional approach students typically only see 1-2 representative problems on traditional exams, and they may only get those problems partially correct.

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CONCLUSION With an MBL assessment structure, students must demonstrate mastery of prerequisite material before moving forward into the next topic. Therefore, we stop the cycle of students failing to master fundamentals leading to an infinite loop of struggle and confusion because of missing prerequisite understanding. For example, in mechanics, it is impossible to correctly solve for equilibrium or for the equations of motion without correctly setting up a free body diagram; MBL ensures all students can correctly construct a free body diagram before attempting the next skill. Students cannot ‘partial credit’ their way to a C. If a student wants to earn a C in an MBL course, they must fully master fundamental skills. MBL will not likely have much effect on the top students in a course, but will significantly change the outcomes for the lower performing students. As a result of this structure, students take ownership of their learning, quickly realizing that almost understanding is not enough. Students who do not achieve mastery after skill assessments become highly motivated to learn the material because they know they cannot simply move on. Students seek out their faculty, TAs and tutors to master topics. If you adopt MBL in your courses be prepared to be very popular during office hours. Similarly, the role of the instructor shifts. The student’s work on the assessment is correct or not, and the instructor’s role is to help students master the material so that they can get it correct. The instructor is now a coach who is “on their side” helping them achieve mastery. With a traditional model, too often a C in a prerequisite course means the student did not really master any of the material in the first course and is unlikely to be able to succeed in mastering any of the material in the next course from that foundation. We have designed our MBL classes such that mastery of the skills required to earn a C in one course will provide a student with an appropriate foundation to earn a C in the next course. This approach has a natural alignment with program-wide assessment such as ABET. With this approach, we can report definitively how many students have performed at an acceptable (mastery) level on which skills, and how

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many students mastered the skills our faculty have determined as ‘acceptable’ for the course. We envision an entire curriculum where we can definitively state that a student with a C average has mastered a core set of skills, and a student with an A average has demonstrated mastery of specified additional skills, embedded in a culture where students are motivated to take ownership of their learning while working alongside instructor facilitators and coaches.

REFERENCES Atwood, S. A., Sinawski, M. T. & Carberry, A., 2014. Using standardsbased grading to effectively assess project-based design courses. Indianapolis, 2014 ASEE Annual Conference & Exposition. Bloom, B. S., 1976. Human Characteristics and School Learning. New York: McGraw-Hill. Bloom, B. S., 1984. The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13(6), pp. 4-16. Brumm, T., Mickelson, S., Steward, B. L. & Kaleita, A., 2006. Competency-based outcomes assessment for agricultural engineering programs. International Journal of Engineering Education, 22(6), p. 1163–1172. Carberry, A., Siniawski, M. T., Atwood, S. A. & Diefes-Dux, H. A., 2016. Best practices for using standards-based grading in engineering courses. New Orleans, 2016 ASEE Annual Conference & Exposition. Craugh, L. E., 2017. Adapted Mastery Grading for Statics. Columbus, Ohio, 2017 ASEE Annual Conference & Exposition. DeGoede, K. M., 2018. Competency Based Assessment in Dynamics. Salt Lake, Utah, 2018 ASEE Annual Conference & Exposition.. Felder, R. M., Brent, R. & Prince, M. J., 2011. Engineering Instructional Development: Programs, Best Practices, and Recommendations. Journal of Engineering Education, 100(1), p. 89–122.

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Freeman, S., Eddy, S.L., McDonough, M., Smith, M. K., Okoroafor, N., Wenderoth, M.P. & Jordt, H., 2014. Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, June, 111(23), pp. 8410-8415. Froyd, J. E., Wankat, P. C. & Smith, K. A., 2012. Five Major Shifts in 100 Years of Engineering Education. Proceedings of the IEEE, Volume 100, pp. 1344-1360. Guskey, T. R., 2010. Lessons of Mastery Learning. Educational Leadership, October, 68(2), pp. 52-57. Henri, M., Johnson, M. D. & Nepal, B., 2017. A review of CompetencyBased Learning: Tools, Assessments, and Recommendations. Journal of Engineering Education, 106(4), pp. 607-638. Lee, E., Carberry, A. R., Diefes-Dux, H. A., Atwood, S. A. & Siniawski, M. T., 2018. Faculty perception before, during and after implementation of standards-based grading. Australasian Journal of Engineering Education, 23(2), pp. 53-61. Mendez, J., 2018. Standards-Based Specifications Grading in a Hybrid Course, Salt Lake City, Utah.. Salt Lake, Utah, 2018 ASEE Annual Conference & Exposition. Mendez, J., 2018. Standards-Based Specifications Grading in Thermodynamics. West Lafayette, IN, ASEE IL-IN Section Conference. Post, S. L., 2017. Standards-Based Grading in a Fluid Mechanics Course. Ingternational Journal of Engineering Pedagogy, 7(1), pp. 173-181. Prince, M., 2004. Does active learning work? A review of the research. Journal of engineering education, 93(3), pp. 223-231. Roe, E. A. & Bartelt, T., 2015. Converting a Traditional Engineering Technology Program to a Competency-based, Self-paced, Openentry/Open-exit Format. Seattle, 2015 ASEE Annual Conference & Exposition. Tyler, R. W., 2013. Basic Principles of Curriculum and Instruction. Chicago: University of Chicago Press.

In: Mechanical Engineering Education … ISBN: 978-1-53617-791-6 Editor: Charles E. Baukal, Jr. © 2020 Nova Science Publishers, Inc.

Chapter 4

ADDRESSING MULTIPLE STUDENT OUTCOMES THROUGH TEAMWORK John L. Krohn Arkansas Tech University, Russellville, Arkansas, US

Keywords: teamwork, assessment, student outcomes

INTRODUCTION Teamwork has always been a double-edged sword in engineering education. Everyone recognizes the need for students to learn to work in teams on projects, as the skills learned are necessary for a successful career and advancement. However, it is inherently difficult to evaluate individual student performance in a group setting. Additionally, all instructors, at virtually any level, are aware that the workload and attainment of desired learning objectives is not distributed evenly among group members. There are, invariably, those students, or, sometimes, that one student, who put forth an inordinate portion of the effort while others simply ride along,

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sometimes learning by observation the objectives and sometimes not, their lack of attainment masked by the overall performance of the group. Despite this difficulty, team-based assignments can be a valuable learning tool in the classroom and lab and can be used to help assess a number of required student outcomes including some of those less amenable to measure in individual assignments. Obviously, the objectives related to teamwork must be assessed in team-based projects. In this chapter, means to use team-based assignments to assess several other learning objectives, some of which had previously proven difficult to assess by other means, will be described. Two examples, one in a laboratory course and one from a lecture course, will be presented with examples of the various learning objectives addressed and personal experience gained from the use of teams in these classes.

MECHANICS OF FLUIDS LAB The first example of the use of teams is in a laboratory setting, namely, in a senior level lab that follows the Mechanics of Fluids course and is devoted to illustrating fluid flow phenomena through experiments. For years, the department had struggled with how to meet the ABET required student outcome related to “design of experiments”. One of the driving forces in developing the currently used format in this lab class was in an effort to address this outcome. While teams, in some form, had been used in this lab for many years, one of the primary driving factors in doing so had been simply the convenience of having fewer lab reports to read as lab sections grew larger. In attempting to use the lab and teams to address the design of experiments question, a much more focused emphasis on teamwork and its components was needed. As described in a previously published paper (Krohn 2013), the process that was developed had as its aim to address the need for experience and instruction in design of experiments. This approach was somewhat similar to that described in another paper (Servoss and Clausen 2012) that had as its aim increased student participation, but was developed

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independently from that effort. The lab experience that has grown out of this effort, in the author’s opinion, gives the students more ownership in the class and lends itself to a better review and understanding of fluid mechanics topics covered by the laboratory exercises, especially those in which the student is a member of the “lead” team. The process that was developed and originally implemented in the lab resulted in the following list of typical semester activities: 1. The students in the class are divided into teams of three to five (with four being the most typical number) students per team. These teams remain together throughout the semester. 2. The first two weeks of the semester are devoted to discussions of report writing, data reduction and analysis, and uncertainty calculations. 3. The initial lab is “instructor presented” in the traditional manner and students are required to submit individual reports 4. For the next three lab experiments, the groups take turns as the “lead” group for the instructor specified experiments. 5. The lead group is responsible for preparing the pre-lab lecture/briefing, determining the type and number of data to be collected, and serving as the “instructor” during the experiment. 6. Students not on the lead team for a given experiment submit individual or group reports while the lead team submits a team report with an “Observations” section replacing the “Results”. 7. During the last third of the semester, a second round of “student designed” labs is conducted during which the lead team for each lab is responsible for specifying the experiment to be performed including all details. The course instructor acts as an advisor for these labs, assisting the lead team in choosing a topic and in the lab design. In the first round of student led lab exercises, although the labs are noted to be “instructor specified”, the specifications given are not complete. The lead team is given broad instructions as to the content of the

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lab but not all details. As an example, the “instructor specified” instructions could be “Use the wind tunnel to measure drag characteristics of various bodies and compare them to standard coefficient of drag values from the textbook”. Thus, the lead team must decide a number of specifics regarding the lab, which bodies to study, at how many and what speeds to test the bodies, how to derive the coefficient from the wind tunnel measurements, etc. For the last round of student design labs, the teams are given only a choice of equipment. Once they chose which device they would like to utilize for their lab, all other aspects are completely in the hands of the lead team. It should be noted that the instructor does not abandon the lead team during this planning period, but every attempt is made to allow the student team to plan the lab with minimal input from the instructor. The goal of this process is to move the students from a lower level of understanding of how to conduct experiments and take data to a higher level of involvement in which they look at a physical system or phenomena and devise an experiment to measure some aspect of that system. This process not only helps the student to develop some sense of experiment design, but also reinforces their understanding of the underlying fluid mechanics concepts involved in the labs, especially those labs that they lead.

Implementation Implementation of the method has produced many of the desired results. Students are more attentive and have a higher level of involvement in the actual lab exercises than was previously observed. From observation, it appears that fewer students are “along for the ride” and a higher proportion actively participate in the lab experience both as “lead” and regular team members. While there is still difficulty in measuring the extent of such, students do exhibit greater understanding of experiment design. However, as is the case with most such changes, there have been some negative experiences as well.

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One negative aspect of the new methodology that was noted in the earliest offerings is the quality of pre-lab instruction and discussion from the lead teams. In the first two semesters of implementation, the author observed that the pre-lab briefings tended to be very minimal, consisting, often, of a few simple words regarding the topic to be investigated followed by short instructions on what measurements were to be taken and a summary of data reduction equations that would be needed. Many times the lead teams gave very little information regarding the relationship of the planned measurements and the phenomena being investigated nor were any reasons given for the seemingly arbitrary number of trials or data sets to be taken. This problem was at least partially addressed, beginning during the second semester and extending to the present, by requiring that the lead teams meet with the course instructor prior to the lab session to preview and discuss their pre-lab briefing. This has resulted in a more robust prelab but the tendency to minimalize, thus leaving out valuable information, is still one that has to be guarded against. Another negative aspect of this methodology has been a slightly increased time commitment, both on the part of the students and the instructor. This is primarily due to the meetings to review teams’ pre-lab discussions. During the time in which the author taught this lab, these were scheduled for an afternoon during the week leading up to each lab. Through better organization and planning, the current instructor of the lab has incorporated these meetings into the regularly scheduled lab time thus greatly reducing any additional time commitment due to implementation of the “lead team” process.

Student Outcomes Assessment As noted earlier, one of the driving forces in devising and implementing the described method for conducting the Mechanics of Fluids Lab was to address the ABET required student outcome related to design of experiments. This outcome has been re-stated in the current

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ABET criteria in student outcome 6 under Criterion 3 (ABET, 2018) which states that graduates of the program must have “an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgement to draw conclusions”3. The methodology described does address the student’s ability to both develop and conduct appropriate experimentation. The use of lead groups to plan and prepare experiments conducted by the other students in the lab is a clear instance of developing and conducting experimentation. The remaining portions of outcome 6 may also be assessed within this lab class through the lab reports required from the non-lead teams/members. Student outcome 5 states that graduates of the program must have “an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives”3. While senior capstone projects have historically been one of the primary places where outcomes related to teamwork have been assessed, the use of teams in the lab setting opens up additional opportunities. In particular, the “lead team” methodology described here would seem to be an excellent source for assessment data for the last three terms in outcome 5.

Student Response The methodology described here has been in use in the fluids lab class for the past several semesters by three different instructors. While each instructor invariably puts his/her own “spin” on the process, the basic methodology has remained the same. The three instructors that have used this method are in the process of more fully documenting the method and processes used so that any new instructors that are assigned the class can more easily follow it. Student response to the lab class has been mostly positive. Students taking the lab during the first two semesters of implementation were quizzed at the end of the semester on their perspectives on the method. These results were mostly positive with the lowest ratings given to the

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student-led pre-lab briefings. As noted above, later adjustments to the process seemed to somewhat alleviate these concerns. Subsequent classes have rated the class highly with few negative comments in the university’s regular course assessment process. As noted earlier, students seem to be more attentive and involved in the class and the lead teams, in general, spend more time working on their lab than they typically would for a traditional instructor presented lab exercise. Pre-lab briefings have improved with increased instructor involvement and the number of experiments conducted in a given semester has remained basically the same as when all of the labs were instructor presented. Overall, those involved consider the change to this methodology to be an improvement over the previous method and one that has succeeded in increasing the student’s awareness and training in lab development and involvement in their lab teams.

THERMODYNAMICS CLASS The second example of using a team project to help assess multiple student outcomes is in the thermodynamics sequence. The department offers thermodynamics as a two-semester sequence with Thermodynamics I appearing in the fifth semester and Thermodynamics II in the seventh semester of the projected schedule. While team projects with presentations are used in both thermodynamics courses, the emphasis here will be on their use in Thermodynamics II which covers exergy analysis, gas power systems, thermodynamics relations, non-reacting mixtures, psychrometrics, and combustion. In Thermodynamics II a single team project is required. This project is assigned close to mid-term and student teams present their solutions at the end of the semester. Teams chose project topics from a list provided by the instructor. At the assigned finals period for the class, the teams make oral presentations to the class of their project solution. These presentations typically involve a PowerPoint or similar presentation, but are not required to do so. The project grade is based on five categories: the overall

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presentation (primarily, the visual portion of the presentation, 15%), the oral component (25%), the report “mechanics” (grammar, clarity, flow, etc., 10%), the technical content (40%) and, a “peer evaluation” component (10%). Of these categories, the oral presentation component and the peer evaluation component are individually assigned and the remaining components, comprising 65% of the project grade, are assigned as a group score. As noted, project topics are provided by the instructor which gives a level of control as to content. This helps assure that the projects contain sufficient engineering analysis and design. In addition, the project assignment requires that the presentations and reports address other considerations such as economics, safety, environmental effects, etc. An example problem statement is given in Figure 1, below. For this example problem, in addition to designing the system from a thermodynamics approach, specifying components, temperatures, pressures, etc., the team must find through their own research the local industrial electricity price, estimated costs for the components of a cogeneration system, costs for natural gas, etc. Economic calculations must be carried out to determine the monthly costs of the proposed new system. The final report is required to include the requested projected cost comparison for the existing vs. replacement system along with addressing safety, environmental and other considerations.

Figure 1. Example Project Problem Statement.

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Since the department’s curriculum does not include a required engineering economics course, one class day during the semester, normally after the project has been assigned, is devoted to a basic primer in time value of money calculations, sinking funds, etc. This primer also serves to give the students some exposure to these type calculations before they take the FE exam. The author has included a similar team project for many years, dating back to the earliest offering of Thermodynamics II on campus in the Fall of 1994. For most of that time, the project has been used to provide assessment measures for several student outcomes. Under the ABET a-k outcomes list, the project was used to assess student’s ability to complete a system design including environmental and economic aspects, to incorporate pertinent independent information into engineering solutions, and to orally communicate project results. Other outcomes could have been identified to assess through the project, but the above list are the outcomes that were designated to be measured in the course. Obviously, the teamwork aspects of the project could have also been added to the program’s assessment matrix, but these were covered in other classes and the decision was made to not utilize this class/project to assess that outcome. In the author’s experience with using this project, several benefits and drawbacks not related to the present topic have been observed. The project is usually seen as a positive part of the class by students. They appreciate the opportunity to participate in a creative exercise that is not just another rote problem solution. Most students tend to view the requirement for the oral presentation as less onerous than a course final exam. From an instructor’s point of view, the author has also appreciated the smaller time commitment for grading the project reports as opposed to a final exam. More importantly than providing a means to assess various student outcomes, such a team project allows the students to gain experience in working in a team setting to address an engineering project at least somewhat akin to what they may experience once in industry. While the monitoring and evaluation of individual effort within the team is very likely at a much reduced level from what would be common in the

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workplace, the experience is, nonetheless, valuable and may well be many student’s first exposure to a nearly full-fledged engineering project. Along with benefits, there are, of course, drawbacks, both present and potential, in undertaking such projects. For a number of students, the primary negative aspect of the group project is the oral presentation. Some students, and especially those for whom English is not their first language, are quite apprehensive about speaking in front of a group even though the group in question is their fellow students and the course instructor. Also, as is all too common with group projects, there invariably is at least one group that has a member who never met or communicated with the group during the semester and only rejoins the group for the presentation. The peer review component of the grading distribution was added to help account for this. However, the percentage of the overall grade attributed to this component is probably on the low side of what is appropriate for the “teamwork” grade. Unfortunately, the problem of uneven effort seems unavoidable but can also be a learning experience for the students.

Student Outcomes Assessment As can be seen from the required contents of the project report/presentation, there are several student outcomes that could be assessed from this team project. Using the recently implemented ABET 17 list of required outcomes, the following list of possible student outcomes would appear to be possible to assess using the results of the team project: 

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Outcome 1 – the solution to the project statement clearly fits the definition of a “complex engineering problem” and the format of the project involves both formulating and solving this problem. Outcome 2 – the project solution is achieved by applying engineering design to produce a solution meeting a specified need and the team is asked to address at least safety, environmental and economic factors.

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Outcome 3 – the presentation can be assessed for the ability to communicate effectively Outcome 4 – while perhaps not as good a fit as several of the other outcomes, the project solution may give the teams the opportunity to address professional responsibilities in their solution while addressing environmental and economic, and possibly, societal contexts Outcome 5 – the team project obviously gives an opportunity to assess the ability to function effectively on a team. Additional requirements could be added (team blogs, for instance) that would assist in addressing particular aspects of this outcome Outcome 7 – since the project statements do not contain all of the information needed to complete the project, the student teams must “acquire and apply” new knowledge to come to their solution.

Thus, this one team exercise presents possibilities for assessment measures linked to five or six of the required seven student outcomes.

Team Formation One of the more diverse aspects of using team projects in courses is the procedure for selecting/assigning teams. In the author’s experience, a variety of methods have been used from allowing the students free choice to assigning teams based on a variety of student attributes. Two different methods are used in the example courses discussed in this chapter. In the Fluids Lab course, students are allowed to form their own teams with the only limiting factor typically being the size of the teams. In the Thermodynamics II class, students still chose their teammates, but in a slightly different manner. During one class period close to mid-term, a number of students, corresponding to the number of groups that are to be formed, are randomly chosen, usually by using the last digit of their student ID numbers. These students then conduct a “draft” of the remaining students in the class taking turns to pick team members. While

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this method definitely does not insure any desired diversity among the groups, it is normally seen by the students as a “fun” exercise. As an aside, this method has also led to an observation by the author that most students know a surprisingly small proportion of their classmates. By the third or fourth round of the draft, most of the students doing the drafting are forced to ask students for names, choose by pointing, or simply ask who wants to be on their team. This is always a surprising development as, due to the number of students in the program and limited number of multiple section courses, they have almost assuredly had other classes with almost everyone in the present class. Other methods that the author has used for group formation have included forming groups based on a target common average score on the first exam in a class (used in Thermodynamics I for the group projects in that course), forming teams based on a targeted average overall GPA, or forming teams to insure the highest level of gender and ethnic diversity among groups as possible. The draft method used here does tend to result in lower ethnic and, to a lesser extent, gender diversity among the teams, but with the selections coming in turns, most teams do have students who have not previously worked together on them which is its own form of diversity that is often not seen if self-selected groups are used exclusively.

CONCLUSION While the use of student teams in lab courses or on term projects in other courses is commonly used, it is hoped that by the examples given here that others can better utilize those teams in their program and course assessment activities. Both of the examples given here were developed with the goal of better preparing students and meeting expected student outcomes. In the author’s, and his colleague’s, estimation, these goals are being met. The Fluids Lab has become a much more interactive course and student participation and interest seems clearly higher. While less formal evaluation of the project, separate from the course as a whole, has been undertaken in the Thermodynamics class, anecdotal evidence from

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graduating students and alumni points to the project being a positive experience. There is no doubt that other faculty at other institutions have developed similar class methodologies. Through this publication, it is hoped that these faculty may find some new aspects of teamwork to incorporate into their own efforts to improve them further.

REFERENCES ABET. 2018. Criteria for Accrediting Engineering Programs. Available at: http://www.abet.org/wp-content/uploads/2018/11/E001-19-20EAC-Criteria-11-24-18.pdf. Krohn, J. L. 2013. Design of Experiments: Student Response to an Experiential Learning Approach. Proceedings of the 2013 ASEE Midwest Section Conference. Servoss, S. L. and Clausen, E. C. 2012. Incorporating Inquiry-Based Projects into the Early Lab Experience. Proceedings of the 2012 ASEE Midwest Section Conference.

In: Mechanical Engineering Education … ISBN: 978-1-53617-791-6 Editor: Charles E. Baukal, Jr. © 2020 Nova Science Publishers, Inc.

Chapter 5

APPLYING TEAM-BASED ACTIVE LEARNING TO EXPOSE STUDENTS TO THE AEROSPACE DESIGN PROCESS AND INDUSTRY PRACTICES Kenneth W. Van Treuren Baylor University, Waco, TX, US

Keywords: active learning, team-based activities, aerospace education

INTRODUCTION Aerospace continues to be a popular industry for engineering graduates. Aerospace and Defense (A&D) industries in the United States are expecting to hire over 86,000 people in 2019, a 30% increase over what was initially projected. With the continued increase in retirements of employees over 60, A&D is a great job market for graduating engineers. Surveys show among university students that 48% of female, 55% of black, 49% of Latino and 70% of white students are considering a career

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path in A&D. Students aspiring to enter this field are attracted because of their interest in aircraft/defense/space, the technological challenge, and the opportunity for advancement. More is being done to attract women and minorities. A&D companies also have a strong STEM outreach in the elementary, middle, and high schools to address workforce needs. The jobs will be there for interested students who choose this career path (Hedden and Sands 2019). Aerospace is an important industry for Baylor University and the local community. There are over 30 aviation related business in Waco, TX and three airports. The three higher education institutions, Baylor University, Texas State Technical College and McClennan Community College, have education opportunities in aerospace engineering, aircraft maintenance and airport management. They provide a pipeline of talented workers from the local area for this industry. The Greater Waco Aviation Alliance, connected to the Waco Chamber of Commerce, coordinates activities with these companies and schools, providing scholarships to students who are pursuing degrees in aviation/aerospace or related engineering programs at the three schools. The community is behind local aerospace students who will help build the local workforce. There has always been a strong interest among Baylor students to work in the aerospace industry. Many Baylor graduates already work for such companies as Bell, Boeing, Delta G (Waco), Department of Defense, GE Aviation, Honeywell, Honda Jet, Lockheed Martin (Ft Worth), Bell Helicopter (Ft Worth), L-3 Communications (Waco), NASA, NAVAIR, Pratt & Whitney, Raytheon, Solar Turbines (Dallas), Spirit Aerosystems, and SpaceX (McGregor). To prepare Baylor students for the aerospace industry, elective courses such as Analysis and Design of Propulsion Systems (2001) and Introduction to Aeronautics (2007) were introduced. These courses initially used a traditional lecture style with assessments based on standard assessment tools such as homework, exams, laboratories, design projects, midsemester exams and a comprehensive final exam. Over the years, pedagogical styles in engineering have changed to include more active learning in the classroom. In light of these developments, the goal of this effort was to update the two courses with

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new approaches to that would engage the student, help them learn the material more thoroughly, and enjoy themselves while doing it (Van Treuren 2018a, Van Treuren 2018b, and Van Treuren 2019). A topic of importance to a student’s success in the aerospace industry is their engineering knowledge and skill to operate in an aerospace company environment. At the same time, in order to be successful in any company setting, students should know how a company operates and how to be an effective team member. The Boeing Company had such a program for faculty, The Boeing Welliver Faculty Fellowship, which enabled a “summer internship” with The Boeing Company to gain insight into Boeing’s operation. The author participated in the program in 2009 and found it extremely beneficial. Experiences gained in this program, which Boeing eventually canceled after 2009, provided the foundation for some of the changes incorporated into these courses. There were two main objectives of the program: 1. To provide faculty with a better understanding of the practical industry application of engineering, information technology and business skills, to influence the content of undergraduate education in ways that will better prepare tomorrow's graduates for careers in a global industrial environment. Program participants are asked to apply this increased knowledge and understanding within their courses and disciplines and too influence broader curriculum changes in a holistic way that addresses the needs of the practitioner. 2. To have faculty observe the Boeing environment, process, and procedures with “fresh sets of eyes.” Faculty will bring their expertise to bear by documenting what works well at Boeing and what they would recommend for improvements. Faculty in all engineering, information technology and business disciplines are encouraged to apply (Boeing Welliver Announcement 2006). In addition to updating teaching methods in the courses, it was also desired to introduce the students to the aerospace industry operation and

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have them develop team skills and creativity using design projects. The framework for change came as a result of attending a Kern Entrepreneurial Engineering Network (KEEN) Innovating Curriculum with Entrepreneurial (ICE) Mindset Workshop. Since 2007 Baylor University has been involved with KEEN. KEEN is “a national partnership of universities with the shared mission to graduate engineers with an entrepreneurial mindset so they can create personal, economic, and societal value through a lifetime of meaningful work.” (KEEN 2019) This is accomplished through incorporating entrepreneurially minded learning (EML) into the classroom by instilling curiosity, connections, and creating value (three Cs) in the students. This approach to educating engineering students is gaining acceptance in the classroom (Kirkpatrick et al. 2016, Seery et al. 2010, Kline et al. 2018, and Grigg 2018). The result is more opportunities for students to practice innovation and creativity through several motivating, stimulating activities. The three Cs have been purposely incorporated into several courses in Baylor University’s engineering curriculum to challenge the students to be more creative and innovative as they approach problem solving. What results is a mindset and skillset which prepares Baylor students to be competitive in the workplace, a goal of any engineering program. Feedback from Baylor industrial advisory boards highlighted that engineering students may have great technical skills, however, their knowledge of a business and how it operates was identified as lacking. Making students more aware of challenges in the workplace was a primary motivation to intentionally modify the two elective course projects to reflect a company setting for both the gas turbine engine and lightweight utility fighter (LUF) design process. The ICE Workshop was a three day, hands-on workshop addressing active and collaborate learning (ACL) as well as problem/project based learning (PBL) (ICE Workshop 2018). The workshop, run by Lawrence Technological University and sponsored by KEEN, helped faculty understand the fundamental pedagogical techniques of EML, ACL and PBL. Faculty attending the workshop:

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Experience firsthand examples of EML, ACL, and PBL pedagogical techniques as “students” in the workshop Experience examples of entrepreneurial mindset course integration Analyze unfamiliar situations and open-ended problems using various methods to define the “true” problem statements Interact as part of an interdisciplinary team with members from multiple institutions and backgrounds.

The workshop had 23 faculty representing different engineering disciplines from 11 different universities. While the author has over 28 years of experience in ABET accredited engineering programs, there were many new pedagogical techniques that the workshop participants experienced in individual and group activities over the three days. One activity that was the most interesting to the author was the Jigsaw Technique which was eventually included in the Introduction to Aeronautics course. ICE participants were paid a stipend to attend and deliverables were expected. Deliverables from the workshop included developing two ACL or PBL modules that could be incorporated into an existing class. The workshop facilitators followed up with periodic teleconferences to give feedback on EML ideas and to hold the participants accountable for actually implementing the modules in a class. Comprehensive Assessment of Team Member Effectiveness (CATME) was used to determine team composition for both classes based on instructor weighted criteria (Furgeson 2018, Layton et al. 2007, and Pung and Farris 2011). CATME is used by over 1,100,000 students of 17,000 instructors at 2200 institutions in 85 countries (Furgeson 2018). The software has two main functions. The first is called Team-Maker. The CATME website describes this feature: “Team composition affects the success of individuals and teams in cooperative learning and project-based team environments. Using appropriate criteria when assigning students to teams should result in an improved learning experience. Team-Maker was created to make the team-assignment process simple, even when using a complicated set of

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Criteria used for team selection can include: Schedule Grade-Point Average (GPA) Software skills Sub-discipline Hands-on skills Leadership preferences Commitment level Sports

Gender Race/Ethnicity Prerequisite courses Discipline Writing skills Shop skills Big-picture/details Fraternity/Sorority Instructor Added.

The instructor can select and weight the criteria that are important to the course and teams are automatically assigned using the instructorimposed criterion. Using this software greatly reduced instructor workload. An additional benefit to using CATME is that students learn how to be better team members using this software as it included the capability for peer evaluations, and more recently, anonymous comments back to other team members. This is the second major component and a description of the peer evaluation is highlighted below. When using teams in education, instructors often use peer evaluations and self-evaluations to assess how effectively each team member contributes to the team. The Comprehensive Assessment of Team Member Effectiveness (CATME) was developed for this purpose. This web-based instrument collects data on team-member effectiveness in five areas that research has shown to be important: 1. 2. 3. 4. 5.

Contributing to the Team’s Work Interacting with Teammates Keeping the Team on Track Expecting Quality Having Relevant KSAs (Knowledge, Skills, and Abilities) (Furgeson 2018).

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CATME peer evaluation was used in determining peer/instructor evaluation points for the final course grade. These points constituted half a letter grade. The software was able to track and assign values to each team member. Students received valuable anonymous feedback on their team performance from their classmates. The undergraduate program is an excellent opportunity for students to discover how they interact with others in a team environment before they join the aerospace industry. Thus, the two elective courses, “Analysis and Design of Propulsion Systems” and “Introduction to Aeronautics” were restructured to apply team based active learning to expose students to the aerospace design process and industry practices.

5.1 ANALYSIS AND DESIGN OF PROPULSION SYSTEMS This course, Analysis and Design of Propulsion Systems, is an elective course for the B. S. in Mechanical Engineering degree at Baylor. The course is for seniors who have previously taken Advanced Thermodynamics (cycle analysis) and is typically taught in the fall semester. It meets two days a week for 29 lessons. Each lesson is one hour and 15 minutes in length. The main emphasis of the course is for the students to design, as a team, a turbofan engine cycle for a designated aircraft, this offering the B-52H. Previous semesters have included designing a gas turbine cycle for a ground attack aircraft and a High Altitude Long Endurance (HALE) Unmanned Aerial System (UAS) (Van Treuren and McClain 2010). The B-52H re-engining was chosen as the design project as it is a real world engineering challenge that has recently been in the news (Insinna 2017 and Greco 2017). Figure 1 shows the current engine/nacelle on the B52H. Figure 2 displays a typical engine cutaway for a high bypass turbofan engine. The three cycle design choices are the overall compressor pressure ratio, OPR, and the fan pressure ratio, FPR, and the bypass ratio, ALPHA. The OPR is the air pressure rise occurring from the inlet to the fan compared with the exit air pressure of the compressor. The FPR is the air

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pressure rise across the fan. ALPHA is the ratio of air bypassing around the center core of the engine to the air passing through the center core of the engine. Students were formed into teams of four. The teams remained the same throughout the semester and sat together in the classroom to accomplish Think-Pair-Share exercises (Stanford University 2018 and Canino 2015) or example problems with their team.

Figure 1. B-52H Engines and Nacelle (Practical Aeronautics 2019).

Figure 2. High Bypass Ratio Turbofan Engine (The Engineer 2012).

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5.1.1 The First Day In an effort to keep students engaged in the learning process, active learning was used throughout the course. Active learning is a form of learning that seeks to involve the students in the learning process more directly than other more traditional methods, such as lecture. Teams were selected using the CATME software prior to the first day of class. This is challenging as students needed to enter their requested data into the CATME program several days prior to the first day of class so teams can be assigned. In addition, drops or adds can complicate how the teams were assigned, however, CATME is flexible enough to allow manual drops or adds to the teams. An example of active learning was the module presented on the first day of class. No preparation was required for the first day of class which caused some concern for high achieving students. On the first day of class students, sitting with their teams, addressed the question “What do I need to know to design a jet engine?” In a short ten minute session, called a Quick Think (an extended Think-Pair-Share), teams listed all the topics that might be important in the design of a gas turbine engine without accessing any outside references. At the end of the ten minute session, the teams shared some of their findings with the rest of the class. The lists of topics were collected, collated, and presented to the class on the next lesson. Many of the topics proposed by the students were incorporated into the course syllabus, which was also provided to the students by the next lesson. The homework assignment after the first class was to write a persuasive/position paper, either for or against replacing the engines on the B-52H, supported by research. When the papers were turned in two lessons later, some teams supported the United States Air Force (USAF) reengining project and some surprisingly were against. All defended their positions with documentation. The position paper gave the students a chance to learn about the aircraft, its current engines, and mission capabilities. Through this exercise, the students became very familiar with the details surrounding the re-engining project.

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Figure 3. Examples of student team names, logos, and mission statements.

As part of writing this position paper, the teams were to pick a team name, a logo, and a mission statement (Figure 3). Examples from existing engine and aerospace companies were shown to the students on lesson one to help them frame the task.

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These items aid the public when identifying commercial companies. Student selection of a team name, logo, and mission statement provided a team “branding” which would unify the student teams throughout the semester. Each teams became a “company” in competition with the other teams.

5.1.2 The Request for Proposal Early in the semester, the students were exposed to the concepts of creativity/innovation and the essentials of writing a Request for Proposal (RFP). Previously, as occurs in most design classes, the students would be given an RFP listing the design constraints for the project. There was no appreciation or questioning by the students about who wrote the RFP (the instructor) or how the constraints became part of the proposal. It was thought an exercise in writing an RFP would give the students an appreciation for the difficulty in defining the design scenario for any product. Working in teams for the first lesson, students defined creativity and innovation and how the two were related. Typical definitions are below: 

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Creativity - Ability to produce something new through imaginative skill, whether a new solution to a problem, a new method or device, or a new artistic object or form. The term generally refers to a richness of ideas and originality of thinking. … Studies also show that intelligence has little correlation with creativity; thus, a highly intelligent person may not be very creative (Answers 2018). Innovation - the multi-stage process whereby organizations transform ideas into new/improved products, services or processes, in order to advance, compete and differentiate themselves successfully in their marketplace (Baregheh, Rowley, and Sambrook 2009).

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The next question given the teams was “What is the difference between innovation and creativity?” Amabile et al. (1966) suggests: “All innovation begins with creative ideas ... We define innovation as the successful implementation of creative ideas within an organization. In this view, creativity by individuals and teams is a starting point for innovation; the first is necessary but not sufficient condition for the second.”

Creativity brainstorming exercises were accomplished in class, the first being how to improve on a basic bicycle design. Students came up with the usual suggestions, to add gears and even a motor. After the students presented their findings, the students were challenged with thinking differently about bicycles. The topics of form and function came up as well as considering bicycles as art (see Figure 4). Their homework assignment was a brainstorming session identifying new uses for gas turbine engines. The teams came up with some very creative ideas. The second lesson began with a collection of the team lists of gas turbine uses which were then given to other teams. Mixing up the lists was not shared with the class ahead of time and this caught them by surprise. The teams were to evaluate the other team’s ideas, identifying the best idea (most possible) from the list as well as the most outrageous. This exercise let students see the work of other teams and make a value judgement on what they received. The subject matter for this class focused on introducing the concept of an RFP. A case study on the KC-X (KC-46) was introduced and the students discussed how poorly this RFP was managed. The point was made that each time the RFP was rewritten and rereleased, more time elapsed and the procurement of the system was delayed yet again. The different sections of a typical RFP were discussed and the class ended with a homework assignment having the student teams write an RFP for replacing the engines on the B-52H. The RFP needed to be specific enough to provide guidance to a company considering entry into the RFP competition yet at the same time not be so specific as to limit possible solutions. A new engine was desired which meant that new technologies needed to be addressed. This exercise helped prepare the student teams to

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understand the origin of the RFP and its purpose. The homework assignment again required the teams to examine the current B-52H aircraft mission capabilities such as range, endurance, altitude, and speed and modify them appropriately assuming the impact that a new, improved engine cycle would have on the aircraft’s performance. Several typical RFP formats were suggested for the report with the teams choosing what would best suit their needs. The proposals by the teams were evaluated by the professor and some of the student concepts were incorporated into the actual design project RFP used for the course.

Figure 4. Bicycles as art.

5.1.3 Design Project The companies (student teams) were eventually given an RFP with new mission specifications for the B-52H which needed to be satisfied. While the project was introduced in class, there were still topics that needed further explanation and research. The basic premise of the project was to determine the drag of the airplane under cruise and loiter conditions which would then determine the amount of installed thrust the engine would need to produce. Revisions were accomplished to determine engine

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performance not installed on the aircraft, as this uninstalled performance is the figure of merit engine companies use for design. While an RFP listing the desired performance specifications for the B-52H, along with a drag polar showing the relationship between lift and drag, was supplied to the companies, the mission had unrealistic expectations for the engine performance. Specifically, the range required for the mission would lead to a specific fuel consumption that would be too low for the cruise attitudes given in the RFP. At a point in the design process it was anticipated that the students would come to this realization in the next phase and then question the RFP as the project progressed. Negotiations with the customer (professor) would ensue with each team proposing modifications to the RFP constraints. The actual design project covered the latter two thirds of the course and served as a means to understand gas turbine operation and the engine design process. The design project was broken into three parts with written reports required for each. The final report was a formal compilation of all three phases along with a final presentation. A more detailed description of the design process, to include technical descriptions and equations, can be found in Van Treuren and McClain (2010) which describes cycle design for a HALE UAS using a similar process.

5.1.3.1 Design Project I – Mission Analysis Design Project I, Mission Analysis, effectively studied the RFP mission. The mission was modeled in a spreadsheet as seen in Figure 5. Fuel used in each leg was either specified in the RFP or found using standard aeronautical calculations for straight and level, unaccelerated flight which resulted in calculations of drag based on aircraft weight at the beginning of each leg. The end result of this phase led to the determination of the important uninstalled figures of merit for the engine design: the specific fuel consumption, S, thrust, F, and the point selected as the engine on-design point. Specific fuel consumption is the amount of fuel burned per pound of

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thrust produced. Normalizing the fuel burn with the thrust gives a figure of merit that could be compared between engines. Specific fuel consumption is analogous to the “miles per gallon” used when comparing automobile engines, although in the case of automobile engines fuel is used to normalize the miles driven so different cars can be compared. The number found for specific fuel consumption in the spreadsheet that satisfied the required fuel reserve was an average value for all cruise and loiter legs and needed to be low enough to complete the mission using the calculated aircraft drag, which corresponds to the thrust required by the aircraft in straight and level, unaccelerated flight. The overall thrust required for each mission leg needed to be calculated and then divided by the number of engines, eight for the B-52H, to determine the amount of thrust required by an individual engine over the various mission legs. The aircraft was initially to climb to 43,000 ft, fly for 4,000 nm, loiter for 4.7 hours, deploy munitions, climb to 50,000 ft and return 4,000 nm to base, ending the mission with a 20% fuel reserve. The students completed a spreadsheet which calculated aircraft performance and used an optimization function to determine an average specific fuel consumption necessary for the RFP mission which would allow the aircraft to land with the proper fuel reserve. Included in the analysis was an estimate for installation losses supplied by the RFP. Again, the companies were actually given an impossible scenario in the RFP (range too long and loiter time too high) which resulted in an average S that was unrealistic for the mission, however, at this point in the design process the students were unaware of the challenges facing them. The average S found with the spreadsheet would eventually not allow mission accomplishment. The companies were also to determine the appropriate design point for the engine. This design point usually is the altitude and airspeed where the aircraft will be operating for extended periods of time and where the engine will need to be very fuel efficient. For this mission it was determined to be the cruise out condition where the aircraft was the heaviest and would burn the most fuel.

Figure 5. Mission Analysis spreadsheet.

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The companies were also required to research current values of compressor pressure ratio, fan pressure ratio, and bypass ratio and to determine the trends with time that could be found in the literature. Knowing typical values of today’s engines could be used to predict future values of the technology. With the average S required to accomplish the mission and the design point identified, the companies were ready for the next phase, Parametric Cycle Analysis (PCA).

5.1.3.2 Design Project II – Parametric Cycle Analysis With the information from the Mission Analysis, Design Project II had the students accomplish an on-design Parametric Cycle Analysis looking at many different engines (combinations of OPR, FPR, and ALPHA) to see which combinations would satisfy mission requirements. The software for this analysis, as well as the subsequent off-design analysis software for Design Project III, is supplied by Mattingly and Boyer’s Elements of Propulsion textbook (2016). Component efficiencies and all required component pressure ratios except the OPR and FPR were supplied to the companies. Knowing the required average S from Mission Analysis, Carpet Plots were accomplished plotting Uninstalled S (TSFC in the plot) vs. Specific Thrust for various combinations of compressor pressure ratio, bypass ratio and fan pressure ratio (see Figure 6). The specific thrust is the amount of thrust produced by the engine divided by the airflow through the engine. This number should be as large as possible which means the frontal area (diameter) of the engine would be smaller and produce less drag. Uninstalled specific fuel consumption, S, should be as low as possible to be fuel efficient. Higher ALPHA values mean the engine is more propulsively efficient which results in a lower fuel burn. An engine could be any combination of these values; however, only certain combinations will satisfy the mission requirements and tradeoffs must be realized for optimization.

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Figure 6. Carpet Plot of TSFC vs Specific Thrust.

Each point on the plot represents an engine cycle that will operate but may not satisfy the average S requirement. The RFP given the students was designed to be impossible to accomplish because the required average S with the initial mission constraints was lower than found on the carpet plots. Once the companies realized the original average S was impossible to achieve, each company had to negotiate with the customer (the professor) to relax some of the RFP requirements. This resulted in reductions in range, loiter time, speed, altitude, and payload. In essence, each team chose different combinations of mission requirements which had to eventually be justified in the final presentation by stating how the changes impacted mission capability. Companies had to examine tradeoffs between engine component values to decide on a final compressor pressure ratio, bypass ratio, and fan pressure ratio that delivered an appropriate average S at the design point (altitude and airspeed). In the report for Design Project II, students were to explain the tradeoffs between S and

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Specific Thrust. They were to physically explain the results of increasing compressor pressure ratio, bypass ratio, and fan pressure ratio on the exit velocity from both the core of the engine and the bypass. These values directly impacted both propulsive and thermal efficiency of the engine. A high overall efficiency would translate to a more fuel efficient engine.

5.1.3.3 Design Project III – Engine Performance Analysis With the engine design choices, the engine was then sized and flown off-design in Design Project III. Engine Performance Analysis (EPA) determined if the chosen engine combination of design parameters could satisfy the mission. The purpose of EPA is to test one engine over all the mission legs to check acceptability. The engine had to be sized to specify the massflow rate and to insure that there was enough thrust to operate at takeoff, on the cruise legs, and during loiter. The mission point for sizing is typically the takeoff condition for most high bypass engines however, for this case, the high cruise out became the critical leg as this engine was intentionally throttled back to a reasonable turbine inlet temperature for continuous operation. Some mission legs were throttled back even more to match thrust required for the drag produced. After sizing, running the engine off-design enabled the specific S values to be found for each leg. These S values were then placed back into the Mission Analysis Spreadsheet in place of the average S previously found. Now, with the actual S for each leg, a more realistic calculation of the reserve fuel was possible. In addition, knowing the massflow rate through the engine at each leg enabled a calculation of the additive drag induced by the streamtube for not flying on-design. Algorithms were available to determine engine weight based on component selection and, from the weight, a cost estimate could be accomplished (Brown et al. 2013 and Byerley et al. 2013). After verification of the chosen engine, a sensitivity analysis was performed, not unlike what is done in a company design process, to determine if any variation of the chosen components around the design choices would result

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in better performance. The sensitivity analysis basically involved holding constant the chosen fan pressure ratio and then slightly varying the bypass ratio and the compressor pressure ratio and running an off-design analysis for each “new” engine. In all, the original chosen engine and five modified engines were examined to see if there would be a benefit to slightly modifying the original design choices. The comparison was made based on fuel remaining at the end of the mission as seen in Figure 7. A formal report encompassing all three design phases was accomplished using a report format following ASME standards. To finish the process, each company presented their engine to the class as if they were trying to sell the engine to a customer, the professor. While all engines satisfied the mission requirements, the customer picked an overall winner based on the information presented (i.e., engine component design choices, performance, weight, cost, and mission capability). A prize was given to each member of the winning team.

Figure 7. Sensitivity analysis.

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5.1.4 Assessment As part of the course assessment, exams were given and homework graded. Students had to demonstrate a knowledge of the analysis tools used in the software, namely the equations used in the mission analysis and the on- and off-design cycle analysis. A comprehensive final exam was also given which included calculations as well as a conceptual understanding of the Design Project. Exams and homework accounted for approximately 60% of the course. The team activities, such as the persuasive paper, writing an RFP, and the Design Project accounted for the remaining 40%. A survey was given to two sections of the class, fall 2017 with 19 students and fall 2018 with 14 students. A Likert scale was used with 1 being “disagree” and 5 being “agree.” Table 1 contains scores to the general questions about the course. Table 2 focused on the first two lessons of the course. Table 3 addressed the Design Project. The scores reported in Tables 1, 2, and 3 are a weighted average from both sections. Most student comments indicated they selected the course because of their interest in jet engines and gas turbine design. Table 1 scores were mostly between 4.0 and 5.0 which shows the majority of students felt the course met their expectations and they feel confident in their understanding of gas turbine engines. They stated the material was presented at the appropriate level. CATME was used to pick teams and to provide student peer-to-peer feedback but the students did not feel CATME was effective. Part of the disconnect lies with the author’s inexperience with the software and the lack of peer-to-peer comments for the first offering. The peer-to-peer feature was added to CATME and was used in the second offering. Students indicated that they were prepared by their Baylor education to be a good team member however, some did not enjoy working in teams. Table 2 shows the first two lessons were generally thought of as a good introduction to the gas turbine design project in the course, namely to reengine of the B-52H, a real-world scenario. They found the position paper valuable because it provided the context for replacing the engines on the B52H. It gave them the background they needed for the Design Project. The RFP lesson demonstrated how difficult it is to define a need but not to over

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specify the design with constraints which would suppress creativity. The students clearly did not appreciate determining a team name, logo, and mission statement. Being creative is often difficult and this exercise served to expose students to the importance of these items. Table 1. B-52 General Course Question a. The course met my expectations. b. I feel confident in my understanding of gas turbine engines. c. Material presented at the appropriate level. d. I enjoyed working with my team selected by CATME e. Feedback received by CATME on team dynamics was useful. f. My Baylor education has enabled me to be a good team member. g. My team functioned efficiently. h. I enjoyed working in teams i. The level of homework was appropriate for this course. j. The Design Project (I, II and III) was useful to reinforce the concepts of propulsion design. k. The field trip to TSTC to look at engines was a valuable part of the course and should be retained. l. The presentation by SpaceX was a valuable part of the course and should be retained. m. I would recommend this course to other students. n. Lectures were used appropriately in this course.

4.21 4.24 4.18 4.15 3.09 4.42 4.00 4.24 3.94 4.55 4.61 4.33 4.03 4.03

Table 2. B-52 First Day Questions a. The open ended question on design made me think about the topic more deeply. b. Finding a team name, logo, and mission statement was a valuable exercise for our team c. The position paper on the B-52 engine replacement made me think more deeply about the project. d. The first two lessons were a good introduction for the gas turbine engine and engine design.

4.24 3.58 4.00 3.80

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Table 3. B-52 Design Project Questions a. The B-52 Design Project was a good way to learn the gas turbine engine design cycle. b. Being considered a “company” in competition with other groups made me begin to appreciate how products such as gas turbine engines are developed and sold. c. I enjoyed researching about typical engine parameters and the future technologies. d. I understand the purpose of Design Project I, mission analysis and its importance to gas turbine design. . e. I understand the purpose of Design Project II, parametric cycle analysis and its importance to gas turbine design. f. I understand the purpose of Design Project III, engine performance analysis and its importance to gas turbine design g. I understand the cyclic nature of gas turbine design. h. I understand the tradeoffs associated with gas turbine engine design. i. The final presentation selecting a “winning” engine was a valuable part of the design process and reinforced the concept of company competition.

4.55 4.36

3.88 4.64 4.70 4.79 4.58 4.67 4.07

Table 3 highlights that the Design Project in its three phases. It was an effective means of teaching the design of a gas turbine engine cycle according to the students. The students indicated they clearly understood the purpose of Design Project I, II, and III. Students felt there should have been more lectures (Table 1) instead of the discovery nature (active learning) of the classroom environment although this score improved 18% in the second offering. Students’ response to whether they would recommend the course to others was low, probably due to the workload involved however, the response also improved 15% in the second offering of the course. Students did not enjoy researching typical engine parameters and future technologies. This could be because for Design Project I, where the research was accomplished, the students did not have a full and complete understanding of what to search for in the literature. The final presentation was not as enjoyable as well. Student feedback was that the presentation guidance needed more structure and they wanted this guidance well in advance of the presentation date.

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“I loved working in teams, and it really gave me a better understanding of the design process when companies are trying to win a contract.” “The design project was a simulated real-world application with real world expectations. The ability to design our own engine that could be applied to an already existing aircraft was a very cool project.” “It gave an insight into industry which I appreciated. Not many classes cover aerospace topics, granted this course is an aircraft and rocket propulsion (course), but it showed what it is like to work in industry and got me up to speed to understand what is going on in industry. The design project was the culmination of everything we were learning in class. We didn't come up with a great engine but it’s cool how companies will soon be bidding on their own B-52 replacement engine soon.” “It was a good way to go through a simplified engine design process and see what the process is like in industry. Researching current technology and anticipating future trends is also (a) useful skill to practice” “I enjoyed getting to see how companies actually go through the process to get the engines to work. Using the Para and Perf programs where great to get to see how iterations are simulated on the computer and how companies are able to consider 100s of engines and compare them in order to find the best one to fit the mission analysis.” “Working on a team was a great experience and contributed to my overall understanding of the course greatly.”

Students obviously understood the objective of the course and did enjoy addressing a real world challenge that was done in a team/company competition.

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Improvements to the course revolve around communication challenges. CATME is a valuable tool and it needs to be explained more clearly to the students so they understand the purpose of using this tool. It is also now being used in other courses so this should help with the familiarity in the future. The formal writing format for the project was not given to the students until the final report, Design Project III. Students were not assigned a format for the first two phases and that made them uncomfortable. They will be asked to write using the assigned format for all reports in the future. This is not unlike what would be required in a company or a professional society, to use a prescribed format.

5.2 INTRODUCTION TO AERONAUTICS This course, Introduction to Aeronautics, is considered an upper-level elective for the B.S. in Mechanical Engineering degree at Baylor University. Typically taught in the spring semester, the course is usually taken by seniors graduating in May. There are no prerequisites for this course and occasionally both juniors and electrical engineers are eligible to take this course. The reason for the latter, electrical engineers, is because a number of the School of ECS students are in Air Force Reserve Officer Training Corps and are heading for pilot training with the USAF. The course has homework, exams, and a Glider Design Project, all of which are accomplished individually and account for 65% of the course. The other 35% included the group Airfoil Lab and the Design Project. There were nine teams of four students per team for a total enrollment of 36. This elective class was larger than usual, with 15-20 students being optimum. The students were expected to establish a team name, logo, and a motto, much like companies in industry. Example logos, names, and mission statements were similar to those shown in Figure 3. The spirit of competition brings a real world dimension to the class as assignments were created in this context. Essentially student teams were to operate as a company and in competition with the other companies. In the course, the students design a lightweight utility fighter as a team. The concept of a

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lightweight utility fighter is currently under investigation by the USAF (Mizokami 2017). Figure 8 shows the Scorpion, one of the jet aircraft being considered to fill this need. Comprehensive Assessment of Team Member Effectiveness (CATME) was used to determine team composition based on instructor-weighted criteria. The teams remained the same throughout the semester and teams sat together in the classroom to do Think-Pair-Share exercises or example problems.

Figure 8. Scorpion Lightweight Fighter aircraft contender (Mizokami 2017).

5.2.1 The First and Second Day – Quick Think Students, based on instructor criterion, were placed in teams of four and were required to sit with their teams for the rest of the semester in an attempt to foster team spirit. For the first day of class, students did not have any homework or outside readings. The expectation was to have the students come to class without having recent prior readings to bias them concerning the exercises planned for the lesson. On the first day students learned about aircraft design, in particular, pairs of aircraft that were designed for the same mission but that are very different. Having given the students some ideas, students, in their teams and without any access to research sources (i.e., internet), addressed the overarching question “What is aircraft design?” In short sessions, called Quick Thinks (extended Think-

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Pair-Share exercise), teams addressed a series of questions. The first question, “What is design as it applies to an aircraft?,” prompted the students to develop a definition of design and then specifically how does that apply to aircraft in general. The teams were given only five minutes to come up with their interpretation and then, as a class, these answers were shared with the group. The next Quick Think had the students finish the sentence “To design an aircraft I need to know….” The purpose of this 10 minute exercise was to have them list topical areas that might be applicable to studying about aircraft design. Again, these lists were eventually shared with the group. The students were pleasantly surprised to see that they listed many topics they had already experienced in their academic coursework. Obviously, there were also advanced technology topics that were not ever previously studied. The last exercise asked the students finish “To design a lightweight utility fighter, I need to know…..” This 10 minute exercise asked students to understand the mission of a lightweight fighter and then take their previous lists and see which might be applicable for this specific application. The idea behind the three exercises was to go from the “big picture” down to the specific application. The information collected by the teams loosely formed the material in the course syllabus (which was handed out the second lesson). Homework for this first class was to write up their answers to these three questions/statements with no more than three pages. For homework, student teams were encouraged to use outside references, something that was not allowed during the first class. Student teams were also asked to select a team name, logo, and mission statement to foster team spirit and “company” competition. The second day began with a presentation and discussion about the history of the airplane, propulsions systems, and some general principles concerning the design of airplanes. After the general discussion, teams were asked, in a 10 minute time period, to generate a list of topics/technology/current issues for lightweight fighters. These items were then listed on the board and each team was asked to select a topic that they would research in depth. Ten minute presentations would be given to the class later in the semester. Some topic areas chosen were aircraft materials, ejection seats, UAV/UASs, stealth, weapons systems, hypersonics, wing

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design, countermeasures and survivability, and innovations in propulsion systems. The coverage of these topics would be on a broad level, not specifically for the lightweight utility fighter. Teams would be reporting on the latest technologies available in their topical area. These are subjects not normally covered in an introductory aeronautics course. Self-selecting a topic provides the motivation and desire to learn deeper about that topic. Presenting it to the class provides an aspect of accountability to their classmates. Homework for the teams was to develop a written report, a two page summary handout from the report, a 10 minute (maximum) PowerPoint, and four multiple choice questions which could be used on future exams. At selected times later in the semester each team made their presentation, one presentation per class period. The homework (presentation, report, summary, and exam questions) was all due on the same lesson for all teams to ensure that presentations were finished and summary sheets available when needed. These two classes introduced aeronautics in a new way that sparked additional interest in the topic.

5.2.2 Lightweight Utility Fighter Design Project The Lightweight Utility Fighter Design Project was used as a tool to learn about aircraft performance and the importance of size and weight on that performance. Students were given a RFP that included mission requirements and constraints such as fuel volume and payload. The RFP also described the use of the Jigsaw Technique and how this would impact learning the material. The student teams were asked to first do a baseline performance design using the default aircraft platform. Once the performance parameters were satisfied and the baseline aircraft validated, the student teams were then encouraged to modify the aircraft structure to reduce weight which also reduced the cost yet still satisfied the mission/performance requirements. Weight reduction was equated to lowering the costs and a portion of the project score was tied to the weight optimization. Their final score depended on how much weight was removed while still allowing the aircraft to perform its function. Again,

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each team represented a company with the goal of having the lightest utility fighter. Final designs were presented informally to the class and a winner selected, as would occur in industry.

Figure 9. Lightweight Fighter Design Spreadsheet – Design Worksheet (USAF Academy 1983)].

The main tool, which the project was built around, was a spreadsheet, originally produced by the USAF Academy for their Introduction to Aeronautics courses (USAF Academy 1983) but has been heavily modified since that time. The basic spreadsheet had a design worksheet that allowed for the selection of an airfoil and to determine the size of the wing, tail, and fuselage. The second worksheet had a list of engines containing both turbojets and turbofans. It required the students to select an engine for their aircraft based on weighted criterion. The third worksheet had a location for all the performance equations needed to satisfy the RFP for this aircraft. Other worksheets displayed the performance charts for takeoff, cruise, and loiter. A written formal report was required for each student team

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documenting and justifying their designs. A sample of the design worksheet is given in Figure 9.

5.2.3 Jigsaw 1 – Lightweight Utility Fighter Design Features The Jigsaw Technique for active learning was thought to promote team building and was adapted for this course. Gerhart and Carpenter (2016) offered this definition of the Jigsaw Technique during the ICE Workshop (2016): “Jigsaw is a cooperative learning strategy that enables each student of a “home” group to specialize in one aspect of a problem. Students meet with members from other groups who are assigned the same aspect, and after mastering the material, return to the “home” group and teach the material to their group members. Each student’s part is essential for the completion and full understanding of the final product. This technique engages all students and is very effective.”

Figure 10 displays the Jigsaw technique using a puzzle analogy. From the figure, it is easy to see how the original team is divided into experts which reunite with the original team once the material is mastered. Experts are held accountable to their original team. Team members are dependent on the other members to learn the material. The Jigsaw Technique is not new and has been used in its original form or slightly modified for decades. The Jigsaw Technique began in education when Aronson et al. introduced it in 1978. Since then, many people have used the Jigsaw technique and published about applying this in the classroom setting (Scales and Varnado 2012, Ledlow et al. 2002, Ali AlBahi 2006, McStravick and O’Malley 2007, and Golter et al. 2010). Jayaram describes using the Jigsaw technique to have expert teams develop a poster around the topic of discovering a future direction for the automotive industry (2013). Fang and Stewardson presented a modified Jigsaw technique where the instructor only meets with one person from the team and not the whole team (2007). Cheville et al. successfully applied the Jigsaw technique to a Capstone Design course (2007). This introductory aeronautics course used two

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Jigsaw exercises to challenge the students and hold them accountable for their learning. The first Jigsaw was a two-class period module in support of the Lightweight Fighter Design Project. It was desired to have the students learn about four chosen technology topics not covered in previous offerings of this course. Research in these areas stimulated student thinking about each topic and its significance to the design features of the lightweight utility fighter aircraft. The Jigsaw Technique allowed students to focus on one topic area in depth, to bring this knowledge back to their teams, and then teach their team members about their area of expertise. Teams of four meant that four topical areas were needed with each team selecting one of their team members to become an expert in that area. Topical areas for this module were: 1. 2. 3. 4.

Propulsion/fuel systems Airfoil/structure/aircraft configurations Required systems to include weapons systems, radar, etc. Stealth and future technologies that should be considered.

Figure 10. Jigsaw Technique [3].

While the topical areas sound similar to the special topic areas from the second day, the emphasis here is on how these topical areas relate to the lightweight fighter. The first class period was spent with the experts placed on teams, again, with the experts being identified ahead of class and

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expert teams assigned prior to the class. Since there were nine teams and only four expert categories, it was decided to split this into two parallel expert design teams limiting the expert team to four people, or for one of the expert teams, a team of five to accommodate the odd number of original teams. On this first lesson the expert teams researched their topical areas and then, as expert teams, prepared a one page summary of their research for presentation to their student teams for the next class period. The second class period was back with the original student team teaching their team members about the topic in which they became the experts and how this information applied to a lightweight jet fighter.

5.2.4 Jigsaw 2 – Lightweight Fighter Performance The performance section of the course previously covered the following four topics as four lecture periods. These would be topics found in any introductory aeronautics course: 1. 2. 3. 4.

Climbs and glides Range and endurance Turns and V-n diagrams Landing and takeoff.

Using the Jigsaw technique, similarly to what was outlined in the previous section, student teams selected an expert for each area and two lessons were used for the expert team members to become experts in their performance areas. The professor was available to answer questions throughout the two class periods but the learning and research on the topic was to come from the expert groups. The first lesson was to be devoted to understanding the concepts behind the performance topic. The second lesson was understanding how to use any required performance curves and their equations. As an expert group, developing example problems to illustrate these topics was encouraged. A handout on the objectives for that performance topic, possible homework problems from the textbook, and a

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list of the appropriate sections of the performance spreadsheet was provided by the experts for each topic area. Each expert area was to then develop a one page summary of important topics that would be taken back to the student teams. Solved example problems were also taken back to their teams as well. The next two lessons would be spent with each expert now teaching their performance topic to their student team. With two lessons for reporting back, that would allow half a class period per performance topic. After this module, student teams would be ready to take their expert knowledge and accomplish the performance equations worksheet in the spreadsheet.

5.2.5 Assessment The students accomplished an assessment of the course and a summary of the results follows. Twenty-two out of 36 students returned the survey. Most students indicated they selected the course because of their interest in aircraft and a desire to work in the aerospace field. The survey addressed three areas of the course: general questions on the course, the two introductory lessons, and the Jigsaw projects. Table 4 shows the general questions and the average for each question. Scale was 1-5 with 5 being the highest score meaning “Agree.” From the scores, for the most part, students were pleased with the topics and projects in the course. It was clear that students did not like CATME and the team formation. Teams were asked on the first day to sign a Team Policies and Expectations Contract to select leadership roles on the team: Coordinator, Recorder, Monitor, or Checker. Each designated role came with responsibilities. This also was not popular. For the most part, students felt the course met their expectations and they felt confident in their understanding of airplanes. They stated the material was presented at the appropriate level. CATME was used to pick teams and to provide student peer-to-peer feedback but the students did not feel that was CATME was effective. Part of the disconnect lies with the author’s inexperience with the software and the

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lack of peer-to-peer comments. The peer-to-peer feature has since been added to the current version of CATME and will be used in the future. Table 4. LUF General Questions a. The course met my expectations. b. I feel confident in my understanding of Aeronautics. c. Material presented at the appropriate level. d. I enjoyed working with my team selected by CATME e. Feedback received by CATME on team dynamics was useful. f. The contract signed at the beginning of the course provided the framework for the team to operate. g. The use of roles on the team was useful to help the team function more efficiently. h. The level of homework was appropriate for this course. i. The Airfoil Lab was useful to reinforce the concepts of lift and drag. j. The Fighter Design Project was a good approach to learn the performance of an aircraft. k. The weight optimization of the fighter was a good approach to learning what factors can influence performance. l. The Glider Design Project was a good approach to learn about stability. m. The Lockheed Martin presentation on airworthiness was useful to understand the design process. n. I would recommend this course to other students. o. Lectures were used appropriately in this course.

4.32 4.06 4.29 3.54 2.83 2.92 2.50 4.29 3.58 4.38 4.25 4.29 4.17 4.25 4.21

The introductory lessons assessment, shown in Table 5, did not reveal any strong deficiencies. The lowest rated question addressed the team name, logo, and mission statement. Students were asked to comment on what they liked about the lessons and what they did not like. Students liked the approach to introducing the course in this manner. Many said it stimulated their interest in the subject and liked researching more in depth on one topic. Several liked the team approach but not the CATME tool. Thinking about the aircraft design process early on was also a good introduction to the class. Lack of team member engagement was an issue experienced by some of the teams. Other students did not want to focus

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just on fighters but wanted to talk about other types of aircraft. Writing the papers associated with the lessons was also not enjoyable for many. Table 5. LUF Introductory Lessons a. The open-ended questions on design made me think about the topic more deeply. b. Finding a team name, logo, and mission statement was a valuable exercise for our team c. The list of special topics developed was appropriate to help our team think about lightweight fighter design. d. The special topic presentations were useful and taught us interesting aeronautical topics to incorporate in our fighter design project. e. I enjoyed researching a special topic in depth and presenting it to the class. f. The Special Topic handout was sufficient to prepare for the exam questions. g. Writing multiple choice questions that would be used on the exam was a good approach to identifying key information with our topic

3.39 2.96 3.87 3.87 3.83 3.22 3.39

Table 6. LUF Jigsaw Projects a. The Jigsaw Method was effective in learning new material. b. The Jigsaw Method kept the team members accountable to each other. c. The handouts prepared by the experts were informative and sufficient to learn the material d. I was able to use the information presented by the experts to prepare adequately for the exam. e. My group was effective in accomplishing the Fight Design Project. f. My teamwork skills improved as a result of the Fighter Design Project h. My team leadership skills improved as a result of the fighter design project.

2.92 3.04 2.88 2.46 4.00 3.58 3.67

The Jigsaw project results, shown in Table 6, reveal that the design project itself was a great learning tool and was reasonably liked by the class. The first two question about the effectiveness of the Jigsaw Technique show that the students did not think this was an effective

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method of learning. Though not sampled, this was most probably the first time that these students have ever encountered this type of team learning environment. The next two questions addressed the quality of the peer developed handouts for learning the expert material. According to the students, the handouts were not informative and sufficient to learn the material. Without acceptable material students could not prepare well for the exams. Again, the lack of the professor to fully explain the concepts of Jigsaw and the responsibility of the students for the learning process contributed to this deficiency. The last three questions definitely show that the team environment had a positive impact on the students, one of the desired outcomes. Students were asked to comment on the likes and dislikes of the Jigsaw Technique. There were many comments that said they did not like any part of the Jigsaw Technique. Others liked focusing on one topic more in depth and splitting up the workload among the team members in a prescribed manner. The team aspect of the project was liked by all.

5.3 RECOMMENDATIONS FOR IMPROVEMENT A comparison was done between current assessment values and scores from previous offerings of this course without active learning. Directly compared were scores from Exam 2, the Final Exam, and the Fighter Design Project. Scores for 2014, 2014, and 2016 were averaged and then compared with scores from 2017 using active learning. For Exam 2, scores in 2017 were lower by 6 percentage points. The final exam was 4 percentage points lower as well. The Fighter Design Project was 1 percentage point lower. It is difficult to draw conclusions with only one semester of active learning data however, these preliminary results indicate a lower performance with the Jigsaw Technique. Based on the survey there are several areas that need attention. A large part of the disconnect with the Jigsaw Technique and CATME can be related to a lack of preparation and understanding. For CATME, more emphasis/time early in the course explaining the motivation for using

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CATME and the benefit to the student is needed in the first two days. There are short training videos that can be used to improve student interaction with the CATME software and this should be required viewing prior to doing any assessment with CATME. Also, on the first two days, a better introduction to the Jigsaw Technique, to be used later in the course, is required to help the students understand how it works and why it is being used. Handouts developed by the team experts for study and the special topics presentations were not as complete or professional as they could have been. More points assigned with these items might get them more attention by the students. It was discovered that during Jigsaw 2 students spent their second two lessons filling out the performance spreadsheet instead of teaching their team members. This will have to be addressed and no spreadsheets will be filled out in class. Team member apathy was evident as this elective, for some students, is in their last semester of their senior year. This will have to be addressed and, to make it more effective, a larger point value for peer assessment should be implemented. Questions generated by the students were used on the semester exams however none were on the final exam. Students commented on this and it is reasonable to place some of these special topic questions on the final exam. In general, students liked writing the multiple choice questions especially if they would be used on the exams. The company emphasis of the fighter design project should be developed more to reflect the competition that would be experienced in industry.

5.4 CONCLUSION In conclusion, active learning modules used with a design project are effective in challenging and exciting the students about both gas turbine and aircraft design. The company context for teams better prepares students for what they will face in industry and reminds them these items are made by companies in competition. Using design projects, such as reengining the B-52H and the lightweight utility fighter, are excellent ways to capture the environment of industry competition. The initial day

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modules for both courses were an effective way to introduce the topic of gas turbine/aircraft design and to get students thinking about what that might be required. For Analysis and Design of Propulsion Systems, writing an RFP gave the students experience and understanding of the purpose of the RFP and its role in the development of new products, in this case, a gas turbine engine. The three part design project was an excellent way to have students become familiar with the engine conceptual design process, not unlike that found in industry. Having an impossible RFP forced the students to make decisions about mission changes and then negotiate with the customer for changes to the RFP, also something found in industry. Throughout the entire process, tradeoffs were made in the design requiring the student teams to make sound engineering judgements based on available data. Choosing a “winner” also reinforces the nature of competition in the business world. The special topics presentations in Introduction to Aeronautics gave each student team an opportunity to learn about a topic in depth and then be exposed to topics from the other teams. The Jigsaw Technique for the lightweight fighter design was a good exercise to have students operate in a team environment with which they were unfamiliar. There are challenges such as student motivation but these can be overcome. While the Jigsaw Technique pushed some students uncomfortably into the roles of expert and teacher, overall, the team experience was positive. Throughout the entire process for both courses, tradeoffs were made in the design requiring the student teams to make sound engineering judgements based on available data. Choosing a “winner” also reinforces the nature of competition in the business world.

REFERENCES Aeronson, E., Blaney, N., Stephan, C., Sikes, J., and Snapp, M., 1978, The Jigsaw Classroom, Sage Beverly Hills, CA, 1978.

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Ali AlBahi, K., 2006, “Development of a Design Phase Checklist for Outcome Based Active/Cooperative Learning Courses,” 2006-1883, ASEE Annual Conference and Exposition, Chicago, IL, June 18-21, 2006. Answers, 2018, www.answers.com accessed on October 29, 2018. Amabile, T., Conti, R., Coon, H., Lazenby, J., and Herron, M., 1996, “Assessing the Work Environment for Creativity,” The Academy of Management Journal, 39(5), pp. 1154-1184. Hedden, C. R., and Sands, C., 2019, 2019 Aviation Week Network Workforce Summary: Need for Tailored Recruiting as Job Requirements Boom, www.aviationweek.com/workforcereport accessed on September 28, 2019. Baregheh, A., Rowley, J., and Sambrook, S., 2009, “Towards a Multidisciplinary Definition of Innovation,” Management Decision, 47 (8), pp. 1323-1339. Boeing Welliver Announcement, 2006, https://mgt.ncsu.edu/pdfs/faculty/ BoeingWelliver%20Announcement2006.pdf accessed on September 28, 2019. Boren, B., Rahimian, S., Malone, M., and Sanchez, R., 2017, “Design Project: Re-Engine the B-52H,” Report submitted for ME 4347 Analysis and Design of Propulsion Systems, Baylor University, Waco, TX, November 14, 2017. Brown, C., Harmon, W., and Liller, T., 2013, “Gas Turbine Cost Estimation,” Paper submitted for graduate credit in fulfilment of ME 4347, Analysis and Design of Propulsion Systems. Byerley, A., Rolling, A., and Van Treuren, K., 2013, “Estimating Gas Turbine Engine Weight, Costs, and Development Time During the Preliminary Aircraft Engine Design Process,” GT2013-95778, Proceedings of ASME Turbo Expo 2013, San Antonio, TX, USA. Canino, J., 2015, “Comparing Student Performance in Thermodynamics Using the Flipped Classroom and Think-Pair-Share Pedagogies,” Paper ID # 11334, 2015 ASEE Annual Conference and Exposition, Seattle, WA, June 14-17, 2015.

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Cheville, A., Co, C., and Turner, B., 2007, Improving Team performance in a Capstone Design Course Using the Jigsaw Technique and Electronic Peer Evaluation,” AC2007-748, ASEE Annual Conference and Exposition, Honolulu, HI, June 24-27, 2007. Fang, N., and Stewardson, 2007, “Improving Engineering Laboratory Experience through Computer Simulations and Cooperative learning.” AC2007-517, ASEE Annual Conference and Exposition, Honolulu, HI, June 24-27, 2007. Furgeson, D., Comprehensive Assessment of Team Member Effectiveness (CATME) software, https://info.catme.org/ accessed on October 29, 2018. Gerhart, A., and Carpenter, D., 2016, “Level 3 (Formal) Cooperative Learning,” PowerPoint slide show as part of the KEEN ICE workshop, August 2016. Golter, B. Van Wie, G. Brown, D. Thiessen, and B. Abdul, 2010, Shifting Gears: Moving Away from the Controlled Experimental Model While Improving Rigor in Engineering Education research,” AC2010-2415, ASEE Annual Conference and Exposition, Louisville, KY, June 20-23, 2010. Greco, L., 2017, “B-52 Re-engine Effort Could Start in 2020,” FlightGlobal Online, November 30, 2017, https://www.flightglobal. com/news/articles/b-52-re-engine-effort-could-start-in-2020-443791/, accessed on January 10, 2017. Grigg, S. J., 2018, “Evaluating Innovations from a Critical Thinking Approach,” Paper presented at 2018 ASEE Annual Conference & Exposition, Salt Lake City, Utah. “ICE Workshops Transform Curriculum,” current ICE Workshop Flyer, January 21, 2018. Insinna, V., 2017, “US Air Force Glides towards B-52 Replacement,” Defense News Online, February 6, 2017, https://www.defensenews. com/air/2017/02/06/us-air-force-glides-toward-b-52-enginereplacement-plan/, accessed on January 10, 2018. Jayaram, S., 2013, “Implementation of Active Cooperative Learning and Problem-based Learning in an Undergraduate Control Systems

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course.” Paper ID#6493, ASEE Annual Conference and Exposition, Atlanta, GA, June 23-26, 2013. KEEN website, https://engineeringunleashed.com/, accessed on January 10, 2019. Kirkpatrick, S. R., Watt, A., and Bernal, A., 2016, “Developing an Entrepreneurial Mindset in Engineers: An Application of the Three C's (Creativity, Curiosity, and Connections) in a Collaborative Summer Mega-Course,” Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.26745. Kline, W. A., & Chow, T., & Ribera, T., 2018, “Analysis of Student Utilization and Activities in a Campus Innovation Center,” Paper presented at 2018 ASEE Annual Conference & Exposition, Salt Lake City, Utah. Layton, R., Ohland, M., and Pomeranz, H., 2007, “Software for Student Team Formation and Peer Evaluation: CATME Incorporates TeamMaker,” AC2007-1565, 2007 ASEE Annual Conference and Exposition, Honolulu, HI, June 24-17, 2007. Ledlow, S., White-Taylor, J., and Evans, D., 2002, “Active/Cooperative Learning: A Discipline-Specific Resource for Engineering Education,” Session 2793, ASEE Conference and Exposition, Montreal, Canada, Junew16-19, 2002. Mattingly, J., and Boyer, K., 2016, Elements of Propulsion: Gas Turbines and Rockets, 2nd ed., American Institute of Aeronautics and Astronautics, Reston, VA. McStravick, D., and O’Malley, M., 2007, “Improving Interdisciplinary Capstone Design Projects with Cooperative Learning in the MediFridge Project,” AC2007 – 1674, ASEE Annual Conference and Exposition, Honolulu, HI, June 24-27, 2007. Mizokami, “The Air Force Wants a Lightweight Fighter to do what the F35 Can’t,” Popular Mechanics Online, Feb 27, 2017, https://www. popularmechanics.com/military/aviation/news/a25400/air-forcelightweight-fighter-oa-x/ accessed on February 2, 2018. Practical Aeronautics Inc., used by permission.

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Pung, C., and Farris, J., 2011, “Assessment of the CATME Peer Evaluation Tool Effectiveness,” AC2011-2116, 2011 ASEE Annual Conference and Exposition, Vancouver, B.C., Canada, June 26-29, 2011. Scales, A., and Varnado, T., 2012, “Active Learning For Engineering/Technical Graphics Online Environments, AC 2012-3590, ASEE Annual Conference and Exposition, San Antonio, TX, June 1013, 2012. Seery, N., & Canty, D., & Dunbar, R., 2010, “Maximizing the Impact of Creative and Innovative Activities within the Constraints of Defined Education Structures,” Paper presented at 2010 Annual Conference & Exposition, Louisville, Kentucky. Stanford University, Teaching Commons, Think-Pair-Share, https:// teachingcommons.stanford.edu/resources/learning/learningactivities/think-pair-share, accessed on March 17, 2018. The Engineer, 2012, http://www.theengineer.co.uk/aerospace/indepth/dream-factories.article accessed on 1/11/12. Van Treuren, K. W., and McClain, S. T., 2010, “The Challenges of High Altitude Gas Turbine Engine Cycles,” GT2010-23490, Proceedings of the ASME Turbo Expo 2010: Glasgow, UK, June 14-18, 2010. Van Treuren, K. W., 2018a, “Applying Active Learning to an Introductory Aeronautics Class,” ASEE National Conference and Exposition, June 24-27, 2018, Salt Lake City, UT. Van Treuren, K. W., 2018b, “Using Active Learning and Team Competition to Teach Gas Turbine Cycle Design,” ASEE Gulf Southwest Annual Conference, April 4-6, 2018, Austin, TX. Van Treuren, K. W., 2019, “Involving Students in the Learning Process – Using Team Competition to Teach Gast Turbine Cycle Design,” GT2019 – 90535, Proceedings of the ASME Turbo Expo 2019 Turbine Technical Conference and Exposition, International Gas Turbine Institute, Phoenix, AZ, June 17-21, 2019. USAF Academy Lightweight Fighter Design Spreadsheet, USAF Academy, 1983.

In: Mechanical Engineering Education … ISBN: 978-1-53617-791-6 Editor: Charles E. Baukal, Jr. © 2020 Nova Science Publishers, Inc.

Chapter 6

ASSESSMENT OF CONVERSION FROM PROBLEM-BASED LEARNING TO ENTREPRENEURIALLY MINDED LEARNING IN A SEMESTER-LONG SENIOR/GRADUATE MECHATRONIC DESIGN PROJECT James A. Mynderse1, Jeffrey N. Shelton2 and Andrew L. Gerhart1 1

Lawrence Technological University, Southfield, MI US 2 Purdue University, West Lafayette, IN, US

Keywords: problem-based learning, project-based entrepreneurship, mechatronics, design, KEEN

learning,

INTRODUCTION Entrepreneurial education in the United States has existed within business schools since the end of World War II (Katz, 2003), but entrepreneurial education within engineering is now catching up. Among

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others, the National Science Foundation’s (NSF) Epicenter Pathways Initiative, NSF’s I-Corps Program, and the Kern Entrepreneurial Engineering Network (KEEN), have incentivized engineering faculty training in entrepreneurial education and the number of engineeringspecific entrepreneurial education publications is increasing at about the same rate as publications on entrepreneurial education across all disciplines. (Huang-Saad, Morton, & Libarkinbl, 2018). At Lawrence Technological University, this shift in engineering curriculum resulted in a multiyear effort to incorporate entrepreneurial education throughout the engineering curriculum (Gerhart & Carpenter, 2013; Gerhart, Carpenter, Fletcher, & Meyer, 2014; Bell-Huff, Carpenter, & Gerhart, 2016). Entrepreneurially minded learning (EML) activities, as championed by KEEN, combine problem-based or project-based learning activities with student skills associated with an entrepreneurial mindset. For examples, these additional skills might include integrating information from many sources to gain insight, conveying engineering solutions in economic terms, and identifying unexpected opportunities to create value. EML activities emphasize “discovery, opportunity identification, and value creation with attention given to effectual thinking over causal (predictive) thinking” (Gerhart & Melton, 2017). As a partner school in KEEN, Lawrence Tech uses the KEEN framework to define an entrepreneurial mindset. The KEEN framework begins with the “three Cs” of Curiosity, Connections, and Creating Value (Kern Entrepreneurial Engineering Network, n.d.). Each element is supported by two example student behaviors that describe typical actions displayed by those operating with an entrepreneurial perspective. For instance, Curiosity is demonstrated by “explore a contrarian view of accepted solutions” and Creating Value is demonstrated by “identify unexpected opportunities to create extraordinary value.” The framework continues from the three Cs to Engineering Thought and Action, Collaboration, Communication, and Character. As with the three Cs, each concept is supported by example student behaviors. In total, the framework includes 18 example student behaviors.

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Within engineering and the KEEN framework in particular, an entrepreneurial mindset is not the same as entrepreneurship. While business schools have historically focused on wealth-creation or firmcreation (Katz, 2003), the engineering-specific entrepreneurial mindset is thinking like an entrepreneur. This means the application of the KEEN framework “three Cs” to engineering practice, but not necessarily the creation of new business (Wheadon & Duval-Couetil, 2017). Inclusion of entrepreneurial education is a valuable addition to the traditional engineering curriculum (Condoor & McQuilling, 2009; Kim & Tranquillo, 2014; Jordan, Fry, & Treuren, 2016) and aligns with portions of the previous ABET Criterion 3a-k (Duval-Couetil, Wheadon, Kisenwether, & Tranquillo, 2013). In this work, a senior/graduate course in mechatronic design was modified to convert an existing design project to a problem-based learning exercise (Mynderse & Shelton, 2014; Mynderse & Shelton, 2015) (Fall 2013, Fall 2014) and then to an EML exercise (Mynderse, Shelton, & Gerhart, 2017) (Fall 2015, Fall 2016). While an argument could be made that the inclusion of social value in the PBL offerings added components of an entrepreneurial mindset, these PBLs lacked attention to economic value. PBL-to-EML modifications were made to provide a specific customer for design tasks, increase student focus on economic drivers, and provide student teams with ambiguity in stakeholder requirements (Mynderse, Shelton, & Gerhart, 2017). This work assesses the student demonstration of sample behaviors associated with an entrepreneurial mindset and provides comparison before and after EML course modifications.

PROBLEM-BASED LEARNING AND PROJECT-BASED LEARNING Problem-based learning (PBL) and project-based learning (PjBL) are active and collaborative learning techniques that introduce engaging realworld problems for students to solve, usually as part of a group (Prince, 2004). Originally developed for use in medical schools, PBL activities may

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span several weeks, or longer, and may include both in-class and out-ofclass work for student teams. Prince and Felder distinguish between PBL and PjBL by noting that the “emphasis in project-based learning is on applying or integrating knowledge while that in problem-based learning is on acquiring it” (Prince & Felder, 2006). Additionally, despite similarity in most student outcomes, students may perceive a higher level of autonomy with PjBL compared to PBL due to the availability of many possible solutions (Stefanou, Stolk, Prince, Chen, & Lord, 2013). The literature supports the use of PBL to develop technical engineering skills as well as professional skills. Many students feel unprepared for capstone design projects and wish capstone design projects occurred earlier in the curriculum (Atman, et al., 2010). PBL is an effective learning experience that provides practice with complex problem solving outside of the context of a capstone experience (Litzinger, Lattuca, Hadgraft, & Newstetter, 2011). However, the problems presented to students must be authentic, which can be difficult for instructors to create (Jamaludin, Mohd.Yusof, Harun, & Hassan, 2012). PBL activities can substantially improve long-term student learning (Yadav, Subedi, Lundeberg, & Bunting, 2011; Hsieh & Knight, 2008; Prince & Felder, 2006) and skill development (Prince & Felder, 2006). Cooperative learning, as experienced during PBL or PjBL activities, promotes academic success, quality of relationships, and self-esteem (Johnson, Johnson, & Smith, 1998). The course under considering in this work includes features of both PBL and PjBL. As described in the following sections, students use a scaffolded semester-long mechatronic design project to learn skills associated with mechatronic design. Some aspects of the design project, such as integrating sensors and actuators, make explicit use of classroom instruction. These are clearly “applying or integrating knowledge” and imply PjBL. Other aspects of the design project, such as identifying an appropriate balance between domains of mechatronics and debugging, are not taught during classroom instruction and must be learned by students during the hands-on practice. These are “acquiring knowledge” and imply PBL.

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MECHATRONIC DESIGN Mechatronics is characterized by the integration of mechanical, electronic, control, and computer systems and may be viewed as the intersection of at least three of these four domains, as shown in Figure 1. Mechanical systems may include thermal systems, fluid systems, or solid mechanics. Electronic systems may include sensors, actuators, power systems, or communication systems. Control systems at the supervisorylevel and part-level may include digital logic, state machines, or automatic feedback control. Computer systems may include both the use of computers in the design phase and the integration of microcontrollers or other computational platforms into the final product. The study of mechatronics is, by nature, an interdisciplinary engineering field. A conventional sequential design might require a mechanical engineer to develop the mechanical design in advance of asking an electrical engineer to integrate necessary electronic components. Once mechanical and electronic designs are finalized, a programmer might then be required to invoke desired device actions and responses. While each step of this sequential process may produce the best design then available, limitations imposed by decisions at prior steps preclude any guarantee that the overall design will be optimal.

Figure 1. The interdisciplinary nature of Mechatronics (Craig, 2001).

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Mechatronic design is the process of applying all four domains (mechanical, electronic, control, and computer) in a parallel process. To optimize a mechatronic product, the engineer must simultaneously consider sensor, actuator, and software needs while designing (or selecting) mechanical components. Furthermore, performance objectives may potentially be addressed in any of the four domains with varying degrees of complexity and cost. The mechatronic systems engineer must identify tradeoffs between potential design approaches and arrive at a solution that appropriately balances performance against time, cost, and other relevant metrics. One example of integrated mechatronic design is the simultaneous optimization of mechanical and control systems (Peters, Papalambros, & Ulsoy, 2011). To participate in a parallel, interdisciplinary, design process, the mechatronic systems engineer must have a familiarity with, if not expertise in, each of the four mechatronic domains. The design course under consideration, MRE 5183 – Mechatronic Systems I provides students with an opportunity to improve their skills in each of the four domains and to practice mechatronic design through a semester-long design experience. Beyond the skills explicitly taught during classroom sessions and structured lab experiments, two skills are of particular importance during the design experience: selecting an optimal balance of the four domains comprising mechatronics and debugging. Neither skill is expressly taught, but the instructor provides feedback during regular interaction with student teams to assist in developing these skills.

MECHATRONIC DESIGN COURSE STRUCTURE The structure of the mechatronic design courses, shown in Figure 2, is framed around the semester-specific design problem. After issuing the design problem to students within the first week of the semester, the combination of lecture topics and structured laboratory experiments form an integral part of the design experience as they provide exposure to

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needed techniques while remaining rooted in the context of the problem. Lecture includes both in-class sessions and video lectures available online. This combined on-ground and on-line delivery reduces the duration of lecture to only eight (8) weeks out of the sixteen (16) week semester (including holidays). Following completion of the lecture topics and structured laboratory experiments, student teams are free to work exclusively on the design project in the remaining eight (8) weeks of the semester. The complete course schedule is shown in Figure 3. The schedule is color-coded to differentiate lecture (yellow), laboratory experiments (orange), and time solely dedicated to the design project (blue) sessions. Laboratory experiments and concepts covered are provided in Table 1.

Figure 2. Mechatronic design course structure.

Structuring the course around the design project required that lecture material and laboratory topics frequently loop back to the overall design problem. For instance, the first lecture topic was an introduction to mechatronic design. This class session included Shelton’s Rules of Design (an informal set of design rules provided developed over 30+ years of practicing and teaching design, see Figure 4), a collaborative brainstorming

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session to identify the relative strengths of mechanical, electrical, control, and computer system-based solutions to a simple problem, and the introduction of the design problem. Later sessions included Think-PairShare (Canino, 2015) activities to discuss prompts such as “What types of sensors will you use on your design project?” During these Think-PairShare activities, students thought quietly about the prompt for a set length of time (usually two minutes or less), discussed with a partner to achieve consensus, and then shared with the rest of the class.

Figure 3. Complete class schedule for mechatronic design course.

Table 1. Structured lab experiment concepts Lab # Lab Title 1 Electronic Systems and Testing 2 Introduction to Computer Systems 3 Tradeoffs Between Hardware And Software 4 The Digital Lock 5 Analog Input and Output 6 Implementing Sensors

7

Concepts Applied Oscilloscope, function generator, digital multimeter, DC power supply, logic analyzer Arduino Uno programming, integrating switches and transistors Boolean logic with TTL, Boolean logic with IC, Boolean logic in software, voltage comparator Finite state machine design and implementation Sampling, integrating analog IR sensors, filtering

Integrating analog and digital IR sensors, calibration, integration of sensors with finite state machine Implementing Actuators DC motors: PWM speed control and H-bridge

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Figure 4. Shelton’s “Rules of Design,” an informal set of design rules provided to assist students in working together on their mechatronic design problem.

Laboratory experiments were tailored to the needs of the project because students knew the project focus and could easily relate the concepts to their preliminary designs. For instance, during Lab 3: Tradeoffs between Hardware and Software, students explored the differences between logical operations implemented using transistors, integrated circuits, and software on the Arduino microcontroller. Students identified advantages and disadvantages of each hardware and software implementation. In Lab 5: Analog Input and Output, students compared RC filters with software filtering using the Arduino microcontroller. After testing, students commented on how their results applied to their design project. More obviously, Lab 6: Implementing Sensors and Lab 7: Implementing Actuators built on Lab 4: The Digital Lock to incorporate sensors and actuators into a finite state machine for controlling motion based on object detection. Finally, the midterm examination asked students to develop a finite state machine (FSM) to solve a simple task. Each semester, the selected task was a subset of the semester-long design activity. For example, one design task involved locating and moving tennis balls within a playing field marked with a circle. The corresponding midterm exam tasked students with generating FSM logic for moving a robot (equipped with downward facing sensors capable of line detection) from a position inside the circle to the circle

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periphery, and to then following the circle, moving in either the clockwise or counterclockwise direction. Students were also required to implement their design on an Arduino Uno microcontroller board. Having completed the structured preliminary work building the foundation for the design project, students were ready to begin unstructured class periods dedicated to the design project. Student design teams (about 4 students each) were formed around the fifth week of the semester by combining two sets of lab partners. Each design team selected a name and organized distribution of tasks. Biweekly progress reports (using a prepared template) provided all teams an opportunity to understand the challenges and successes of their peers. Discussion among the teams was encouraged during these progress reports.

MECHATRONIC DESIGN PROJECT TASKS A new problem statement was developed each year and provided to students prior to the second class period. Problem statements used were classified into three types:   

locate and flag randomly distributed objects, locate, capture, and transport tennis balls to a horizontal hole, locate, capture, and transport tennis balls to a vertical gate.

In each case, student teams designed and built small, untethered robots to solve the problem. The original problem statement (Spring 2013) offered only technical details. Problems used for the formal PBL approach (Fall 2013, Fall 2014) incorporated real-world details (Mynderse & Shelton, 2014; Mynderse & Shelton, 2015). Problems used for the EML approach (Fall 2015, Fall 2016) added additional stakeholder and economic concerns (Mynderse, Shelton, & Gerhart, 2017). A table of problem types and corresponding offerings is provided in Table 2.

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Table 2. Design problem types and corresponding offerings

Task Type Flag Distributed Objects (walls only) Move Random Balls to Horizontal Hole (circle track) Move Random Balls to Vertical Gate (walls only)

PBL Fall 2013 Fall 2014 -

EML Fall 2015 Fall 2016

Figure 5. Assignment of the semester-long mechatronic design problem during the first lecture session.

Figure 6. Photos of playing field: (left) folded for storage, (right) unpainted side wall during construction.

Student-designed robots were tested within a 7’ x 7’ playing field with side rails. Photos of the playing field are shown in Figure 6. The side rails

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fit loosely into holes in the field, preventing robots from escaping but not acting as a reliable surface for sensing. The black circle was made with electrical tape and was removed for the projects that provided only the side rails.

DIRECT ASSESSMENT OF STUDENT DESIGNS: COMPETITION PERFORMANCE The first direct assessment of student designs was the results of the friendly design competition. The term “friendly” was emphasized with students to reinforce that the results had very little impact on semester grades. Winning was rewarded with bragging rights and the occasional non-grade prize (candy bar, back scratcher, t-shirt, etc.). Competition scoring rewarded locating the randomly distributed objects, completion of the task, and total elapsed time. In each case, the formula used was 𝑠𝑐𝑜𝑟𝑒 =

60 𝑡𝑖𝑚𝑒

+

# 𝑓𝑜𝑢𝑛𝑑 4

+ # 𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑒𝑑

where 𝑡𝑖𝑚𝑒 was measured in seconds, # 𝑓𝑜𝑢𝑛𝑑 was the number of objects found or collected, and # 𝑐𝑜𝑚𝑝𝑙𝑒𝑡𝑒𝑑 was the number of objects marked or deposited in the correct location. Each competition included only three objects to locate and reposition. In each competition, teams were given three attempts to solve the problem. Each team was judged on the best score from the three attempts. Scores for each competition are summarized in Table 3. From the limited competition data available in Table 3, it is not immediately clear whether or not the course modifications have resulted in improved student designs. Despite previously reported improvements in competition performance moving to PBL (Mynderse & Shelton, 2015), the change from PBL to EML in Fall 2013 to Fall 2015 does not immediately

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show up in student competition scores. Anecdotally, competition performance is random and even “good” designs can fail to function. Table 3. Summary of competition scores

Flag Distributed Objects (walls only)

Move Random Balls to Horizontal Hole (circle track)

Move Random Balls to Vertical Gate (walls only)

Fall 2013 Fall 2014 Fall 2015 Fall 2016 (PBL) (PBL) (EML) (EML) 2.25 2.25 1.75 2.75 3.75 3.75 1.00 5.97 5.25 5.63 5.97 1.50 1.00 1.00 1.75

What is immediately clear from Table 3 is that the design tasks are not of uniform difficulty. The presence of the black circle within the playing field made the horizontal hole problem significantly easier. Anecdotally, use of the side walls for navigation was extremely difficult for student teams. This was due, at least in part, to the poor tolerances in construction of the playing field. While interesting, the effect of problem statement difficulty on student performance is not addressed in this work.

DIRECT ASSESSMENT OF STUDENT DESIGNS: RUBRIC Student designs were directly assessed using the rubric provided in the Appendix. The criteria (i.e., rows within the rubric) have been numbered 1 through 13. Technical criteria (rows 1 to 10) contribute positively to the score while writing criteria (rows 11 to 13) are available for point reductions only. As a senior/graduate level course, student teams are

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assumed to have a strong command of technical communication. Each criterion is placed in one of three performance levels: “does not meet expectations,” “meets expectations,” or “exceeds expectations.” Scoring for performance levels was consistent with letter grades: “does not meet expectations” was equivalent to D-F grades, “meets expectations” was equivalent to B-C grades, and “exceeds expectations” was equivalent to A grades. Overall rubric results are shown in Table 4. As with the competition results, the rubric scores in Table 4 do not make it immediately apparent whether or not the PBL to EML course modifications have improved student design work. Comparing the same design task, the Fall 2013 (PBL) to Fall 2015 (EML) comparison resulted in higher design scores with the rubric. Unlike the competition performance, the difference in difficulty between design tasks should be less relevant on the rubric. A t-test does not confirm a difference between the PBL and EML scores, though the EML minimum, maximum, and average are slightly higher than those for PBL problems. Table 4. Summary of rubric scores

Flag Distributed Objects (walls only)

Move Random Balls to Horizontal Hole (circle track)

Move Random Balls to Vertical Gate (walls only)

Fall 2013 Fall 2014 Fall 2015 Fall 2016 (PBL) (PBL) (EML) (EML) 81 73 73 65 67 56 59 77 76 66 64 75 70 69 66

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Despite the burden of additional requirements and discussion points, the conversion from formal PBL to EML activities did not decrease student design performance as measured by the project report rubric. Adding a focus on developing the entrepreneurial mindset did not come at a cost to students’ technical work.

INDIRECT ASSESSMENT OF STUDENT LEARNING AND ENTREPRENEURIAL MINDSET Finally, assessment of student learning and dimensions of the entrepreneurial mindset, as defined by the KEEN framework, was performed using exit surveys. The student survey was administered using anonymous paper copies immediately after the friendly design competition. Table 5. All students’ ratings of general statements after completion of the design project. Using a scale of 1 to 5, 1 indicates “strongly disagree” and 5 indicates “strongly agree” Fall 2013, Fall 2014 Fall 2015, Fall 2016 (PBL) N = 24 (EML) N = 27 Mean St. dev. Mean St. dev. My project accomplished the required task I consider the results of my project successful I found my work on the project to be satisfying I had fun working on the project The real-world application of the project motivated me to do my best work The open-ended nature of the project motivated me to do my best work. I got what I wanted out of this course (the course met my expectations).

4.46

0.72

4.07

0.96

4.33

0.82

3.70

1.03

4.50

0.52

4.37

0.88

4.54

0.78

4.59

0.57

4.46

0.83

4.26

1.06

4.21

0.83

4.22

0.93

4.67

0.48

4.65

0.49

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All students completed the survey. Questions included general topics, perceived improvements in technical skills, and demonstration of entrepreneurial mindset dimensions taken directly from the KEEN framework. To limit the length of the survey, only those 8 entrepreneurial mindset dimensions most related to EML design project modifications were addressed in the survey. Future work will focus on the remaining 10 dimensions and identify specific course modifications that might encourage associated behaviors. Exit survey results for students with PBL implementation (N = 24) and EML implementation (N = 27) are shown in Table 5, Table 6, and Table 7. The exit survey was modified after Fall 2013 to add additional questions about technical improvements and all questions about entrepreneurial mindset dimensions. Specific questions have been marked with reduced N values. Survey results are broken into responses to questions about general topics (Table 5), perceived improvements in technical skills (Table 6), and demonstration of entrepreneurial mindset dimensions (Table 7). From the survey results in Table 5, students generally agreed that “I found my work on the project to be satisfying,” “I had fun working on the project,” and “I got what I wanted out of this course (the course met my expectations).” Both the PBL and EML course projects presented students with an illdefined real-world problem. The survey asked students if this sort of problem motivated them to do their best work. Average responses were a bit lower than on the previous questions, but as seen in Figure 7 most students agreed or strongly agreed. From the survey results in Table 6, students agreed that the course and project improved technical skills for both the PBL and EML projects. The only dimension showing a difference between PBL and EML projects (p < 0.1) was the project improving technical skills in “selecting an appropriate balance of mechanical-electrical-software design.”

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Table 6. All students’ ratings of technical skill improvement after completion of the design project. Using a scale of 1 to 5, 1 indicates “strongly disagree” and 5 indicates “strongly agree” Fall 2013, Fall 2014 Fall 2015, Fall 2016 (PBL) N = 24 (EML) N = 27 Mean St. dev. Mean St. dev. This PROJECT improved my technical skills in: Selecting sensors and actuators. 4.42 0.78 4.32 0.75 Interfacing sensors and actuators into a 4.33 0.76 4.20 0.87 mechatronic system. Integrated mechanical-electrical4.50 0.72 4.36 0.70 software design. Identifying trade-offs in mechatronic 3.86 0.53 4.20 0.96 design. (N = 14) Selecting an appropriate balance of mechanical-electrical-software design. 3.86 0.95 4.44 0.77 (N = 14) This COURSE improved my technical skills in: Selecting sensors and actuators. 4.42 0.65 4.46 0.86 Interfacing sensors and actuators into a 4.50 0.59 4.42 0.70 mechatronic system. Integrated mechanical-electrical4.46 0.66 4.38 0.64 software design. Identifying trade-offs in mechatronic 4.14 0.77 4.31 0.55 design. (N = 14) Selecting an appropriate balance of mechanical-electrical-software design. 4.21 0.58 4.38 0.70 (N = 14)

The distribution of student responses is shown in Figure 8. This result may be due to the increased focus on cost effectiveness of student designs fostered by the EML project. The issue of cost was a frequent discussion point between the instructor and project teams, leading to interesting thoughts about alternate approaches.

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Figure 7. Survey responses regarding the real-world open-ended project. Using a scale of 1 to 5, 1 indicates “strongly disagree” and 5 indicates “strongly agree.”

The highest average responses for both PBL and EML projects were to the course and project improving technical skills in “selecting sensors and actuators,” “interfacing sensors and actuators into a mechatronic system,” and “integrated mechanical-electrical-software design.”

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Table 7. All students’ ratings of EML-specific statements after completion of the design project. Using a scale of 1 to 5, 1 indicates “none at all” and 5 indicates “throughout most of the project” Fall 2013, Fall 2014 Fall 2015, Fall 2016 (PBL) N = 14 (EML) N = 27 Mean St. dev. Mean St. dev. During the course of this project, to what extent did you: Integrate information from many 4.21 0.70 4.44 0.75 sources to gain insight Assess and manage risk 3.86 1.10 4.07 0.83 Persist through failure 3.79 1.05 4.56 0.64 Apply creative thinking to ambiguous 4.21 0.80 4.67 0.55 problems Apply systems thinking to complex 3.71 0.83 4.22 0.85 problems Understand the motivations and 3.71 0.83 4.37 0.69 perspectives of others Convey engineering solutions in 3.57 1.09 4.19 1.00 economic terms Substantiate claims with data and facts 3.71 0.83 3.78 1.05

Figure 8. Survey responses regarding improvements in selecting an appropriate balance. Using a scale of 1 to 5, 1 indicates “strongly disagree” and 5 indicates “strongly agree.”

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Figure 9. Survey responses regarding improvements in design skill. Using a scale of 1 to 5, 1 indicates “strongly disagree” and 5 indicates “strongly agree.”

Figure 9 shows the distribution of responses to questions about project and course related improvement in “integrated mechanical-electricalsoftware design.” From Figure 9, responses related to the project were lower after the conversion from PBL to EML while responses related to the course were similar. These were not significantly different (p < 0.1).

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Figure 10. Student self-reported demonstration of entrepreneurial mindset dimension example behaviors as a proportion of total response. Using a scale of 1 to 5, 1 indicates “none at all” and 5 indicates “throughout most of the project.”

Table 8. t-Test results from student demonstration of entrepreneurial mindset example behaviors between PBL and EML projects

Integrate information from many sources to gain insight Assess and manage risk Persist through failure Apply creative thinking to ambiguous problems Apply systems thinking to complex problems Understand the motivations and perspectives of others Convey engineering solutions in economic terms Substantiate claims with data and facts

t-Test Result 0.35 0.48 0.01 0.04 0.07 0.01 0.08 0.85

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Figure 11. Student self-reported demonstration of entrepreneurial mindset dimension example behaviors as a proportion of total response. Using a scale of 1 to 5, 1 indicates “none at all” and 5 indicates “throughout most of the project.”

Survey results related to entrepreneurial mindset dimensions are shown in Table 7. It was expected that the switch from PBL to EML would improve student demonstration of the example behaviors. Average response for all dimensions increased from the PBL to EML deployments, but the PBL sample size was small. The distributions of student selfreported demonstration of the entrepreneurial mindset dimension example behaviors are shown in Figure 10, Figure 11, Figure 12, and Figure 13. From the distributions shown above, it appeared that there was a difference between PBL and EML projects in terms of student demonstration of the entrepreneurial mindset example behaviors. A t-test was applied and the conversion from PBL to EML showed a significant difference (p < 0.1) in demonstration of the following example behaviors:

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“persist through failure,” “apply creative thinking to ambiguous problems,” “apply systems thinking to complex problems,” “understand the motivations and perspectives of others” and “convey engineering solutions in economic terms.” All t-test results are shown in Table 8. From the t-test results shown in Table 8, the conversion from PBL to EML was successful in increasing student demonstration of five example behaviors associated with an entrepreneurial mindset. From Figure 10, Figure 11, and Figure 13, students were already demonstrating the remaining three example behaviors prior to conversion from PBL to EML.

Figure 12. Student self-reported demonstration of entrepreneurial mindset dimension example behaviors as a proportion of total response. Using a scale of 1 to 5, 1 indicates “none at all” and 5 indicates “throughout most of the project.”

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Figure 13. Student self-reported demonstration of entrepreneurial mindset dimension example behaviors as a proportion of total response. Using a scale of 1 to 5, 1 indicates “none at all” and 5 indicates “throughout most of the project.”

STUDENT FREE RESPONSE Written statements were also gathered on the student surveys. The students were asked what they liked (or appreciated) about the project, what should be changed, and any other additional comments/observations. Students’ positive comments followed two themes: the hands-on nature of the course and application of the course concepts to the project.

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“Hands-on lab experiments and opportunity to actually use learned knowledge on a very open-ended design/build project.” “The division of the course into lab phase and project phase (being able to concentrate on one type of work at a time, rather than having the lab and the project overlap.)” “The lab sections before project was very interesting and also became the platform to gain practical knowledge.” “I liked that we were able to get hands on experience throughout the course with the labs.” “I really enjoyed the project (stress and all).” “I like how we practice everything we took in the lab. I liked the idea of the project.” “It was nice to work together in a similar project with other groups and see their designs; comparing different approaches to solving the same problems.” “Very applied. We were given enough theory to explore on our own (Best way to learn!) The project helped in troubleshooting (a skill that cannot be acquired in a different manner).” “Half the class was a real world project. We weren’t just being taught random information, we were applying it.” “I learned … that ‘learning through failure is important’ & improving upon them is the key.” “I loved the content and the final project topic.” “I felt that this class pushed me out of my comfort zone and forced [me] to really think like an engineer.” “I like the [concentration] on practical theory. We got lots of hands-on experience” “It was good to get design practice rather than just analysis” “Having such a large final project for the second half of the semester is by far the most production use of time. I learned far more in this class than most others.”

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“Working in a group … helped expose me to not only mechatronic systems, but also different ways of approaching different problems and new methods of doing things.” “It’s the most wonderful class for a mechanical engineer to know how automation begins.” “I really enjoyed the course, I would strongly recommend this class to a fellow engineer.” “I liked the progression of the course.” “I feel like the project really helped me build some deeper relationships with colleagues.”

When asked what should be changed, students cited the duration of the project and the need for additional resources for students with less background. Students with background in Electrical Engineering specifically identified the lack of mechanical design instruction, which was not included due to the prevalence of students from Mechanical Engineering. Meanwhile, students with background in Mechanical Engineering identified a need for more instruction on soldering and programming.  



“It would be nice to have more time (1-2 weeks more) to work on the project.” “Some people initially seemed completely lost when it came to Arduino programming. It would be nice to have an individual assignment in the first week or two of the class (before labs start) that would give everybody a chance to familiarize (or refamiliarize) themselves with programming and with the Arduino.” “I think the project needs to be re-vamped. Having only 1.5 months to complete a project that I had no experience with was asking a little too much. While I was successful, it took a tremendous effort, more than should be required for a 3 credit course, in my opinion.”

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“The final project needs more time throughout the course.” “More prior instruction on robot design … also teaching additional skills needed to build the robot such as soldering.” “I wish I would have had more time for refining the project. Work and life get in the way unfortunately. “It’s difficult to see how to [setup] some of the logic and read some of the electrical schematics.” “The project was kind of boring. Would have liked something more interesting.” “It may have been satisfying to have some Mechanical takeaways from the course.” “If we had more time we could have made a better project.”

Very few students expressed dissatisfaction with the course or project before or after the conversion from PBL to EML. As a technical elective for undergraduate students, overall student satisfaction is an important metric to ensure continued enrollment. From the student free response, the course continues to appeal to students with either PBL or EML project statement.

CONCLUSION A senior/graduate level mechatronic design course was modified to convert an existing formal PBL project to an EML project. Direct assessment demonstrated no significant change in student design performance between the PBL and EML projects, but indirect assessment results showed significant differences in student demonstration of example behaviors associated with an entrepreneurial mindset as defined by the KEEN framework. This increase in demonstration of an entrepreneurial mindset came at no cost to the technical mechatronic design work.

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REFERENCES Atman, C. J., Sheppard, S. D., Turns, J., Adams, R. S., Fleming, L. N., Stevens, R., ... Lund, D. (2010). ENABLING ENGINEERING: The Final Report for the Center for the Advancement of Engineering Education. Retrieved from http://www.engr.washington.edu/caee/. Bell-Huff, C. L., Carpenter, D. D., & Gerhart, A. L. (2016). Fostering an Entrepreneurial Mindset through a Sophomore-Level, MultiDisciplinary, Engineering Design Studio Experience. Proc. ASEE Annual Conference & Exposition. Canino, J. V. (2015). Comparing Student Performance in Thermodynamics Using the Flipped Classroom and Think-Pair-Share Pedagogies. Proc. 2015 ASEE Annual Conference & Exposition. Seattle, Washington. Condoor, S., & McQuilling, M. (2009, June). Incorporating An Entrepreneurial Mindset In Freshman Engineering Students. Proc. 2009 ASEE Annual Conference & Exposition. Craig, K. (2001, Jun). Is anything really new in mechatronics education? IEEE Robotics Automation Magazine, 8(2), 12-19. Duval-Couetil, N., Wheadon, J., Kisenwether, E., & Tranquillo, J. (2013, Oct). Entrepreneurship and ABET accreditation: How and where does it fit? Proc. 2013 IEEE Frontiers in Education Conference. Gerhart, A. L., & Carpenter, D. D. (2013). Campus-wide Course Modification Program to Implement Active & Collaborative Learning and Problem-based Learning to Address the Entrepreneurial Mindset. Proc. 2013 ASEE Annual Conference & Exposition. Gerhart, A. L., & Melton, D. E. (2017). Entrepreneurially Minded Learning: Incorporating Stakeholders, Discovery, Opportunity Identification, and Value Creation into Problem-based Learning Modules with Examples and Assessment Specific to Fluid Mechanics. Journal of Engineering Entrepreneurship, 8(1), 44-62. Gerhart, A. L., Carpenter, D. D., Fletcher, R. W., & Meyer, E. G. (2014). Combining Discipline-specific Introduction to Engineering Courses into a Single Multidiscipline Course to Foster the Entrepreneurial

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Mindset with Entrepreneurially Minded Learning. Proc. 2014 ASEE Annual Conference & Exposition. Hilliger, I., Mendoza, C. M., Pérez-Sanagustín, M., & Vega, M. D. (2017). Does the Revision of ABET Student Outcomes Include the Competencies Required to Succeed in Start-Ups and Entrepreneurial Companies? Proc. 2017 ASEE Annual Conference & Exposition. Hsieh, C., & Knight, L. (2008). Problem-Based Learning for Engineering Students: An Evidence-Based Comparative Study. The Journal of Academic Librarianship, 34(1), 25-30. Huang-Saad, A. Y., Morton, C. S., & Libarkinbl, J. C. (2018). Entrepreneurship Assessment in Higher Education: A Research Review for Engineering Education Researchers. Journal of Engineering Education, 107(2), 263-290. Jamaludin, M. Z., Mohd.Yusof, K., Harun, N. F., & Hassan, S. A. (2012). Crafting Engineering Problems for Problem-Based Learning Curriculum. Procedia - Social and Behavioral Sciences, 56, 377-387. Johnson, D. W., Johnson, R. T., & Smith, K. A. (1998). Cooperative Learning Returns To College What Evidence Is There That It Works? Change: The Magazine of Higher Learning, 30(4), 26-35. Jordan, W. M., Fry, C. C., & Treuren, K. W. (2016, June). Promoting the Entrepreneurial Mindset through Faculty Development. Proc. 2016 ASEE Annual Conference & Exposition. Katz, J. A. (2003). The chronology and intellectual trajectory of American entrepreneurship education 1876–1999. Journal of Business Venturing, 18, 283-300. Kern Entrepreneurial Engineering Network. (n.d.). Entrepreneurial Mindset 101. Retrieved from http://engineeringunleashed.com/keen/ em101/. Kim, C., & Tranquillo, J. (2014, June). K-WIDE: Synthesizing the Entrepreneurial Mindset and Engineering Design. Proc. 2014 ASEE Annual Conference & Exposition. Litzinger, T. A., Lattuca, L. R., Hadgraft, R. G., & Newstetter, W. C. (2011). Engineering Education and the Development of Expertise. Journal of Engineering Education, 100(1), 123-150.

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Mynderse, J. A., & Shelton, J. (2015, June). Assessment of an Improved Problem-Based Learning Implementation in a Senior/Graduate Mechatronic Design Course. Proc. 2015 ASEE Annual Conference & Exposition. Mynderse, J. A., & Shelton, J. N. (2014, June). Implementing ProblemBased Learning in a Senior/Graduate Mechatronics Course. Proc. 2014 ASEE Annual Conference & Exposition. Mynderse, J. A., Shelton, J. N., & Gerhart, A. (2017). Entrepreneurially Minded Learning in a Semester-Long Senior/Graduate Mechatronic Design Project. Proc. of the ASME 2017 Dynamic Systems and Control Conference. Prince, M. (2004). Does Active Learning Work? A Review of the Research. Journal of Engineering Education, 93(3), 223-231. Prince, M. J., & Felder, R. M. (2006). Inductive Teaching and Learning Methods: Definitions, Comparisons, and Research Bases. Journal of Engineering Education, 95(2), 123-138. Smith, K. A. (2011, Oct). Cooperative learning: Lessons and insights from thirty years of championing a research-based innovative practice. Frontiers in Education Conference (FIE), 2011, (pp. T3E-1-T3E-7). Smith, K. A., Sheppard, S. D., Johnson, D. W., & Johnson, R. T. (2005). Pedagogies of Engagement: Classroom-Based Practices. Journal of Engineering Education, 94(1), 87-101. Stefanou, C., Stolk, J. D., Prince, M., Chen, J. C., & Lord, S. M. (2013). Self-regulation and autonomy in problem- and project-based learning environments. Active Learning in Higher Education, 14(2), 109-122. Wheadon, J., & Duval-Couetil, N. (2017). Elements of Entrepreneurially Minded Learning: KEEN White Paper. Journal of Engineering Entrepreneurship, 7(3), 17-25. Yadav, A., Subedi, D., Lundeberg, M. A., & Bunting, C. F. (2011). Problem-based Learning: Influence on Students’ Learning in an Electrical Engineering Course. Journal of Engineering Education, 100(2), 253-280.

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In: Mechanical Engineering Education … ISBN: 978-1-53617-791-6 Editor: Charles E. Baukal, Jr. © 2020 Nova Science Publishers, Inc.

Chapter 7

INTEGRATING OPEN-ENDED PROBLEMS IN ENGINEERING COURSES AT ALL LEVELS Matthew J. Jensen1, PhD and Kimberly Demoret2, PhD, PE 1

Assistant Professor of Mechanical Engineering, Utah Valley University, Orem, UT, US 2 Assistant Professor of Aerospace Engineering, Florida Institute of Technology, Melbourne, FL, US

Keywords: open-ended problems, active learning, design, project based learning

INTRODUCTION Real world engineering problems rarely, if ever, have a single correct answer. An automobile can be powered by a gasoline engine, a diesel engine, an electric engine, or even a combination of engine types. None of these solutions are incorrect; they can all provide sufficient power to move a vehicle from point A to point B. As a result, we often evaluate the

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different solutions based on a set of criteria, thus finding the best solution given constraints such as cost, size, weight, etc. However, the “best” solution can vary based on the person evaluating it, leading to multiple correct answers. Utilizing this type of approach, engineers can end up with sports cars, luxury cars, compact cars, trucks and sport utility vehicles being produced to fulfill the same basic requirement, travel from point A to point B.

Figure 1. The Porsche 911 Turbo (Porsche, 2019) and the Dodge Ram (FCA, 2019): two very different solutions to the problem of travel from point A to point B.

Mechanical engineering curriculums are heavily rooted in math and science, courses that at their fundamental level solve problems that have one single correct answer. Similarly, most traditional engineering courses heavily emphasize the analysis of existing systems and utilize problems with a single correct answer. Problems with single answers can be useful tools for students and instructors to gauge mastery of concepts, but the curricular emphasis on analytic skills can leave students poorly prepared when they encounter open-ended problems in higher level courses, capstone design, and in the workplace. As a result, open-ended problems may present challenges for students. There are different levels of ambiguity, complexity and choice associated with open-ended problems. In some cases, the requirements are clearly defined, but a design solution must be generated. In other cases, students may have considerable latitude in defining the problem itself. Classic engineering design is traditionally described as an iterative cycle of synthesis, analysis, and evaluation.

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Engineering students can struggle when they are asked to move beyond analysis of an existing system, but developing the design skills required to solve open-ended problems can lead to a greater sense of mastery, and are required by ABET for accredited engineering degrees. This chapter will discuss different types of open-ended problems, as well as how to implement and assess open-ended problems in courses of all levels. Detailed examples will be included to ease potential barriers to adopting open-ended problems for instructors of all levels and backgrounds.

TYPES OF PROBLEMS: INSPIRATION AND SOURCES OF OPEN ENDED PROBLEMS This section will describe five general categories of problems (Industry Problems, Philanthropic Problems, Competitions, Faculty Defined Problems, and Student Defined Problems), how to structure the problems for use in the classroom, and specific guidance for instructors. Table 1. Level of difficulty for integrating types of open-ended problems into various levels of courses

Curriculum Level

Introductory Fundamental Stand-alone Labs Upper-Level Senior Capstone

Type of Problem Industry Philanthro pic Moderate Low Moderate Low Moderate Low Moderate Low

Moderate Moderate

Competitio ns Low Moderate Moderate Moderate

Faculty Defined High

Student Defined High

Moderate

Moderate

High Low Moderate Moderate

Moderate Moderate

Low

Low

Moderate

Moderate

Moderate

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Industry Problems Industry problems, either directly sponsored or simply inspired by business enterprises, are commonly used in mechanical engineering courses. Industry problems are excellent for student motivation and are often easier for students to understand the problems’ objective and how the solution will be evaluated. Does the product meet the needs of the client? Is it cheaper or better performing than past products or the current competition? Although not necessary, industry problems work best when there is an industrial partner. Students are often highly motivated when they have a non-academic client, especially as they get close to graduation and are searching for jobs! Industry problems can be integrated into courses at all levels, so long as the problem is appropriately defined for the students’ knowledge and experience level. It is highly encouraged that the instructor work closely with any industrial partner to help define the scope of the problem to ensure the best possible experience. A more detailed discussion on how best to integrate open-ended problems in courses of all types in included later in the chapter. One of the most important aspects of industry problems is providing the students with a clear problem statement and set of requirements. It is common for industry representatives to use acronyms, testing procedures, policies, and other language that is well known within the company or industry, but unknown (and sometimes difficult to look up) to students and even instructors. One of the biggest challenges for students solving openended problems is understanding how to get started on the problem, and a poorly defined problem statement or set of requirements can exacerbate this issue (Jaeger-Helton and Smyser, 2017). One practical reason industry projects are most often seen in capstone design is that many real industry problems can be too complicated, difficult and time intensive to solve by younger students in one semester.

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Industry projects are commonly funded by the company that has defined the problem, which can be important if the problem includes prototype development and fabrication. Universities often reach out to corporate partners that are either geographically nearby and/or have open positions for new engineers. For example, a university near Kennedy Space Center may be sponsored by a space launch provider to design ground support equipment, while a school in Detroit might focus on an autonomous vehicle application. Though faculty may already have connections in the local business community, help in finding industrysponsored projects may also come through professional societies or university offices focused on research, corporate relations, development, alumni relations, or student career services.

Philanthropic Problems Philanthropic problems can take many forms, but most function similarly to industry problems without financial sponsorship. Philanthropic problems typically start with a basic need or issue that a community is facing. For example, a community might lack enough potable water, or have limited access to electricity or internet. Compared to industry problems, students will often have a larger role in defining the problem statement, scope of work and the evaluation process for philanthropic problems, providing the students with a greater sense of project ownership. For some students, this increase in responsibility and the philanthropic nature of the problem will increase motivation and interest in solving the problem. For other students, these attributes will act as a hurdle. As a result, philanthropic problems work best when used as an option for the given assignment, thus allowing the students to choose between the philanthropic problem or some other problem type. Philanthropic problems also present a unique engineering challenge to the students due to the increased constraints placed on viable solutions (cost, parts, environmental hazards, available support infrastructure, etc.). Often philanthropic problems are ones that have already been solved, but

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for some reason the common solution is not an option for the given situation. As a result, philanthropic problems commonly require more creativity and critical thinking in order to find a successful solution.

Design Competitions (and Grand Challenges) Design competitions and grand challenges (henceforth referred to as Competitions) like the 2004 DARPA Grand Challenge (DARPA, 2019) present great opportunities for well-defined open-ended problems. Competitions have a built-in advantage of clear requirements and a strong evaluation process, often including assessment of design, analysis, communication, and manufacturing if a physical model is within the scope of the competition. Competitions are also typically popular with students as they are able to showcase their work and compete against other schools or programs. More recently, Competition problems have become multi-disciplinary in nature, requiring teams of students from difficult majors and even colleges to work together towards a common goal. Obviously a multidisciplinary team better simulates a real-world design experience, and allows students to broaden the design space of their solutions. While there are many benefits to multi-disciplinary teams, significant challenges may arise when forming teams of students from different academic programs for class credit. Outside of common scheduling difficulties, academic programs can have very different credit requirements, course structure, and assessment requirements. As a result, multi-disciplinary Competition problems may work better as a co-curricular activity (more discussion on co-curricular activities in included later in the chapter).

Faculty Defined Problems Faculty open-ended problems are often more research oriented than the other types of problems described in this chapter. Research problems have

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some inherent advantages as well as difficulties. Advantages include covering current topics of interest to the field, utilizing state of the art technologies/equipment/methodologies, and engaging undergraduate students in research, which in turn can aid faculty in recruiting undergraduate and graduate students to their research teams. However, research problems can be challenging to include in a graded course simply due to the uncertain nature of research problems: solution(s), length of time needed to perform the work, and needed resources are all unknown. It is because of the unknowns that instructors should be careful when adopting research focused problems in their courses. Not all faculty Defined problems need be pure open-ended research problems. It is fairly common for faculty to have smaller, better defined problems that they have encountered in their research or consulting work, even problems for which they have already found a solution, that could be used in a course as an open-ended problem. While a solution may have already been found, the course instructor may challenge the students to find an improved solution based on criteria set by the faculty. Design teams can also be challenged to develop or improve laboratory equipment used in the curriculum or for research, such as upgrading a structures lab experiment or a wind tunnel test section. As with all open-ended problems, the course instructor should make sure that the problem is appropriately defined for the given course.

Student-Defined Problems Student-defined problems are most commonly seen at the two ends of the academic curriculum: either in first year classes or as senior design projects. In these venues, physical projects of different levels of sophistication are often created, typically in a team setting. Allowing students to define the scope of their own problems for an open-ended project can pose both unique challenges and opportunities. Research has shown that a level of autonomy can improve intrinsic motivation, and one might assume that students would be more invested in

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projects they helped to define (Niemiec and Ryan, 2009). On the other hand, students often underestimate the difficulty of a problem and overestimate what is possible, especially in capstone design. Studentdefined problems pose unique challenges for the instructors who must grade diverse student products. There is also the challenge of how to guide students in their discovery process; when the problem is defined by the students, the skills and knowledge required can go in unexpected directions, including areas outside the professor’s knowledge base. An instructor without past experience with student-defined projects may consider starting with a small project with modest expectations, or find a project that has been successfully implemented by others. One small education-themed project used in an engineering mechanics class is described at the end of the chapter. For large student-defined projects in senior design, instructors should encourage their students to take time up front to refine their idea, define the scope of the effort, and consider a “design thinking” approach. Design thinking is an approach to creative problem solving that stresses early prototyping and frequent engagement with prospective customers. Stanford University has extensive online resources detailing the method (Stanford University, 2019). An example assignment where design thinking has been used early in senior design is provided at the end of the chapter.

METHODOLOGIES FOR COURSE INTEGRATION Open-ended problems can be integrated into engineering courses at any level, so long as the problem is structured appropriately for the students in the course. While challenging students is important and can lead to excellent engineering work, giving students a problem they consider to be too challenging can be demotivating and counter-productive to student learning. It is critical to understand your audience and make adjustments to the problem in order to maximize the learning experience. This section will discuss general procedures for utilizing open-ended problems in a variety of levels and types of courses.

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First Year/Introductory Level Courses One of the biggest challenges with integrating open-ended problems into an introductory course is making the problem flexible enough for the typical wide range of knowledge and skill sets of the students. Instructors often face the largest skills and knowledge gap between individual students in first year introductory courses; however, even in programs that have strong university level prerequisites for their mechanical engineering programs, rarely do the prerequisites translate to students’ critical thinking skills, maker skills, and ability to synthesize knowledge from different disciplines. This large gap acts as the primary barrier for successfully integrating open-ended problems in an introductory course. For introductory level courses, it is important that open-ended problems aren’t too broad. The most common difficulty first year students will have with open-ended problems is where or how to begin solving the problem. Some students may have never been assigned a problem resembling an open-ended problem, so structure is needed in order to help focus the students and point them in the right direction. Smaller/shorter problems work well in class if you can dedicate the time, and are a good way to introduce the students to open-ended and design style problems. If class time is limited, an alternative to providing students with an open-ended problem experience is assigning problems for homework or longer out of class projects. When assigning problems out of class, make sure to plan on spending some time in class to provide students with milestones. It is critical to scaffold the introductory level students’ learning, and periodic milestones is a great way to accomplish this structure. When assigning open-ended problems either in class or out of class, utilizing teams can be an effective way to mitigate the large knowledge and skills gap previously discussed. When utilizing teams, try to make the teams as diverse as possible. Often, it works best to assign the groups yourself instead of allowing them to choose their own teams. For introductory level courses, it is important to have the problem focus on using creativity and modern engineering tools (micro controllers,

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3D printing, CAD, etc.), not just engineering analysis. If class/lab time is limited, it is not necessary to extensively teach how to utilize all the possible engineering tools that could be used to solve the problem. Rather, it is reasonable to simply introduce some of the tools the students may want to consider when attempting to solve the problem. Students are resourceful and the internet has many well documented resources to assist with these tools, so don’t feel the need to fully cover these topics. Be sure to provide the students with some appropriate online resources for the tool(s) you suggest they consider, knowing that the students are likely to find additional ones on their own.

Figure 2. Students working on a competition style problem in an Introduction to Engineering course.

When it is assessment time, it is important to recognize effort is equal to or more important than the final product. The students need to learn that failure in the classroom isn’t negative, and in fact can be a great learning experience. Because of this, it is important to challenge them or even allow them to choose their own level of challenge, but don’t over penalize students that shoot for the moon and fail. If it is a team assignment, utilize peer review between teams to allow the students to see how other teams approached the same problem.

Engineering Fundamental Courses Engineering fundamental courses like statics, dynamics, thermodynamics and fluids make up the backbone of many mechanical

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engineering curricula. These courses usually have lower average grades and higher failure and withdrawal rates than other mechanical engineering courses, oftentimes acting as barrier courses for students to obtaining their degrees. As a result, significant attention has been given to improving student performance in these courses, including utilizing open-ended problems (Hadim and Esche, 2002, Huang and Pierce, 2015, Rutz, Eckart, E. Wade, Maltbie, Rafter, and Elkins, 2003). Several common barriers exist for including open-ended problems in engineering fundamental courses. These courses have lots of material that must be covered in a limited amount of class time. Class enrollments are often higher than other mechanical engineering courses within the program, or many sections may be offered of the same course in a given term (possibly with multiple instructors). Because the courses focus on engineering fundamentals, engineering analysis is emphasized, and mastery of the material is demonstrated by finding the correct answer to all of the assigned problems. However, all of these barriers can be overcome while providing the students with an improved understanding of the covered material and better preparation for later courses. It can be difficult to find a significant amount of time to spend on an open-ended problem in an engineering fundamentals class, unless the course is “flipped” and the bulk of instruction occurs outside the classroom. If time is a constraint for an instructor, utilizing out-of-class problems may be the best option. Adding an extra homework problem periodically or even a longer multi-week project reduces class time concerns, while managing the time requirements for students to complete the problems. Because the purpose of open-ended problems is different from analytical problems (understanding and synthesizing versus analysis), instructors need not assign a large number of problems for course assessment purposes. Yet inclusion of even a limited number of openended problems will provide a meaningful learning experience and better preparation for later design-focused courses.

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Large Class Enrollments Class enrollment has a significant impact on the format and structure of a course, especially an engineering fundamentals course. A demonstration or problem session that works for a class of 20 may not be effective for a class of 300. While it may be more intuitive to include openended problems in small classes, it is still possible to include open-ended problems in larger classes while maintaining assessment of assignments. One of the easiest ways to manage large class sizes is to assign the openended problem(s) to groups rather than individuals. Groups of two or three students can significantly reduce the needed grading time without significantly reducing the student learning. If using group assignments, there are benefits to both allowing the students to select their own teams, as well as selecting the teams for the students. Regardless of the method used to assign teams, it is recommended that the groups change over the course of the semester if multiple assignments are to be given. This will reduce the impact of groups that don’t work very effectively. For problems that involve prototyping, large classes can pose a challenge due to limited space or fabrication resources. If possible, try to stagger the problems in shifts throughout the course so as to reduce the number of students needing resources at any given moment. It may also be possible to adjust the problem to focus more on idea creation rather than prototyping, without losing any of the educational value of the problem.

Continuity Across Sections and Instructors For courses that are offered across multiple sections in a given term, care should be given to standardize the learning environment as much as possible. Standardization is less of an issue if the same instructor is used for every section of the course. If different instructors are involved, instructors are encouraged to communicate regularly to aid in the standardization of what is covered and assigned.

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It is possible to utilize the same open-ended problems across sections, as cheating or plagiarizing is far less of a concern for problems that have no single correct answer. See (Demoret, Jensen, and Schlegel, 2018) for more information on how to standardize an engineering fundamentals course across multiple sections, multiple instructors, and multiple class formats.

Standalone Labs Standalone labs are a natural course format to include open-ended problems either in the dedicated lab time, or as an assignment outside of lab. The longer length of time for labs and typically smaller enrollment per section provides instructors an optimal environment to fully cover an openended problem. Labs typically have less structure to the material covered, so it may be easier to integrate an open-ended problem without disturbing the overall course objectives. Additionally, open-ended problems are very well aligned with typical lab objectives: deeper exploration of a concept, experimentation, accounting for unknown variables, and variations in solutions.

Upper-Level Courses Junior and senior mechanical engineering courses, referred to as upperlevel courses, can be one of the most impactful locations to assign openended problems. Upper-level courses are designed to build upon prerequisite course(s) and require the students move beyond simple analysis and start to utilize critical thinking skills, synthesize material from previous courses, and answer more complex problems utilizing advanced engineering techniques.

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While typical upper-level courses continue to utilize analytical problems to gauge student performance, assessment can be supplemented by assigning individual or group open-ended problems. Utilizing group problems is a great technique for handling time limitations (re: assessment) of large class enrollments, but can also be a useful assessment tool for ABET learning objectives. Unlike open-ended problems in intro-level courses, problems used in upper-level courses can be less formally structured, requiring the students to act more as practicing engineers rather than inexperienced students. Similar care should be taken when assessing student performance by focusing more on how the problem is approached and the methodology taken to solve the problem, rather than on the final solution. Open-ended problems can also be useful for having the students utilize more advanced engineering tools such as computational tools or rapid prototyping, which may even increase student motivation for completing the problem.

Senior (Capstone) Design Senior design classes typically require seniors to work in teams to solve one or more open-ended problems, with the goal of preparing students to be practicing engineers. What makes these open-ended problems unique in the curriculum is their complexity and duration; the senior design sequence often consists of multiple classes that span the student’s entire senior year. Problems can be industry proposed, competition based, faculty defined, or student proposed, but in most cases there is either a real-world customer the students must consider or a clear set of objectives the product must meet. Many real-world mechanical engineering projects also include electrical and computer science elements, so teams may need to include students from other disciplines or somehow obtain that knowledge themselves. Because of the complexity of these projects, instructor suggestions for implementing open-ended problems in senior design are provided below.

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Consider the Student’s Previous Experience with Open Ended Problems and Team Projects; Scaffold as Needed Many students struggle in senior design, especially if the earlier curriculum did not include many open-ended projects that developed experience with requirements definition and synthesis. Senior design also requires self-management, teamwork, scheduling and other “professional skills” to a level that can surprise many students, and the learning curve for these areas may be particularly steep. Some programs include one or more smaller “practice” projects to scaffold the student’s learning, such as a preliminary “Junior Design class” or a project at the beginning of senior design. Another option is to have students do an introductory assignment to jump start their learning on the project, such as the design thinking exercise described at the end of the chapter.

Consider Student Preferences and Motivation When Forming Teams and Assigning Projects Senior design projects require a sustained time commitment, students often lose enthusiasm as graduation approaches, and grades may provide less incentive if they have a job offer. Considering student preferences when teams are formed and topics are assigned may help to maintain intrinsic motivation and engagement until completion of the project. Instructors often identity the project options, then allow students to identify and prioritize which topics they wish to work on. Industry-sponsored projects can help motivate students to stay engaged because they address a real-world need and can increase the chances of employment after graduation (either by the sponsoring company or by another company that values the real-world job skills demonstrated by the project). Other students may be highly motivated by competitions or by a project that helps others in the community. Because motivations will vary, giving the student an element of choice will be highly impactful. Instructors may

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allow students to request an assignment with a teammate if that preference is stronger than their topic preference and the same holds for those who wish to avoid a particular classmate.

Take Measures to Reduce “Social Loafing” “Social loafing” or “freeriding” describes the situation where some members of a team fail to contribute their fair share of a project. It is a common problem in senior design and becomes a greater issue in larger teams (Borrego, Karlin, McNair and Beddoes 2013). Some measures to limit social loafing:  



Limit team size to the minimum possible required to do the job. Include peer feedback in the grading criteria. Peer feedback can be a valuable tool for students to reflect upon critical professional skills. There are several automated systems available, including CATME and TeamMates. For large teams and classes, consider requiring students to account for their hours (Demoret, 2019).

Follow a Structured Design Process Open ended problems in Senior Design provide an ideal opportunity to emphasize systems engineering and design processes, especially the importance of requirements. Instructors may use a design process introduced in an earlier class, a course textbook, or one relevant to a major employer in the local area. For example, a school with strong ties to aerospace engineering might consider using the NASA Systems Engineering Handbook (NASA SP-2016-6105), a freely available online resource. Regular Meetings and Intermediate Milestones Periodic face-to-face meetings with an authority figure can maintain momentum and keep senior design teams moving toward successful

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project completion. Weekly meetings with an instructor or advisor are desirable, but may not be practical for many large schools. Bi-weekly meetings may be a good alternative, especially if industry partners are involved. Intermediate milestones can also be used to keep students on schedule. Additionally, utilizing approaches such as Agile Project Management may aid students in developing and reaching milestones throughout the course (Kremer, Juratovac, and Smith, 2018).

Don’t Ignore Teamwork and Professional Skills – and Realize That There’s Help Available Project management, communication, scheduling, teamwork, and other professional skills are just as important as technical skills in the success or failure of a capstone project, and are typically part of the ABET criteria for the class. ABET student outcome 5 requires “an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives.” One way to address teamwork skills is to expose students to a relevant reading or lecture, then require them to write a short reflective essay that can be used as support for an ABET review. Engineering instructors who feel uncomfortable speaking about these topics directly can often find useful resources elsewhere in the university. Faculty in institutional/organizational psychology may be willing to be guest speakers; business schools may provide resources; and staff organizations like Student Life or Career Services may have existing training programs that could be useful. Instructors who teach senior design must tailor their approach to the unique features of their college or university. In some ways class management is its own open-ended problem with many active constraints and no one right answer. Though challenging, senior design courses can also be enormously rewarding because of the value the students receive by working through these complex, realistic problems.

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Open Ended Projects as Co-Curricular Enrichment Many universities encourage students to work with open-ended problems outside the curriculum. Co-curricular activities can help universities challenge, engage, and retain academically gifted students, while still giving average students a chance to be successful in their required classes. Often student clubs are formed to give highly motivated students a chance to apply their skills to design competitions or grand challenges, even as first year students. Academic makerspaces provide a forum for students to build technical skills and exercise creativity through personal projects. If students work together on extracurricular open ended projects, they will be better prepared to function well on team projects within the curriculum. Co-curricular activities can reinforce and supplement classroom learning, give students opportunities to build their resumes, and better prepare them for their and future work.

CONCLUSION Integrating open-ended problems into a mechanical engineering curriculum comes with many challenges. However, open-ended projects can augment the traditional curriculum and make an enormous difference in the skills and confidence of engineering students, thus improving student learning, retention of concepts, and persistence within the engineering major. Before starting an open-ended problem, instructors should be clear on the learning objectives of the project and how student work will be evaluated. Is the focus on “process” or “product”? Must the student’s solution fully meet all requirements? What constitutes success? If this is a team product, does everyone get the same grade? Additionally, cultivate a “growth mindset” and keep a sense of humor. The “growth mindset”, first defined by Dr. Carol Dweck (Dweck, 2007), advocates that intelligence and ability are not fixed quantities; students who believe that they can get smarter and stronger with effort are more

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motivated and likely to eventually succeed. If an open-ended problem is seen as a learning opportunity, not a judge of their future worth as an engineer, it can be a motivational and positive experience, even if not all requirements were met.

Example Problems Introductory Level Course – Design Competition MiniGolf at Panther Links: A General Engineering Design Project

OBJECTIVES MiniGolf at Panther Links will provide you with a hands-on design experience, while working in a group of your peers. You will be assigned to a group of three students to design and build your very own mini golf hole. Your hole will be a part of an overall MiniGolf course that will challenge golfers to combine chance, putting skill, and geometric analysis while demonstrating fundamental engineering principles. MiniGolf at Panther Links will be made available for campus and the public to play, so you must account for difficulty (you will define your hole’s ‘par’), aesthetics and most importantly build quality. Your team’s mini golf hole will be judged on the following criteria:     

(20%) Construction quality (determined by judges) (30%) Aesthetics and fun-ness (as voted on by the participants) (20%) Accurate CAD drawing of the design and/or major feature(s) (30%) Final presentation and write-up Penalties: o (10%) If you run over budget (based on receipts submitted for reimbursement)

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(2%) Every half stroke above/below your designated par based on the average score of participants (30%) If you do not utilize a 3D printed part and/or microcontroller in your final design

LEARNING OUTCOMES MiniGolf at Panther Links is a semester-long design project that is intended to challenge first year students engineering students of all majors. By completing this project, students will demonstrate:     

Working in an interdisciplinary group Both verbal and written communication skills Design-related problem solving skills including product innovation and an entrepreneurial mindset Knowledge of their chosen major and of other engineering major(s) Construction skills

PROJECT THEME This year’s MiniGolf at Panther Links will have a set theme for all golf holes: Countries of the World. Each hole must be in some form or fashion related to the overall course theme. Each group will randomly pick a region of the world wherein they must select a country that lies on that region. Regions maybe represented by multiple groups, but each group must select a unique country. You are encouraged to use your creativity in designing your hole, but make sure you account for what you are capable of constructing in a quality manner. DESIGN REQUIREMENT Your mini golf hole must meet the following requirements: 

Include at least one 4” golf hole cup (one is supplied, any additional count towards your budget)

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Conform to this years course theme Fit within a 4’ x 8’ footprint (frame materials will be supplied) Must have a specified par less than or equal to six (six being the max score for a mini golf hole) Must be transportable to the designated location (Hole locations TBD) Must include at least one (1) 3D-printed part that is at least one cubic inch in size Must include a microcontroller that performs at least 1 task Must incorporate at least one feature from three different majorrelated categories, you are free to propose your own example for approval: o Mechanical/Aerospace Engineering  Moving object(s) (i.e., windmill, moving ramp, etc.) o Civil Engineering/Construction Management  Bridge  Tunnel o Electrical/Computer Engineering  Electronic ____ (i.e., lights, videos, cameras, etc.) o Computer Science/Software Engineering  Computer controlled ____  Automatic scoring o Ocean Engineering/Marine & Environmental Sciences  Incorporate water features (i.e., pond, beach, waterfall, etc.)  Use of a boat or other water craft o Chemical Engineering  Unique use of material properties (i.e., plastic flexibility, low density of foam, etc.) (must be repeatable for all participants) o Biomedical Engineering  Incorporate a biomedical device (i.e., EKG, etc.)

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MATERIALS You will be supplied with the following materials:    

One sheet of plywood, 4’x8’ in size Four 8’ long 2x4’s 4” mini golf hole cup with flag ~32 sq. ft. of AstroTurf for putting surface

You are welcome to use any additional materials. Your team will have a budget of approximately $100 for which to purchase any additional materials. The machine shop has many odd sized and extra pieces of many kinds of materials that you are encouraged to utilize.

TIMELINE August 28th: October 4th: November 29th: Nov. 30th & Dec 1st: campus December 2nd: Discovery Day visitors December 15th:

Form teams, receive design guidelines Preliminary design report due Final day for construction MiniGolf at Panther Links open to FIT MiniGolf at Panther Links open for Project Presentations

DELIVERABLE Besides having a completed mini golf hole for inclusion in the MiniGolf at Panther Links golf course, every group is required to submit a preliminary design report, final design report, and final design presentation. The report should include:   

Introduction Design Overview including major-related, 3D printed, and microcontrolled features that will be included Outline of task assignments for each group member

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Face-to-face interviews of at least 15 ‘users’ (fellow students, staff, teachers, etc.) and community mini golf players for opportunity discovery. You are allowed a maximum of 5 fellow student (college) interviewees. Be sure to create and include the questionnaire used for the interviews. Note: These interviews should be used early in the design process to help drive brainstorming, not used to confirm a design you have already come up with. Proposed timeline Budget containing purchase information for all necessary components

The initial design report is intended to confirm the feasibility of your design and to provide enough time for construction. The final report is limited to 5 total pages, and should include:        

Introduction Design overview Final budget Discussion on the 10 stages of the design process Discussion of what was learned through benchmarking and interviewing activities and what impact that had on your design Discussion of the impact your mentor had on your project CAD drawing(s) of two or more features of your hole (one being the 3D printed part) Lessons learned and conclusions

The project presentation should be no less than 5 minutes and no more than 10 minutes in length. The presentation should be based on your final design report and include visuals of your finished product. Every group member must give a portion of the presentation.

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STATICS Team Project STATICS PHOTO SAFARI and TEAMS TEACHING STATICS Students should self-organize in 3-person or 4-person teams for this exercise. SUBMISSION REQUIREMENTS Students must upload their document in CANVAS. Either a MS Word file or PDF is acceptable. Students may include hand sketches as part of the file.

PART 1: STATICS PHOTO SAFARI (40 points) 1.A. SUPPORT REACTIONS Students must take the photos themselves. Complete the following: Photograph examples of support reactions found on the Florida Tech campus or in the local area. Three-person teams must complete six support reactions; four-person teams must complete eight. Please identify whether you are showing a 2-D or a 3-D support reaction. See Table 5.2 for examples of support reaction types. At a minimum, the following support reactions must be included: 1. 2-D Fixed Support 2. 2-D Pin Joint 3. 2-D Roller/Rocker/ Smooth Contact 4. 3-D Fixed Support 5. 3-D Hinge 6. Journal or Thrust bearing (specify which one)

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Each support reaction example should include the following: 1. Name of support reaction (also identify 2D or 3D), and forces and moments associated with the support reaction. Example: 2-D Fixed Support: forces Fx, Fy, and moment Mz Example: 3-D Fixed Support: forces Fx, Fy, Fz; moments Mx, My, Mz 2. Photo and corresponding sketch of support reaction showing related forces and moments (include a coordinate system) 3. Identify location of object and who took the photo and created the sketch Example: Statue of president at the quad Photo credit: _____ Sketched by: _______

1.B. FRAMES AND MACHINES a) Photograph a simple frame or machine found on the Florida Tech campus or in the local area. b) Draw a separate free body diagram of each member of the frame. Follow the standard conventions for a free body diagram: label support reactions, applied forces, angles, and dimensions. Angles and dimensions can be estimated or given labels like L1, L2, and L3… Remember that objects in contact experience forces that are equal in magnitude and opposite in direction. 1.C. INTERNAL FORCES: TENSION, COMPRESSION, BENDING AND TORSION Provide four photograph of objects subjected to internal forces: (1) (2) (3) (4)

Tension Compression Bending Torsion.

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Briefly describe in words the applied forces that cause the internal forces.

PART 2: TEAMS TEACHING STATICS (40 points) Teams must create a product that can be used to help explain a concept covered in MAE 2081: Engineering Mechanics: Statics. Teams then have to use the product with someone outside the team, then report their results.

1. Create a product. Here are some possible examples  

 

A physical object to be used for a visual aid / demonstration tool. A short video (less than 3 minutes) with a demonstration that illustrates a concept (any video must have a demonstration, not just a lecture) A software program or simulation. Other student-proposed ideas approved by the instructor.

Students are free to look for ideas on the internet, as long as the actual product created is their own work. For example, a student-created video or demonstration tool that is similar to something online is acceptable, but a video copied from the Internet is not. Different teams may share ideas, but each team must have a unique product.

2. Use your product and document the event. Use your product to explain the statics concept to someone outside the team (it can be another student in the class, a roommate, a friend- anyone you choose.) Take at least one photo of your product (If you’ve produced a video, upload the link) Take at least one photo of the team using your product.

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3. Write at least 3 paragraphs about “Teams Teaching Statics” Describe your product and what statics concept it was intended to explain. Describe the process of using your product. Describe something you learned as part of doing this exercise. Upper-Level Course – Industry Problem Cutting Tool Design Project

OBJECTIVES You have just been hired at a leading aerospace firm. The first project you are to work on is designing a cutting tool for use in space. The cutters must be as lightweight as possible, be able to cut a 1 in. diameter hose by applying a 100 lbf normal force at the blade, and fit in a 12 in. x 4 in. x 2 in. box. You are free to use any design that meets those constraints. Your team (you and two partners) is competing against other teams to supply the lightest and most cost effective design. Your design will be graded on: 

  

Accurate analysis of the design - 50 pts. o Dimensioned CAD drawings o Appropriate ANSYS analysis figures/documentation Total Cost – 25 pts. Total Weight – 25 pts. If you submit a prototype (3D printed, metal or wood) of your design you will receive 3% extra credit

Points for cost and weight will be broken down as follows:    

25 pts – top 20% of groups 23 pts – top 21% - 40% of groups 20 pts – top 41% - 70% of groups 16 pts – top 71% - 95% of groups

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12 pts – bottom 5% of groups 0 pts – no documentation

Material properties: - Structural Steel: - Aluminum Alloy - Magnesium Alloy - Your own material* - Titanium Alloy

$10/lbf. $25/lbf. $35/lbf. $45/lbf. $50/lbf.

*Material must be an actual material that already exists. If you choose a composite, you must perform an accurate composite analysis, which is much more complex than what we have covered

DELIVERABLES: Design report - Describe your design including the expected cost and weight - Maximum of 7 total pages including a cover page and up to 1 page of appendices DESIGN REQUIREMENTS: The cutting tool must -

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Be capable of applying a 100 lbf normal force onto a 1 in. diameter pipe using no more than a 10 lbf applied force (i.e., P = 10 lbf on Figure 1) Fit within a 12 in x 4 in x 2 in box when fully assembled Require no more than two hands to operate in a zero gravity environment Have no more than a 15 degree angle between the jaws when the 1 in pipe is placed between the jaws

Senior Design Course – Faculty Defined Problem Lionfish Remediating Invasion Device Since their introduction in mid 1980’s, the invasive lionfish have caused significant damage to the marine environment in the Atlantic,

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Caribbean, and parts of the Gulf of Mexico. To reduce the lionfish population, numerous removal methods have been attempted, but none have been truly effective in curbing the population boom. We want to develop an automated remediating device similar to a lobster trap, but tailored to lionfish. The device must operate for a period of up to one week or until approximately 30 adult lionfish have been captured, at which point the device is brought up to the surface for maintenance. The device must operate at saltwater depths common for lionfish activity, up to 100 ft. The device must be able to identify lionfish with near certainty and safely release any non-lionfish creature or object back into the open water. The device must be economically feasible, environmentally safe, and scalable so as to be deployed where ever lionfish have been found to cause harm to the natural ecosystem. Senior Design - Student-Defined Problem (also adaptable for other problem types) Design Thinking Exercise

EXERCISE OBJECTIVES Get to know your team. Seek insight on your capstone topic area using the “design thinking” process Gather data by conducting interviews with knowledgeable faculty, staff, customers, and other stakeholders or authorities. Design thinking is a methodology for creative problem solving that emphasizes early contact with people in order to understand and define the real problem. Rough prototypes or “mockups” are used to stimulate conversation. Additional references: YouTube Video: “What is Design Thinking (2015)” Website: https://dschool.stanford.edu/resources/getting-started-withdesign-thinking

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SUMMARY OF ACTIVITIES Meet your team, discuss the project, and together build a “mockup” (rough prototype made of craft materials) of your project as you currently understand it. Take pictures of the build process, and plan interviews to get feedback on your ideas. Over the next week, conduct at least three interviews using the mockup to get feedback on the project. Take pictures documenting the interviews. All team members must actively participate in (and be photographed at) at least one interview. After the interviews are complete, as a team, discuss what you learned from the interviews. In the meeting, write insights on sticky notes, post them on a whiteboard, and look for patterns. Take a picture of this activity. DELIVERABLE As a team, complete a 3-5 minute outbrief to the class using PowerPoint slides that contain photos of the build process and interviews. Slides should include One or two pictures of the prototyping process For three different interviews- have one picture of the team and interviewee holding interacting with the prototype. Photo of team reviewing data and looking for patterns (full team photo preferred) Brief bullet summary on what was learned- either about their project or about the exercise itself- the prototyping/interview/feedback activities) Put names of the interview team on the slides to get credit for the assignment.

REFERENCES Borrego, M., Karlin, J., McNair, L. D. and Beddoes, K. (2013), Team Effectiveness Theory from Industrial and Organizational Psychology

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Applied to Engineering Student Project Teams: A Research Review. J. Eng. Educ., 102: 472-512. doi:10.1002/jee.20023. DARPA, 2019, https://www.darpa.mil/about-us/timeline/-grand-challengefor-autonomous-vehicles. Demoret, K., Jensen, M., Schlegel, J., 2018. Standardizing the Statics Curriculum across Multiple Instructors. Proceedings of the 2018 ASEE Annual Conference, https://peer.see.org/30980. Demoret, K., 2019. Using Team Time Cards to Encourage Accountability in Senior Design. Proceedings of the 2019 ASEE Annual Conference, Paper ID #25350 https://peer.asee.org/33519. Dweck, C. S. 2007. Mindset: the New Psychology of Success. Ballantine Books. FCA, 2019, https://www.ramtrucks.com/ram-1500.html. Hadim, H. A., Esche, S. K., 2002. Enhancing the engineering curriculum through project-based learning. Proceedings of the 32nd Annual Frontiers in Education, https://doi.org/10.1109/FIE.2002.1158200. Huang, S., Pierce, E., 2015. The impact of a peer learning strategy on student academic performance in a fundamental engineering course. Proceedings of the 2015 IEEE Frontiers in Education Conference, https://doi.org/10.1109/FIE.2015.7344044. Jaeger-Helton, K., Smyser, B., 2017. Switching Midstream, Floundering Early, and Tolerance for Ambiguity: How Capstone Students Cope with Changing and Delayed Projects. Proceedings of the 2017 ASEE Annual Conference, ASEE Paper ID #18699 https://peer.asee.org/ 28895. Kremer, G. G., Juratovac, J., & Smith, J., 2018. Sprint+PDCA Approach to Improving Project Management Skills and Mindset in Capstone Design, Proceedings of the 2018 Capstone Design Conference, http://www.capstoneconf.org/resources/2018Proceedings/Papers/Krem erEtAlCDC18.pdf. Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, competence, and relatedness in the classroom: Applying self-determination theory to educational practice. Theory and Research in Education, 7(2), 133– 144. https://doi.org/10.1177/1477878509104318.

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Porsche, 2019, https://www.porsche.com/usa/models/911/911-turbomodels/911-turbo-s/. Rutz, E., Eckart, R., E. Wade, J., Maltbie, C., Rafter, C. and Elkins, V. (2003), Student Performance and Acceptance of Instructional Technology: Comparing Technology‐Enhanced and Traditional Instruction for a Course in Statics. Journal of Engineering Education, 92: 133-140. doi:10.1002/j.2168-9830.2003.tb00751.x. Stanford University, 2019, https://dschool.stanford.edu/resources/gettingstarted-with-design-thinking.

In: Mechanical Engineering Education … ISBN: 978-1-53617-791-6 Editor: Charles E. Baukal, Jr. © 2020 Nova Science Publishers, Inc.

Chapter 8

HANDS-ON DESIGN IN MECHANICAL ENGINEERING EDUCATION Matthew Cavalli1 and Dustin McNally2 1

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Western Michigan University, Kalamazoo, Michigan, US University of North Dakota, Grand Forks, North Dakota, US

Keywords: hands-on design, active learning, design thinking, engineering thinking

INTRODUCTION Most mechanical engineering graduates will spend a majority of their careers in jobs that require them to regularly apply the concept of design, i.e., to identify a set of requirements that need to be met and then to specify a process or product that can satisfy them. While the concept of design is straightforward in theory, the implementation can be quite complex and a wide variety of approaches to design exist.

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How best, then, to teach the skill of designing to undergraduate mechanical engineers? This chapter will explore the advantages of handson design experiences in the mechanical engineering curriculum and provide ideas for how best to support the professional development of novice designers. For our purposes, ‘hands-on’ is assumed to be a process in which students are expected to create and test or evaluate one or more physical models or prototypes, in addition to any paper- or computer-based analytical work.

DESIGN AND DESIGN THINKING Engineering design is a systematic process of problem-solving with the aim of meeting a client’s needs within a specified set of constraints. The constraints can be technical, such as the 2nd Law of Thermodynamics, but they can also be economic, legal, or ethical. Dutson and co-workers present a thorough overview of how both the concept of engineering design and the teaching of capstone design courses have evolved over time (Dutson et al. 1997). Lande and Leifer separate the intellectual processes needed throughout the design process into two parts – design thinking and engineering thinking (Lande and Leifer 2009a, 2009b). Design thinking preserves ambiguity and focuses on concepts, characterized by Dym as the ability to (Dym et al. 2005):      

tolerate ambiguity that shows up in viewing design as inquiry or as an iterative loop of divergent-convergent thinking; maintain sight of the big picture by including systems thinking and systems design; handle uncertainty; make decisions; think as part of a team in a social process; and think and communicate in the several languages of design.

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Engineering thinking follows when the concepts are implemented as functional systems and the ambiguity/uncertainty is reduced as much as possible. A successful engineering design process typically starts by identifying the problem to be solved and the associated constraints. Preliminary solutions are identified and their ability to meet performance goals evaluated. Models are constructed and tested with the results informing additional design iterations. Both performance goals and constraints may evolve as the process continues. Ideally, an optimal solution is identified through repeated cycles of idea generation, analysis, modeling, and testing.

THE ROLE OF PROTOTYPING IN DESIGN THINKING The left-hand column of Figure 1 illustrates a traditional approach to design in which physical modeling and prototyping follows the selection of a preferred design based on engineering analysis (during the ‘engineering thinking’ portion of the process). The right-hand column shows how additional modeling/prototyping can be integrated earlier in the process (in the ‘design thinking’ steps) (Lande and Leifer 2009b). Elverum and Welo surveyed a number of automotive companies and found that extensive prototyping at all stages of design process is critical for project success, including the ‘fuzzy front-end’ – the very earliest stages of developing a new product (Elverum and Welo 2014). This compares to the results from Deininger and co-workers who evaluated the role of prototyping in designs by engineering students with limited design experience compared to professional design engineers. They found that engineering students with limited experience used prototypes and models less frequently than more experienced designers, especially early in the design process when multiple alternatives were being explored. Their process tended to look more like the left-hand column with prototypes being used primarily for confirmation of a design rather than to inform all stages of the design process. The use of prototypes and models also tended to be less systematic for the less experienced designers (Deininger et al. 2017). Lauff found similar

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differences between students and practicing engineers with respect to their perceptions and utilization of prototypes (Lauff et al. 2017). The ability to effectively apply modeling and prototyping is a learned skill that depends not only on the designer’s technical knowledge but also their experiences with the design process itself. For example, Atman and co-workers compared the performance of first-year and senior students on a problem related to the design of a playground. They found that both groups spent a similar amount of total time in developing their design but that the seniors tended to spend more time on generating and evaluating alternative solutions compared to first-year students who spent a larger portion in understanding customer needs and design constraints (Atman et al. 1999). Jang and Schunn compared the design process and project outcomes for 43 design teams in a product realization course. They found that teams who utilized ‘collaborative’ design tools (smart boards and prototypes) throughout the design process produced much more successful designs than teams who did not use prototypes or waited to use them until late in the design process. Early prototypes used by successful teams tended to be, ‘…only a piece of raw material or a small part of the candidate design, that is, something simple and abstract,’ and models increased in complexity throughout the process (Jang and Schunn 2012). Viswanathan and Linsey found that the use of physical prototypes did not result in ‘design fixation’ (focusing strongly on a single design early in the process) but instead allowed students to supplement their mental models, ultimately leading to higher quality (i.e., functional) ideas (Viswanathan and Linsey 2010). Youmans similarly observed 120 students in a design setting and found that those who made use of physical prototypes during the design process created better final designs that were less fixated on the original product example than those who did not use physical prototypes during the design process (Youmans 2011). Brereton and co-workers have found that the use of prototypes facilitates conceptual learning of engineering fundamentals in addition to benefits for the design process itself (Brereton 1999, Brereton and McGarry 2000). However, they also found that when faced with a disagreement between the behavior of a prototype and a theoretical

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prediction, students were more likely to discard the theory than disregard their physical observations. Thus, oversight of the interpretation of prototypes by experienced personnel can be important to their success (Brereton and McGarry 2000). Jensen found that the use of prototypes throughout the design process in companies (or ‘prototrials’), can help to identify unknown unknowns (Jensen et al. 2017). Comparing this to the results of Brereton, it appears that a key difference in the use of prototypes and physical models is that an experienced designer may interpret unexpected results from a model as pointing towards an incomplete understanding whereas inexperienced designers may be more likely to conclude the theory is completely wrong.

Figure 4. Traditional approach to design (left-hand column) in which prototyping follows the completion of main design process. Opportunities for integrating hands-on design earlier in the process (right-hand column) to increase solution quality (Lande and Leifer 2009b).

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Fabrication of physical models takes time, time to locate the appropriate materials and time to fabricate and construct the required assemblies. Neeley and colleagues required student design groups to fabricate different numbers of prototypes in the same amount of time. Groups required to produce more prototypes reported more dissatisfaction with the time constraints and less satisfaction with their initial model attempts. However, by the end of the project, these groups showed significantly greater improvement and higher overall project quality than groups that were required to produce fewer prototypes. The authors hypothesized that the production of more, lower quality prototypes initially helped group members make abstract concepts concrete and prevented the group from zeroing in on a single design approach too quickly (Neeley et al. 2013). This is consistent with results from Lauff who studied the use of prototypes at three companies and determined they can serve three primary roles throughout the design process: 1) to enable communication, 2) to aid in learning, and 3) to inform decision-making (Lauff et al. 2013). Houde and Hill argue that prototypes are not inherently significant but that the way they are used by designers to ‘explore or demonstrate some aspect of the future artifact’ gives them value (Houde and Hill 1997). Prototypes that capture key design features can be used to understand user preferences and test assumptions about functionality.

EFFECTIVE PROTOTYPING STRATEGIES Camburn and co-workers present a flowchart-based prototyping strategy that helps students identify key considerations at each step of the process. Their approach focuses more strongly on fully functional prototypes near the testing phase of design rather than those to be used early on in the process for concept validation. They also limited evaluation of the final prototypes to whether or not they achieved a binary objective (e.g., hit a target or not) (Camburn et al. 2013). Menold and colleagues present a framework for prototype design they term ‘Prototype for X’ where the X is one of the three lenses of human-centered design (HCD):

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desirability, feasibility, and viability. For each ‘X’, designers must frame (define context, needs, and goals), build, and test a different prototype. Comparisons between students in a junior-level design class who followed the PFX approach versus those who ‘prototyped in the wild’ showed that PFX designs tended to produce higher quality prototypes across four metrics: 1) technical quality, 2) critical part count ratio (total number of parts compared to the theoretical minimum number of parts), 3) user satisfaction, and 4) perceived user value (Menold et al. 2017). Christie and co-workers present a list of nine considerations that can be used to help designers decide how to effectively integrate prototyping into the process. The factors are:         

Prototypes can be of a single system, of a set of subsystems, or of the entire system Prototyping multiple concepts in parallel vs. prototyping only a single concept Iterative prototypes vs. only one prototype per concept Prototypes can be virtual (analytical, CAD, FEA, CFD, etc.) or physical Prototype manufacturing can be outsourced, rapid prototyped, or completed in-house Prototypes can be physically scaled Prototypes can be functionally scaled Prototypes can use similar or different materials than the final design Prototypes can use similar or different manufacturing and assembly techniques than the final design

With regards virtual prototypes, the authors point out that while computer analysis and CAD renderings of designs are beneficial for development and production, physical prototypes are often indispensable for soliciting meaningful feedback on potential designs. They also presented a series of 13 questions to systematically guide students through relevant questions when producing prototypes. The authors note that their

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process is focused on late-stage prototypes ready for testing before final production (Christie et al. 2012). Many of the same considerations apply regardless of when in the design process the modeling/prototyping occurs. Dunlap et al. present a heuristic method for helping students determine the number, type, and characteristics of prototypes to be used for a given design. Students respond to Likert-scaled statements in six categories:      

Number of design concepts to simultaneously prototype Number of iterations for each concept Scaling Subsystem isolation Relaxation of design requirements Physical vs. virtual models

In the category of ‘number of design concepts to simultaneously prototype,’ for example, the statements include:   

There are sufficient materials to prototype multiple concepts There is sufficient time to prototype multiple concepts Rankings of several concepts are very close (e.g., from a Pugh chart)

The heuristic provides inexperienced designers with suggestions about how to implement prototyping into their average responses in each category (Dunlap et al. 2014).

HANDS-ON DESIGN IN THE CURRICULUM Froyd and colleagues provide an overview of how engineering education has evolved since the early 1900s. They observe how the degree to which engineering curricula include design and hands-on design has waxed and waned as the profession has changed. Froyd finds that design

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experiences are currently fairly common at the freshman and senior levels (Froyd et al. 2012). For many students, there is a lack of hands-on design experience in the sophomore and junior years. The National Academy of Engineering (NAE), in their report, Educating the Engineer of 2020: Adapting Engineering Education to the New Century, specifically highlights the importance of teaching, “…the iterative process of designing, predicting performance, building, and testing…,” throughout the engineering curriculum, starting with the first year (National Academic of Engineering 2005). Below, we will discuss how hands-on design has been successfully integrated throughout the mechanical engineering curriculum at the University of North Dakota and how this can improve student outcomes.

Figure 5. Methodology for implementation of reverse engineering as hands-on design projects. The details at each stage can be tailored to student background and abilities, as needed (Wood et al. 2001).

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Reverse engineering can be an excellent way to introduce novice designers to design. By asking them to observe and reflect on existing designs, students can learn connections between concepts like fluid flow, material strength, heat transfer, etc., and their practical implications. Wood et al. describe using reverse engineering as a gateway to teaching design. Students proceed through a process of predicting an existing device’s internal design based on its intended usage and other observations. They gather user information about shortcomings in current performance. Students then disassemble the device and apply their design skills to creating a more functional device. The same general approach has been used at the freshman through graduate levels, with the primary difference being the sophistication of the design process the students were expected to follow (i.e., junior/seniors/graduate students were familiar with Quality Functional Deployment and could use it whereas freshman typically were not) (Wood et al. 2001). Figure 2 shows a slightly modified version of their reverse engineering methodology.

DESIGN THROUGHOUT THE CURRICULUM – THOUGHTS AND EXAMPLES The mechanical engineering curriculum at the University of North Dakota (UND) includes at least one hands-on design-build-test experience each year, starting with the student’s first semester on campus. The scope and complexity of the projects increase as students progress through their degree program. This section presents observations and advice gleaned from the authors’ experience with this approach. Integrating hands-on design experiences into the curriculum requires a balance of time, resources, and expectations. Creating a functional prototype can be an educational and rewarding experience for engineering students. But a successful outcome depends largely on the how well the instructor has matched the project constraints to the abilities and resources of his or her class. A design experience that is poorly planned with unclear expectations or insufficient resources will lead to unhappy students, low

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instructor evaluations, and unmet educational outcomes. For any hands-on design project, the instructor should pay careful attention to the following considerations when developing the requirements: 

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Educational Objectives - What are the educational objectives to be achieved by the project? A clear understanding of why students are doing the project will make it much easier to layout the steps for how the students will achieve that goal. Student Background - What are the expected knowledge and abilities of students in the class? What other classes in the curriculum will most students either have completed or be taking concurrently? Careful selection of project team members (assuming a team-based project) can help balance individual student deficiencies, but the instructor must have a reasonable understanding of the overall class level and skills. Project Timeline - What is the project timeline? How do deliverable deadlines align with other classes students are likely to be taking? How can checkpoints be established to minimize procrastination and encourage progress? Budget and Resources - What is the available budget? Will tools and materials be provided to the students or are they expected to provide their own? What other resources are available (design/build space, shop technician assistance, etc.)?

EDUCATIONAL OBJECTIVES Having clearly defined educational objectives is critical for a successful hands-on design project. The instructor should be able to articulate the value of the project in the students’ overall mechanical engineering education. The instructor should also be able to place the design experience in the context of both students’ current skills and knowledge as well as the skills and knowledge students should develop before graduation. Project educational objectives should directly inform

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the instructor’s expectations and the project requirements. For example, will training with certain tools be required as part of the project for all students? Are all students expected to understand and apply a specific design methodology? Clear objectives benefit all students and aid the instructor in making effective pedagogical choices. Based on the educational objectives, students should be given a clear set of project constraints, including budget, performance requirements, and supplementary resources like equipment, scrap materials or parts, allocated design space, and technician assistance. Constraints must allow the appropriate level of “ill-posedness” (i.e., open-endedness) to allow for meaningful design choices while discouraging (or preventing) students from pursuing ideas that require too much time, resources, or knowledge. In a robotics project, for example, the design choices allowed to first-year students (or those with less design experience) might include selecting electrical components from a limited list of allowable parts. In contrast, more advanced students may be asked to design the entire control system from scratch. The basis of evaluation for the project (i.e., ‘scoring’) should be clearly defined at the outset. Students should be provided with the rubric that will be used for assessment. The rubric is another tool for guiding students to achieve the educational outcomes. Do aesthetics and build quality matter for grading? Is the performance of the prototype part of the grade? Are points awarded based on the material used (or not used) in the design? Put bluntly, we value what we measure. Every assessment metric should be directly related to an educational objective the instructor intends the project to achieve. Too much focus on prototype performance, particularly in first- and second-year projects can overshadow expectations for applying specific design approaches, learning to use certain tools, developing skills like teamwork and communication, or taking design risks. For capstone design projects, prototype performance will likely be a much more significant portion of the overall grade. It is important to remember, however, that these are still student projects.

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STUDENT BACKGROUND A project that is suitable for upper level students can easily overwhelm a first- or second-year student. Similarly, a project suitable for a first-year course could become too simple or uninteresting for students in later years. In the context of hands-on design, consideration must be given to both the complexity of the analysis and the sophistication of the fabrication and testing required to successfully complete the project. If students in a design course are concurrently enrolled in statics, then expecting the incorporation of a finite element analysis into the design is probably not appropriate. A capstone design project incorporating structural loading that does not include an expectation for numerical analysis is similarly suspect. Even if a design project has been successfully used in previous semesters, a given class of students might be more or less prepared. Project pre-planning should be followed by periodic re-evaluations by the instructor during the course of the design project to identify any gaps that need to be addressed. For hands-on design projects, students with little fabrication experience will require more time and instruction to become trained to safely use lab or shop facilities. If this is not explicitly addressed elsewhere in the program (e.g., a lab or shop safety course), the instructor may need to incorporate time for the training into the project schedule. While some students are very skilled in a shop, most should be assumed to have negligible skills with power tools and fabrication/assembly until proven otherwise. Even students who report having used specific tools in the past should be required to demonstrate their technique and safety measures to identify any unsafe practices. Students, particularly those without previous hands-on experience, tend to apply fabrication techniques in their design hesitantly; incorporating explicit requirements for preliminary models into the project (prior to final production of a prototype) will help students become more comfortable with tools and testing. It can be beneficial to initially restrict the fabrication techniques to be used so that students do not feel overwhelmed. For example, a first-year project might be limited to the used of hand tools or a drill press. Second- or third-year projects might

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incorporate mills, lathes and even casting of simple shapes. Rapid prototyping can greatly speed up the process of producing working models and prototypes. But the use of rapid prototyping will result in a significantly different design experience for the students than if they were required to produce the same part via drilling, milling, and cutting. The choice of the correct approach goes back to the educational objectives for the project. It may be beneficial to limit the use of 3D printing and other rapid prototyping technologies in lower-level projects so that students gain a more complete understanding of fabrication and assembly options. For higher level projects, they will then be in a better position to select the appropriate manufacturing techniques based on the project constraints. It is important to match analytical expectations for the project with student abilities. First- and second-year students might be given governing equations for electrical current, bending of structural members, etc., and be asked to apply the equations as part of selecting project components. Third- and fourth-year students should be able to identify the appropriate governing equations for their design and justify their choice based on the project conditions. At all levels, the connection between analysis, design, fabrication, and testing should be stressed. Engineering designs should be based on the application of mathematical and scientific theory. Students should have a prediction for how their designs will behave prior to cutting/drilling/pouring the first piece. Closing the loop by comparing actual design performance to their predictions is an essential step in the process. The use of student teams for hands-on design projects can increase the allowable project complexity, help offset differentials between group members’ knowledge and abilities, and increase students’ abilities related to teamwork, conflict resolution, and interpersonal communication. However, the instructor must give special care to the approach used to assign team members as well as the process for remediating dysfunctional teams. Team formation tools like CATME Team-maker (CATME Teammaker 2019) can be used to inform group selection based on a variety of factors like students’ schedules, shop and hands-on skills, extra-curricular activities, and prerequisite courses. CATME offers additional tools to both

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train students to work as effective teams as well as to evaluate the performance of their teammates (CATME 2019). There is a small per student charge associated with the use of CATME – this will need to be factored into the overall project budget (but could potentially be shared across multiple courses).

PROJECT TIMELINE Each project should include check-in points for progress updates in addition to the overall deadline for producing a final prototype. This helps to ensure that students are achieving the educational outcomes by spending an appropriate amount of time in each step of the design process. For firstyear students or inexperienced designers, a check-in point for submitting a preliminary design prevents students from continually iterating on possible designs rather than choosing a likely candidate, making and testing preliminary models, and then moving on to final fabrication and testing. It can also prevent students from jumping to the final fabrication stage without spending enough time on analysis. Even senior students benefit from regular check-ins, although the specific timeline and check-in points for each capstone project may differ somewhat. Hands-on experiences typically progress slowly and require more time than would be required to cover the learning outcomes with lectures, videos, or simulations. A timeline that allows for consistent, but not overbearing, progress should be developed that focuses on specific tasks or deliverables for each day, with a planned date to finalize the design and begin the prototype fabrication. The design portion may or may not incorporate the production of preliminary models. For first- and secondyear projects, this timeline may be provided by the instructor. For thirdand fourth-year students, creation of the appropriate milestones within the overall confines of the term is a reasonable project expectation. In general, students tend to underestimate the time that will be required for the handson portion of the design. They often fail to consider setup and cleanup time, the fact that more than one design group may need to use the same

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equipment, the availability of skilled staff (i.e., shop technicians) to help with production, or the possibility that mistakes will be made during fabrication. Instructor input into project timelines is critical for successful project completion. It is typically a good idea for the instructor to complete the project himself or herself prior to assigning it in class, if possible. This may be more feasible for first- and second-year projects when the project constraints are likely similar across design groups. Based on the instructor’s level of expertise and the time required for him/her to complete the project, he/she can then assess the minimum team size that would be required to complete the same task in the time available, any additional training that may be needed in the class as part of the project, and any likely sources of error in the final prototype. For upper-level projects where design constraints and project requirements may vary from group to group, the instructor may need to solicit input from practicing engineers or others to ensure appropriate timelines and expectations are set.

BUDGET AND RESOURCES The budget for hands-on design projects can vary widely depending on the type and scope of the project as well as the resources that are already available. In general, project costs increase as the project complexity and sophistication of electrical components increase. Robotic or remote-control (RC) projects tend to have large upfront costs that can often be amortized over multiple terms by reusing key components (assuming they are not damaged during testing!). Simple, inexpensive mechanical kits (e.g., mousetrap cars and similar projects) are completely consumable each term. If the instructor intends to reuse specific parts from term-to-term, this must be taken into account during establishment of the design constraints to minimize the likelihood that student design choices will result in damage to the components.

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Identifying an appropriate budget is challenging for students. They will need significant guidance on appropriate expenditures. Hands-on design projects may be the first time that students have had to base their engineering choices on available resources. Inviting practicing engineers to speak to the class about the process for project budgeting and approval in industry can drive home the importance of economic considerations. For first- and second-year projects, a successful approach to budgeting can be for common expensive components to be provided to each group (e.g., remote controls, servos, power sources). Design groups can also be provided with a kit of additional materials, a budgetary allowance from the department to purchase their own, or a requirement that students supply their own materials. The latter approach can be problematic given the financial constraints on many students, especially if the project costs were not known prior to enrolling in the course. For open-ended projects with few common components between design groups (common for third- and fourth-year projects), an appropriate approach is to establish a reasonable overall budget limit for all groups and to require students to explain their proposed expenditures prior to any purchases being made. For especially expensive projects such a Formula 1/Baja SAE car, it may be appropriate to seek outside sponsorships. Again, careful consideration should be given to what, if any, level of financial investment is expected from the students. Other resources, such as equipment, tools, and technician time also need to be considered when establishing a project budget. Will each group use the same equipment or are multiple tools available? Are students going to be held financially responsible for care and return of their project tools? Who is responsible for overseeing shop/lab safety and tool usage? For a project requiring use of power tools, time may need to be scheduled with a shop technician or class teaching assistant for training. All hands-on design projects require workspaces. Space is often at a premium in engineering buildings, with multiple projects in close proximity. Instructors need to consider well ahead of the project kickoff how issues of space and equipment will be managed.

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SUGGESTIONS FOR HANDS-ON PROJECTS ACROSS A CURRICULUM Insights from implementing hands-on design projects across the mechanical engineering curriculum at UND are given below. General approaches as well as specific project examples are provided. Many of these projects can be modified to fit different levels of student abilities and knowledge. Projects are typically completed in small groups ranging from 3-5 students. More involved projects (typically at the senior level) such as a Formula 1 car for the Society of Automotive Engineers (SAE) competition may have significantly larger design groups.

First-year Projects At a first-year level there are many opportunities to pursue hands-on experiences as students learn about the engineering design process, computer-aided drafting (CAD), or computer programming. Projects at this level may have a strong focus on non-technical skills such as working in teams, identifying students’ particular area(s) of interest within engineering, developing professional communication skills, and understanding a structured design process. Students are in the process of developing their analytical engineering skills and may find it easier to explain why other designs do or do not work than to create their own. They can be expected to make decisions based on guided technical reasoning (pros & cons of design choices, ranking design options based on their judgement of engineering criteria such as durability, ease of manufacturing, performance, safety, etc.), but their conclusions are likely to be much less sophisticated than those of more experienced students. A great way to introduce the concepts of design and design intent to students is through reverse engineering. Reverse engineering works well in courses dedicated to design as well as those focused on learning specific skills such as CAD. Providing similar components to each design group allows students to learn from each other’s analyses but may lead to

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repetition of results, depending on the size of the class. Examples of designs that provide a variety of options for analysis include small internal combustion engines, foam dart guns or similar toys, and small kitchen appliances. Assemblies with moving parts allow students to deconstruct the original design, identify one or more parts to analyze, and then verify that the overall design is still functional after the assembly is reassembled. Students can discuss why they think the original part was designed in a specific way – was the shape required to allow for clearance from other parts? How would changing certain dimensions of the part affect its ability to function? Could the part have been made from a different material? How was the part likely fabricated? In a CAD class, students could be asked to create engineering drawings of the original part and potentially recreate the part using rapid prototyping. The budget required to run a hands-on project of this scope is very low. Objects for analysis can often be found at junkyards or surplus centers (but may require some cleaning!). Students will typically require a set of screwdrivers and wrenches along with a set of calipers. Rapid prototyping will involve additional costs for the printer material and technician/instructor time for facilitating fabrication. The timeline for a reverse engineering project can range from a single class period to a couple of weeks, depending on the complexity of the analysis required and whether students are asked to fabricate their own components. Reverse engineering projects are an excellent lead-in to a constrained design project for first-year students. The goal of these design projects is to design, build, and test a prototype over the course of several weeks. Examples of projects that work well are small wind turbines, remotecontrol balsa planes, or even components for larger student design projects like a robotics team or Formula-1/Baja SAE cars. In all cases, the instructor needs to carefully establish design constraints based on available time and budget. If students are tasked with designing a small turbine that can withstand 40mph winds in a wind tunnel, for example, a range of design choices can be allowed. Students can be given a DC motor and be required to select appropriate gearing to connect it to the turbine. They can be provided with enamel-coated wire, magnets and spindles and be asked to create their own permanent magnet generator. They can be required to

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craft turbine blades out of sections of shapes like PVC piping or plastic sheets or to rapid prototype their own airfoil shapes based on calculations of lift and drag. Students at this level will typically require an orientation and training session in the wood shop to fabricate the turbine structure. The electrical design is typically limited, with the goal being to light up an LED rather that to generate specific voltage or amperage. Educational outcomes for projects at this level typically focus on communication – graphical communication (CAD drawings of individual parts and the overall assembly) and written communication (clearly explaining design decisions and the design/fabrication process) – rather than measured performance of the prototype or quality of the build. A typical budget for this project is about $50 per team, mostly due to bearing and generator part costs which can be reused. The project can be completed in about half a semester (6-8 weeks).

Second-year Projects At the sophomore level, hands-on projects can increase in complexity and may range from a multi-day design experiences to semester-long design projects in dedicated design courses. The ASME Student Design Competition (SDC) (ASME 2019), typically provides excellent project ideas for second- (or third- or fourth-) year students. The SDC project changes every year but typically requires the fabrication of a mechanism (sometimes remote-controlled, sometimes not) to complete a specific task. These projects require significant time and resources to build from scratch – they are best used in a class focused on a single project over the course of a semester unless the instructor simplifies the challenge statement or focuses the design on a sub-system of the overall design challenge. The 2019 project involved creating a remote-controlled device to collect and sort differently sized balls. The 2020 project involves creating a device than can manufacture a tower out of sheets of paper by folding, cutting, or mechanically joining. The opportunity for successful teams to potentially

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enter their device in the ASME competition can provide additional motivation to students. Projects involving remote control (RC) will typically require RC transmitters, receivers, batteries, wiring, DC motors, servos, and brushed motor controllers. These are good projects for students at the sophomore level and above as they can incorporate calculations similar to what is being learned in classes like statics, mechanics of materials, physics, and circuits, although the project may require additional instruction in specific areas. Predicting the torque required for a motor to maintain a constant velocity or to apply a force to move a mechanism are typical for these projects, with the result being used by students to select motors or servos for their mechanisms. This type of project can be completed with robotic kits (e.g., VEX Robotics) which come with an entire ecosystem of parts that allow the project to be built and tested in a shorter period of time than building the structure from metal or plastic stock. Alternatively, students can be required to fabricate their own structural components from aluminum, steel, or plastic. Depending on the level of fabrication required, additional training related to shop equipment like mills, lathes, brakes, and welding stations may be required. Online retailers (Amazon, RC Hobby, ServoCity, AndyMark, among others) have a wide variety of electrical components and mounting hardware suitable for these projects that allow for customization and variation in designs. For a project of this scope, second-year students will typically require assistance in wiring motors to speed controllers and setting up the RC transmitters, but they can be expected to design the rest of their robot or mechanism and spend time testing and working with their prototype. To successfully implement this type of project, it is important to ensure that sufficient budget is available so that there is enough of the RC equipment for each team and to ensure that everyone receives appropriate instruction on soldering and electrical wiring techniques. A typical cost to run the ASME robotic project is about $400 per team in materials and RC parts that students select themselves in the design process, and about $200 per team in RC equipment that can be reused each year (transmitters, speed controllers, batteries, etc.).

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Third-year Projects Projects at the junior level can be similar in scope to ASME SDC projects, with the expectation that students should be able to incorporate more significant analyses of stress, kinematics of motion, and electronics into their designs. As an example of a shorter duration design experience at this level, optimizing the size and shape of a quadcopter frame or a robotic arm is a project that could be designed, rapid-prototyped, and tested over the course of several class periods. Students at this level may also be able to successfully attack more open-ended design challenges in which they have to identify at least some of the design constraints themselves. The ability to create a design that can solve a problem in their own life or that could be of interest to a company at which they have interned can provide additional motivation. Open-ended designs can be standalone or can be part of larger projects such as a Formula 1/Baja SAE car or another group’s senior design project. Instructor feedback related to the design requirements, project timeline, and budget is essential as students move to increasingly openended designs. If student teams are pursuing individual design projects, it will be important to coordinate shop space and equipment usage as well as training. Depending on expectations for ownership of the finished prototypes, projects at this level may be sponsored by outside industry, provided with departmental financial support, or self-financed by students.

Fourth-year Projects Senior-level mechanical engineering projects are typically intended to satisfy the requirements of a capstone design course. Students are expected to apply a variety of analytical tools, follow a logical design process, and produce a functional prototype that satisfies specific constraints. The complexity of capstone projects is often similar to what will be faced by students after graduation, including both shifting design requirements and budgetary limitations. Mechanical engineering programs often use design

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competitions such as Formula 1/Baja SAE (SAE 2019), NASA Lunabotics (NASA 2019) or the ASME SDC as the basis for senior design projects. Another successful application of senior design projects is the fabrication of equipment for faculty research or for use in other mechanical engineering classes. Examples include components for a departmental wind tunnel or a demonstration setup for a thermodynamics class. Many programs successfully recruit outside industry to sponsor senior design projects, as well. Academia and industry work at different speeds. Appropriate industry projects for senior design are often problems that a company would like to get to when they have time as opposed to a critical project that needs to be completed as soon as possible. In addition to the thrill of solving ‘real’ problems, industry sponsorship can provide students with important experience related to intellectual property (IP). Expectations for IP and prototype ownership should be clearly established before the project commences if external sponsors are involved. For a year-long capstone course, an appropriate timeline may be to spend approximately one semester establishing design constraints, conducting preliminary design and model testing, and finalizing a design. The second semester can then be focused on prototype fabrication, testing, and project reporting. Allowing sufficient time for ordering all required parts as well as testing of the final prototype are two areas where students often struggle when establishing a timeline for these projects. Instructor (or industry advisor) guidance is critical for open-ended design projects. Regular check-in points over the course of the design experience increase the likelihood that the overall project goals will be achieved.

CONCLUSION Experience with hands-on design is critical in the development of wellrounded mechanical engineers. By integrating increasingly open-ended and complex hands-on design experiences throughout the mechanical engineering curriculum, instructors help their students gain confidence with using physical models, in addition to computational tools, to

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understand the world around them. Students learn to make and test hypotheses related to their designs. They also improve their visualization skills and understanding of manufacturing processes. Overall, students become more effective mechanical engineers.

REFERENCES ASME Student Design Competition, https://efests.asme.org/competitions/ student-design-competition-(sdc), accessed October 18, 2019. Atman, C., J. R. Chimka, et al. (1999). “A comparison of freshman and senior engineering design processes.” Design Studies, 20, 131-152. Brereton, M. (1999). The role of hardware in learning engineering fundamentals: An empirical study of engineering design and product analysis activity. Mechanical Engineering. Stanford, CA, Stanford. PhD: 215. Brereton, M. and B. McGarry (2000). An Observational Study of How Objects Support Engineering Design Thinking and Communication: Implications for the design of tangible media. CHI Letters, 2(1). Camburn, B., B. U. Dunlap, et al. (2013). Methods for Prototyping Strategies in Conceptual Phases of Design: Framework and Experimental Assessment. IDETC/CIE 2013, Portland, OR, ASME. CATME Team-maker, https://info.catme.org/catme-tools/team-maker/, accessed October 20, 2019. CATME, https://info.catme.org/, accessed October 20. 2019. Christie, E. J., D. D. Jensen, et al. (2012). Prototyping Strategies: Literature Review and Identification of Critical Variables. ASEE Annual Conference and Exposition, San Antonio, TX, ASEE. Deininger, M., S. R. Daly, et al. (2017). Novice designers’ use of prototypes in engineering design. Design Studies, 51, 25-65. Dunlap, B. U., C. L. Hamon, et al. (2014). Heuristics-Based Prototyping Strategy Formation: Development and Testing of a New Prototyping Planning Tool. IMECE 2014, Montreal, Quebec, Canada, ASME.

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Dutson, A. J., R. H. Todd, et al. (1997). A Review of Literature on Teaching Engineering Design Through Project-Oriented Capstone Courses. Journal of Engineering Education, 86(1), 17-28. Dym, C. L., A. M. Agogino, et al. (2005). Engineering Design Thinking, Teaching, and Learning. Journal of Engineering Education, 94(1), 103-120. Elverum, C. W. and T. Welo (2014). The role of early prototypes in concept development: insights from the automotive industry. 24th CIRP Design Conference, Elsevier, 491-496. Froyd, J. E., P. C. Wankat, et al. (2012). Five Major Shifts in 100 Years of Engineering Education. Proceedings of the IEEE, 100, 1344-1360. Houde, S. and C. Hill (1997). What do Prototypes Prototype? Handbook of Human-Computer Interaction. M. Helander, T. K. Landauer and P. Prabhu, Elsevier Science, 367-381. Jang, J. and C. D. Schunn (2012). Physical Design Tools Support and Hinder Innovative Engineering Design. Journal of Mechanical Design, 134, 041001-1 - 041004-9. Jensen, M. B., C. W. Elverum, et al. (2017). Eliciting unknown unknowns with prototypes: Introducing prototrials and prototrial-driven cultures. Design Studies, 49(1), 1-31. Lande, M. and L. Leifer (2009a). Introducing a ‘Ways of Thinking’ Framework for Engineers Learning to Do Design. ASEE Annual Conference and Exposition, Austin, TX, ASEE. Lande, M. and L. Leifer (2009b). Prototyping to Learn: Characterizing Engineering Students’ Prototyping Activities and Prototypes. International Conference on Engineering Design, ICED'09, Stanford, CA. Lauff, C. A., D. Kotys-Schwartz, et al. (2017). Perceptions of Prototypes: Pilot Study Comparing Students and Professionals. IDETC 2017, Cleveland, OH, ASME. Lauff, C. A., D. Kotys-Schwartz, et al. (2018). What is a Prototype? What are the Roles of Prototypes in Companies? Journal of Mechanical Design, 140(6), 061102-1 - 061102-12.

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Menold, J., K. Jablokow, et al. (2017). Prototype for X (PFX): A holistic framework for structuring prototyping methods to support engineering design. Design Studies, 50, 70-112. NASA Lunabotics, https://www.nasa.gov/offices/education/centers/ kennedy/technology/nasarmc.html, accessed October 18, 2019. National Academy of Engineering. (2005). Educating the Engineer of 2020: Adapting Engineering Education to the New Century. Washington, DC, National Academy of Engineering: 208. Neeley Jr., W. L., K. Lim, et al. (2013). Building Fast to Think Faster: Exploiting Rapid Prototyping to Accelerate Ideation During Early Stage Design. IDETC/CIE 2013, Portand, OR, ASME. SAE Student Events, https://www.sae.org/attend/student-events, accessed October 18, 2019. Viswanathan, V. K. and J. S. Linsey (2010). Physical Models in Idea Generation - Hindrance or Help? DETC2010, Montreal, Quebec, Canada, ASME. Wood, K. L., D. Jensen, et al. (2001). Reverse Engineering and Redesign: Courses to Incrementally and Systematically Teach Design. Journal of Engineering Education, 90(3), 363-374. Youmans, R. J. (2011). The effects of physical prototyping and group work on the reduction of design fixation. Design Studies, 32, 115-138.

In: Mechanical Engineering Education … ISBN: 978-1-53617-791-6 Editor: Charles E. Baukal, Jr. © 2020 Nova Science Publishers, Inc.

Chapter 9

FOSTERING THE DEVELOPMENT OF AN ENTREPRENEURIAL MINDSET IN ENGINEERING EXPERIMENTATION COURSES Kimberly E. Bigelow Department of Mechanical and Aerospace Engineering, University of Dayton Dayton, OH, US

Keywords: experimentation, entrepreneurial-minded learning (EML), project-based learning

INTRODUCTION This chapter describes the growing attention to and interest in incorporating entrepreneurially-minded learning (EML) across the engineering curriculum. While EML is most commonly incorporated into courses focused on engineering design, this chapter provides an example of how EML can be successfully and impactfully integrated into an engineering experimentation course. The chapter utilizes the EML

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framework as introduced by the Kern Entrepreneurial Engineering Network (KEEN). The chapter first introduces a background on EML and the benefits of integrating it into the mechanical engineering curriculum. The chapter then details the opportunities that exist to bring EML into experimentation and lab-based classes and provides details about the changes to course structure that were made to an existing engineering experimentation course (required junior-level mechanical engineering course) to better infuse EML throughout the semester. The chapter concludes by discussing the outcomes of this initiative and providing recommendations for other instructors wishing to adopt a similar approach.

ENTREPRENEURIAL-MINDED LEARNING WITHIN ENGINEERING A growing number of engineering instructors across the country have begun to incorporate entrepreneurially-minded learning (EML) within the engineering curriculum (Rae and Melton, 2017). While entrepreneurship education in engineering has existed in various forms for some time (Nichols and Armstrong, 2003), the establishment of the Kern Entrepreneurial Engineering Network (KEEN) in 2005 generated renewed interest in EML through a network-based approach of sharing best practices across institutes of higher education in the Unites States (Kriewall and Mekemson, 2010; Rae and Melton, 2017). There are currently 45 KEEN member institutions and thousands of faculty who are using EML to actively promote the development of an entrepreneurial mindset in their students (KEEN EngineeringUnleashed Website, 2019). Each of these member institutions seek to grow an EML community both within their own institution and with other institutions across the network. KEEN helps foster these network connections through various in-person professional development events, workshops, conferences, and cohort meetings, as well as virtually through a highly-supported website in https://engineeringunleashed.com.

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KEEN’s success in generating momentum for EML is likely, at least partially, rooted in their development of the KEEN Framework (Rae and Melton, 2017). This framework provides a structure which moves toward a consensus understanding of what an entrepreneurial mindset is, and how EML enables its development. As such, the KEEN Framework has emerged as the standard for implementing EML within the classroom, and across the curriculum. The framework has been particularly helpful in shifting perceptions. Without appropriate context, some – including initially myself – incorrectly assumed that an entrepreneurial mindset relates to one’s readiness to start up their own company. The idea of this was a bit off-putting to me as while certainly some students aspire to this, others do not even wish to work in industry, rather wanting to be servantleaders within the community. The KEEN Framework helped me see that EML was intended to help engineering students develop a mindset with skills that complemented their technical abilities, allowing our engineers to be innovative leaders ready to solve problems and make a difference – whatever path they followed. Per KEEN’s Framework, the foundation of EML is based on the development of three important mindset traits: 1. Being Curious, 2. Making Connections, and 3. Creating Value. This mindset on its own is not sufficient: student must also develop an engineering skillset. The skillset as described by KEEN includes those design skills that are generally accepted as fundamental to being an engineer, including: determining design requirements; performing technical design; analyzing solutions; creating models and prototypes; and validating functions. EML however emphasizes additional skills that are often important but not regularly incorporated into the curriculum. The KEEN Framework categorizes these as either Opportunity Skills or Impact Skills. Opportunity Skills include the ability to: identify an opportunity; investigate the market; create a preliminary business model; evaluate technical feasibility, customer value, societal benefits, and economic viability; test concepts quickly via customer engagement; and assess policy and regulatory issues. Impact Skills include the ability to: communicate an engineering solution in economic terms; communicate an engineering solution in terms of societal

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benefit; validate market interest; develop partnerships and build a team; identify supply chains distribution methods; and protect intellectual property. Taken together, these create the educational outcome of producing engineers who “possess an entrepreneurial mindset coupled with engineering thought and action expressed through collaboration and communication and founded on character”. These students are able to: demonstrate constant curiosity about our changing world; explore a contrarian view of accepted solutions; integrate information from many sources to gain insight; assess and manage risk; identify unexpected opportunities to create extraordinary value; and persist through and learn from failure. Additionally, these students should be able to: apply creative thinking to ambiguous problems; apply systems thinking to complex problems; evaluate technical feasibility and economic drivers; examine societal and individual needs; form and work in teams; understand the motivation and perspective of others; convey engineering solutions in economic terms; substantiate claims with data and facts; identify personal passions and a plan for professional development; fulfill commitments in a timely manner; discern and pursue ethical practices; and contribute to society as an active citizen. When one thinks about the value such an engineer with these types of skills and abilities would have, it seems almost a disservice to our students if we choose not to actively foster the development of this (or a similar) mindset. While little empirical data exists to validate the effectiveness of EML and the importance of an entrepreneurial mindset (it has been found that due the nature and breadth of EML-related outcomes, there are numerous challenges in assessing individual impacts and measurable changes in students’ attitudes, behaviors and skills (Duval-Couetil, 2013; Kriewall and Mekemson, 2010)), anecdotal evidence supporting the value of EML is plentiful.

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ENGINEERING EXPERIMENTATION: RIPE FOR EML To date, EML has found its way most prevalently into the engineering design curriculum as so many of the opportunity skills and impact skills so nicely complement existing course expectations relative to design. However, KEEN has continually promoted the importance of embedding EML across the curriculum. For this reason, I decided to focus on revising my junior-level engineering experimentation course to emphasize EML. As a standard required course in many engineering curriculums, experimentation is ripe for this kind of revision, though for many it is not an obvious choice. Historically, experimentation classes have concentrated on discrete skills, sometimes taught through fairly formulaic, or step-bystep, experiments. The emergence of project-based learning has shifted this, such that there are now more examples of students being required to tackle open-ended problems, work on real-world experiments and simulations, and design their own experiments and passion projects. As such practices emerge, experimentation courses begin to parallel certain aspects of engineering design courses even though the two are often taught in isolation. In Summer 2016, I attended a KEEN workshop (Integrating Curriculum with Entrepreneurial Mindset Workshop) with the goal of revamping the course I taught at the University of Dayton, MEE 341 Engineering Experimentation, to best incorporate EML. Previous to this course redesign, I had structured my course around a series of discrete experiments, each introducing a new concept or sensor. Students then synthesized this knowledge with a larger self-defined final project. At the workshop, I was introduced to the idea of structuring the course around larger EML-based laboratory modules instead of smaller, individual experiments. In these laboratory modules a central problem or project statement was introduced. Students then spent multiple class sessions breaking the problem down into smaller activities, milestones, and deliverables (all of which incorporated EML and strived to foster the development of an entrepreneurial mindset). At the workshop I saw examples of effective modules from various engineering courses. I learned

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that the best modules were those that challenged the students using a realistic, relatable problem, providing sufficient background and context (including generally the incorporation of some kind of stakeholder), and ensuring the problem and the deliverable requirements were open-ended enough for students to be given freedom and creativity, while requiring active contributions to a team effort. I revisited the KEEN Framework to establish which skills I could envision implementing into a laboratory module for my engineering experimentation course. One skill which immediately coincided with my course goals – but at a much loftier level – was to “evaluate technical feasibility, customer value, societal benefits, and economic viability”. In my course, like so many engineering experimentation courses, much of the focus was placed on technical rigor – using the right sensor, collecting the right data, running the correct statistics to reach some technical conclusion. However, in reaching our experimental conclusions, I had not placed an emphasis on the other aspects – cost, access, societal benefit - which should be considered in conjunction with our findings in order to make a comprehensive recommendation. I was missing an opportunity, as I imagine many others are too, but hoped my new laboratory module would meet this need.

IMPLEMENTING MY LABORATORY MODULE IN MEE 341: AN EXAMPLE Course Details of MEE 341 at the University of Dayton Prior to providing details of the laboratory module I developed, it may be helpful to put my course in context, as what I describe here may or may not scale well to classes of different sizes or class duration. At the University of Dayton, MEE 341 Engineering Experimentation is taught as a three-credit course with it up to the instructor the balance of lecture vs. lab. Our course sections for this class each consist of 15 – 20 students, with approximately six sections of experimentation offered each semester,

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taught by different instructors. The course meets two days a week for an hour and fifty minutes each time. All courses satisfy the same list of course objectives but achieve them through different course models as the department begins to assess which approach emerges as the most effective for student learning. The course description for MEE 341 is: “This is a course that introduces engineering students to the design and analysis of engineering experiments, as well as the use of common engineering tools.” Course goals are that, by the end of the course, students will be able to:   

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Select an appropriate sensor for their needs, describing their choice in terms of sensor characteristics and errors Interpret measurements taken and experimental results with regard to sources of error and uncertainty Operate basic instruments used to collect data such as an oscilloscope, function generator, dc power supply, and digital multi-meter Collect data by interfacing sensors to data acquisition systems, while appropriately considering conditioning of data, aliasing, and digitization errors Demonstrate their ability to use their laptops to interface with an external data acquisition board and utilize the laptop as a digital oscilloscope Design and carryout testable hypotheses, collaboratively, while considering statistical design Analyze and interpret data, including critically evaluating the data of others Summarize experimental work through both oral and written presentation, including common technical formats

Throughout the course students work in self-selected teams of 3 – 4 students. The classroom is a lab-based room with lecture capabilities (e.g., project, whiteboard, movable tables). The room is equipped with electronic equipment (e.g., oscilloscopes, data acquisition boards, power supplies, function generators, multimeters, and sensors), as well as construction

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equipment (drills, saws, clamps, wood and building supplies, fasteners, etc.).

Lab Module 1: Experimental Investigation of a New Helmet Design for Prevention of Concussions Here I present the details of the primary laboratory module that I created for my class when I redesigned my course to emphasize EML. I have now used this laboratory module seven times. I present this module as an example so that it might inspire others to think about how an EMLinfused, laboratory module-based approach might be possible in their own engineering experimentation courses. As I describe later in this chapter, such an approach is easier done than perhaps one might imagine, and in my experience has been very well received by my students. The laboratory module that I created was developed as a large project to be carried out during the first two months of the course, starting on the very first day. The laboratory module is entitled Experimental Investigation of a New Helmet Design for Prevention of Concussions. The module task is presented to the class in both a summarized format and as a full write-up. In the summarized format students are told: “Your research team at the University of Dayton Impact Lab has been approached by a youth advocacy group to provide an expert opinion as to whether they should advocate for the purchase of newly designed helmets from the company Helmets for Better Brain Health. You will be provided a traditional helmet and the improved helmet for experimental comparison. In reality, it is likely that you will receive one helmet (traditional) and special padding so that when it is inserted in that same helmet it becomes the new (Better Brain) helmet.”

Students are informed that the company Helmets for Better Brain Health is fictitious, though certainly there are other, real, companies working on designing helmets to reduce the incidence of concussions. The more complete write-up details the EML components of the project. In this write-up, students are told additional information such as:

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(fictitious) claims of how much the helmet supposedly reduces impact forces; the cost of the helmet; how frequently the helmet would need to be replaced; who the youth advocacy group consists of (e.g., target audience); and what the youth advocacy group has requested (in this case, a presentation highlighting experimental results and other considerations as to whether the helmet adoption seems promising, as well as proposed next steps). This write-up is critically important to the project and it serves as the instructor’s chance to provide information that will help the students consider aspects of the problem relating to customer value, societal benefits, and economic viability. Choosing to focus my laboratory module on football helmet design as it relates to concussion was a personal choice, and there are infinite other possibilities that could be adopted. Considerations I made in choosing my module topic included it being a topic that: students would likely be familiar with and possibly interested by; was a less traditional engineering example (e.g., not a bridge, motor, etc.) highlighting engineering in the everyday world around us; provided opportunities to connect with current events and emerging news stories; and potentially incorporated content learned in previous general education courses. In trying to be inclusive to diverse students and student interests, I initially discussed with other faculty whether the focus on football would appeal enough to my wide range of students. Those initial conversations are what led me to include a focus on youth (rather than professional) and also include language in the full write-up that highlighted other sports (including women’s sports and sports more regularly played internationally) that also had high rates of concussions. Since introducing the laboratory module seven semester ago, the project has always been well received – from my students who are collegiate football players to international students who have never watched football before. I note this because it is important that care be taken in the crafting of the laboratory module topic so that it is one that will be appealing, effective, and one in which students are going to be invested. In my class, at the time the laboratory module is introduced, students also receive the laboratory module goals, the schedule with each day’s

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activity for the duration of the module, and the grading rubric and expectations of the laboratory module deliverable (the presentation to the youth advocacy group). Developing the laboratory module goals was an important first step to ensure that the laboratory module activities and related course components were logical and supported the achievement of these goals. The lab module goals were written to balance the technical rigor and expectations of the class, with the desired new emphasis on EML. The learning objectives of the module I developed were that by the end of the module, students should be able to:      

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Identify relevant ASTM standards and apply these to design an experimental protocol Compare two contradicting journal articles and discuss limitations of experimental methods Select an appropriate sensor based on technical specifications and needs Demonstrate an ability to collect accurate data from an accelerometer and DAQ Interpret and visually communicate data in a meaningful format Evaluate technical feasibility and experimental evidence, customer value, societal benefits, and economic viability to reach a decision of whether the helmet adoption should be pursued (EML-based goal) Substantiate claims with data and facts (EML-based goal) Communicate an engineering solution and next steps to a diverse audience in terms of both societal benefits and economic terms (EML-based goal) Estimate the scope, costs and timeline for a larger scale follow-up experimental study to continue testing on the helmets (EML-based goal)

This also helped shape the grading breakdown. For this large of a project, it became worth approximately 50% of the total course grade, and was comprised of: the culminating presentation weighted for peer

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evaluations (21%); (independent) homework (10%); quizzes and lab practicals (8%); associated in-class activities and milestones (7%); and participation (3%). Table 1 provides my course schedule for the laboratory module showing the types of activities and content that is embedded into the eight week experience. Many of the lessons had been taught previously in isolation or as a lead-in to a specific experiment. Embedding them into an overarching project has made them seem more meaningfully connected. Table 1. Activities embedded into eight week lab module Class Session 1 2 3 4

5 6 7 8 9 10 11 12 13 14 15 16

Lab Module Activity How to identify, read, & critically evaluate resources; Background research Background research; Read/discuss conflicting academic journal articles that use relevant testing methods Read and identify relevant testing standards (ASTM/NOCSAE); Compile introductory research to establish experimentation direction Introduction to sensor options; Calibration and calibration curves; Tutorials on accelerometers collecting data with both oscilloscopes and DAQs Sensor characteristics; Sensor uncertainty calculations w/ application to relevant sensors Experimental design and planning; Prepare experimental proposal; Time to try preliminary concepts Experimental proposal reviews; How to calculate, interpret and present data lesson and activity Revise experimental concepts; Build test rigs; Begin data collection Data collection Continue data collection Continue data collection; Statistical analysis tutorial; Work on data analysis How to communicate – presentation, proposal, timeline, budget; Finish data collection Finish data analysis; Work time Peer reviews of presentations; Work time Presentations Presentations; Reflections & Wrap-Up

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Sample Lab Module Activities In this section, I give examples of some of the EML-based activities that I incorporated into the laboratory module. I found these to be of particular value to the course experience and all could be easily adoptable by other instructors, even if they are not utilizing a laboratory modulebased course structure.

Introductory Research Activities In my experience, students are eager to get to the hands-on portion of engineering design or experimentation, without recognizing the importance of spending adequate time truly understanding the problem and using that understanding to guide everything else they will do. For this reason, I structure the course so that the first three classes are devoted to aspects of background research. We start with an easily implementable activity: the creation of individual “I don’t know” (or “I wonder”) lists. Applicable to any lab module topic, students are given 3-5 minutes to independently write down as many questions as they have, having just been given the problem statement. Here, students are pushed to include anything and everything that comes to mind, with this list likely based at least partially on their prior knowledge and interests. Sample questions that have emerged from the football helmet module have ranged from “How many players on a football field at a time?” to “How do motorcycle riders stay safe?” Students then share their questions as a team, compiling a long master list of all of the questions. They categorize these questions and begin internet searches and literature reviews to find the relevant information, stopping to discuss at regular intervals. The background research is then supplemented with academic literature readings and review of relevant testing standards. This culminates in a compilation of the information learned as a team from all of these sources, as well as the team’s reflection of how what was learned will be used in developing their experimental concepts. From an EML-perspective this activity helps students practice making connections and appreciating how making those connections can inform their work moving forward.

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Experimental Concepts Proposal Our students in design courses are generally taught about the need to develop and consider multiple design concepts before identifying one that they will fully develop. However, in experimentation classes where they are likely making experimental design decisions – we generally do not incorporate this idea. To address this, I have begun to require all of my teams to create an experimental concepts proposal. In this proposal, the team must develop four possible experimental set-ups. For each of the four, they must describe and justify the concept, discuss the strengths and weaknesses, indicate how data would be collected and which outcomes would be used, and contrast it with any similar concepts they have proposed. Just as with design, forcing students to develop four possible, feasible ideas means that they are suddenly not just picking a design of convenience or one that someone on the team likes most. Rather, through this required activity, there has to be careful thought and ultimate comparison over which concept makes most sense experimentally for this particular problem, given the particular constraints the team is under. To assist with this process, the proposals are then reviewed round-robin during a class activity with peers, the teaching assistant, and myself. During this review we provide constructive feedback, identifying concerns and proposing suggestions to strengthen the experimental design plans before any actual test rig building or data collection begins. The activity ends by having the team make any revisions to their concepts and then putting their revised concepts into a decision matrix. I have found this exercise helps teams be more thoughtful in their experimental planning, thinking through the feasibility of their options. I have also found the ultimate designs are more thoughtful, and creative than they likely otherwise would have been. Building Testing Rigs By the nature of my particular project, students need to design some way to generate an impact to the football helmet. Because of this, it often becomes a necessity for students to build some type of testing rig. This may not be necessary for all experimentation projects, but too often it is not considered for any of them. We as instructors are sometimes so

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concerned with sensors that we don’t think sufficiently about the overall application – often which, in the real world, would require some kind of frame or rig to allow us to mount sensors, deliver impacts, etc. It takes more time, and supplies and equipment to allow students to build testing rigs that they will use for their experiment. However, I have found a great value in incorporating this into my class. It seems to increase student investment in the project, foster further connections to engineering design content, and helps students see that experimentation is an involved process that is not just about a sensor.

Budgeting a Future Experiment In the initially-presented, full problem write-up, I include a statement that says once the youth advocacy group has some preliminary findings (the lab module task) then they will be able to secure funding to carry out further, more rigorous testing. Depending on the team’s conclusion, this includes testing to either further demonstrate the merit of the new helmets or to go in a new direction if the team finds the helmets are not a promising solution to preventing concussions. Therefore, one of the final components of my laboratory module is to have student teams propose to the youth advocacy group their recommended next steps. After envisioning the details of this future experiment (which could utilize some of the testing equipment and standards that they had found during their introductory research but were unavailable in the classroom), the teams must then create a budget to estimate the cost and a timeline for how long it would take. We utilize a budget worksheet similar to that used by our university’s research institute so that students can understand the different personnel they might consider and their relative costs, learn about benefits rates and overhead, and really see how quickly the budget adds up. I have learned that students have quite a bit of practice budgeting for design projects, but despite the parallel to experimentation, it is generally not covered in courses like mine. Driven by a desire to include it as an EML-based activity, I have found having students develop this future experiment, budget, and timeline very valuable.

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Presentation to a Diverse Audience The ability to present to a diverse audience is a hallmark of EML and I have found it is helpful in providing the real-world context that drives these projects and leads students to become increasingly invested. I emphasize throughout the laboratory module the importance of the presentation that they will give to the “youth advocacy group” comprised of football coaches, parents, players, lawyers, doctors, and other stakeholders. We talk at length about what is likely to resonate with these individuals and I try to raise the bar so that students try something other than a standard bullet-point Powerpoint presentation. Sometimes I am able to get an actual audience to attend the presentations; other times it is just me, representing the youth advocacy panel. In either case, I have found the students to be increasingly invested and give by far more outstanding presentations than they do when I have assigned a “regular” class presentation that they know they are just giving to me for class assessment. I have seen so much creativity from these students when given the freedom to make their presentations matter. They have found web-based tools that allow interactivity with the audience and relayed touching personal stories about how concussions have affected those they care about. The idea of presenting to a diverse audience was driven by the KEEN Framework and has become one of the most impactful changes I have made in my course.

Instructor Perspective and Student Response I am currently working on formally assessing the effectiveness of the EML-based components that I have added to my course. The KEEN Network is committed to developing assessment tools to assist with this, but because of the broad scope of the EML skillsets, numerous challenges exist in doing so (Duval-Couetil, 2013; Kriewall and Mekemson, 2010). Because of this, like many others, most of my perspectives are based on observations and anecdotes over the past six semesters of teaching the course the new way, compared to approximately the same number of semesters teaching it using a more traditional structure with a series of

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experiments. My experiences suggest that the approach I describe here is working – students are learning and producing high-quality deliverables, demonstrating EML competencies that weren’t ever included in my old class, engaged and invested throughout the semester in much deeper ways, forming more meaningful relationships with peers, and most importantly there is a huge increase in their overall independence and willingness to persist through failure. I have thoroughly enjoyed teaching engineering experimentation using the EML-infused laboratory module-based approach, and students have expressed (via student evaluation) that they learned more in the class, enjoyed the course, and would recommend the instructor more than students did who took the previous structured version of my course. I should recognize that it took time to develop the lab module problem statement, ensure it mapped to the learning outcomes, develop relevant course activities, know how long activities would take, learn the sensors and DAQs, develop grading rubrics and expectations, etc. However, once the lab module was created, even in that first semester, it took less time to prepare for my individual classes and there was overall less perceived pressure because rather than being “on stage” each time you walked into the classroom, you were prepared with activities and ready to facilitate. Such an approach does, however, require the instructor to be comfortable with there being unknowns as the students often end up following unexpected paths. I had been warned that students might demonstrate resistance to such a different classroom pedagogy. I have not experienced this, perhaps because upper-level students at my institution are already accustomed to classes that include open-ended projects, student-centered learning, and collaborative work. Though, perhaps more importantly, it may just be that when students are active and engaged and working on projects that they feel matter – they like it! Indeed, they have often expressed this to me. At some point I will decide on a new topic for my laboratory module. However, at this point, despite having used the same problem statement for six semesters, I continue to see students identify new information and design creative test rigs that I have not seen before. I have benefitted from

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choosing a topic that is often at the forefront of the news, meaning that each semester new information is available that opens up new opportunities. This is something I will try to incorporate in future topics, as it does add value and variety.

CONCLUSION The integration of EML into courses is gaining nationwide momentum, particularly through the support of the Kern Entrepreneurial Engineering Network (KEEN) and their 45 membership institutions, large and small, across the country. While EML is perhaps most readily conceived to be incorporated into engineering design courses, EML can be beneficial in a wide range of courses within and outside of engineering. The KEEN framework provides an accessible model to identify skills that would complement existing course structure and content, but add additional value and depth to the class – helping to instill an entrepreneurial mindset in our students. The entrepreneurial mindset learning outcomes and skills presented within the KEEN framework also serve as inspiration and a blueprint for those instructors ready to consider a course redesign in which EML is purposefully considered, as I have described doing here. Engineering experimentation and lab-based courses lend themselves to integration of EML concepts and skills, though are not often considered. By broadening the scope of the course from simply technical to providing context which allows students to understand, integrate, or even comment on economical, societal or other aspects surrounding the experimental focus, an emphasis on developing an entrepreneurial mindset can be easily integrated. This can be done even without making significant changes to the course or exerting significant effort. However, when a more purposeful and deep incorporation of EML is made, in my experience, students become more invested and their learning becomes more personal. In this chapter, I have presented an example highlighting the evolution of my course from the somewhat standard structure of many, small, individual laboratory-based experiments, to a laboratory module course

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structure. This module was created to best integrate opportunities to incorporate EML to help students develop skills representative of an entrepreneurial mindset. The new emphasis on entrepreneurial mindset was most directly achieved through careful creation of the problem statement, specific point allocation toward the final deliverable as detailed on the grading rubric, and raising the expectations of the culminating presentation by having it be delivered to a diverse audience. In this chapter, I have attempted to highlight specific activities that I have embedded in the laboratory-module that may be of interest to other faculty members. Most of these could be incorporated into a range of experimentation and lab-based classes even if instructors do not wish to adopt an EML and/or laboratory module-based approach. I have found that in particular, creating parallels between experimentation and design curriculum seems helpful and eye-opening to students. My advice to fellow instructors is that it is easier to implement the changes described here than you might think, and the outcomes have been consistently positive. In my experience, there tends to be higher quality outcomes in student work, students who are more invested, and student evaluations which reflect satisfaction. Student investment is likely to be largely based on the topic chosen as the experimentation focus; ideas to help optimize this selection have been presented in this chapter. Instructors desiring to learn more about EML may wish to visit KEEN’s engineerunleashed.com website. On the website, there are resources to learn more about EML as well as exemplar course content shared by others for adoption by instructors across the country.

REFERENCES Duval-Couetil, N. (2013). Assessing the impact of entrepreneurship education programs: Challenges and approaches. Journal of Small Business Management, 51(3), 394-409. Kern Entrepreneurial Engineering Network (KEEN) Website. https:// engineeringunleashed.com. Accessed October 18, 2019.

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Kriewall, T. J. & Mekemson, K. (2010). Instilling the entrepreneurial mindset into engineering undergraduates. The Journal of Engineering Entrepreneurship, 1(1), 5-19. Nichols, S. P. & Armstrong, N. E. (2003). Engineering entrepreneurship: does entrepreneurship have a role in engineering education? IEEE Antennas and Propagation Magazine, 45(1), 134-138. Rae, D. & Melton, D. E. (2017). Developing an entrepreneurial mindset in US engineering education: an international view of the KEEN project. The Journal of Engineering Entrepreneurship, 7(3).

In: Mechanical Engineering Education … ISBN: 978-1-53617-791-6 Editor: Charles E. Baukal, Jr. © 2020 Nova Science Publishers, Inc.

Chapter 10

ON REPLACING THE STEAM TABLES Smitesh Bakrania Mechanical Engineering, Rowan University Glassboro, NJ, US

INTRODUCTION Steam tables have been an essential mechanical engineering tool for solving common power cycles within engineering thermodynamics. Nearly all thermodynamics textbooks include several pages of tabulated properties of water known as the steam tables. Specifically, the steam tables present key thermodynamic properties as a function of pressure P and temperature T. The tables are conventionally divided by mixture and vapor phases of water and further subdivided by pressures. Instructors must teach this tool to study power generation cycles, such as Rankine Cycles. Students use this tool to retrieve state properties and compute changes within thermodynamic systems. Often, students find the table-based retrieval process challenging to master (Balmer & Spallholz, 2006; Hagge, et al., 2017). The retrieval process can be involved with multiple interpolation steps to identify the correct state properties before solving the engineering problem at hand.

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To develop mastery, instructors typically dedicate both time and effort for students. The rationale for continuing to teach the use of steam tables is steeped in tradition. Its continued use within the Fundamentals of Engineering (FE) exam reinforces its relevance (Dixon, 2001; Miller, 2007). As a result, steam tables continue to occupy the precious textbook real-estate even in the digital age. Naturally, students, as resourceful as they are at reducing hardship, quickly realize that there are easier alternatives to retrieving state properties from the tables. The same algorithm that allows our students to retrieve table properties can be employed by a computer algorithm. Thus, numerous digital and online alternatives exist that rapidly supply state properties of water. These include computer applications (e.g., Taylor, et al., 2008 & EES), algorithms (e.g., X Steam for MATLAB, PyroMAT for Python), webbased tools (e.g., NIST Webbook, IRC Fluid Property Calculator, & SmoWeb Property Calculator), or mobile apps (e.g., International Steam Tables & Steam Tables). These computer-based alternatives of the traditional paper-based steam tables promptly deliver the unknown property given two known properties of a state without having to go through the tortuous process by hand. The computer-based alternatives are also part of the standard practice within professional engineering where it is part of a larger system of design tools. Under the circumstances, should educators replace the steam tables with computer-based tools to better align with engineering practice?

Figure 1. When discussing thermodynamic properties of substances, the emphasis is placed on the relationships, except when discussing water where only values are important.

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The short answer is “no”. To answer this question at length, we must first question the utility of steam tables themselves. What function do the steam tables serve within engineering education. At a more basic sense, the steam tables supply property values of water. Then by extension replacing the steam tables with computerized tools is justified. However, if the retrieval exercise is meant to familiarize our future engineers with water properties so that they develop an intuitive understanding of property relations, then replacing steam tables with computerized tools would exacerbate the student learning outcomes. In fact, one can argue that steam tables themselves fall short of this objective. As a result, use of steam tables is often accompanied with a qualitative sketch of the process on a property chart to contextualize the state. Simply supplying the numeric property values as a computerized tool does is similar to presenting GPS coordinates without a topographical map to go along. We recognize that the context around the values is important for a deeper connection with the data presented. For instance, given a thermodynamic state, can the students predict how the thermodynamic properties will change if the temperature is increased? Going back to the GPS analogy, if one moves north on the map, how does the topography evolve? A map or a visual representation helps us create a mental model or schema of the relationships and the overall trends. The relationships between the thermodynamic properties is more important as far as the student learning outcomes are concerned than the exact state values that steam tables or computerized tools provide. These relationships feed into the development of a mental model and intuition necessary among engineering graduates to examine complex computer-generated thermodynamic system models. The emphasis on relationships is applied to various other concepts within engineering thermodynamics but is strangely absent when dealing with water. For instance, when introducing ideal gases the Pv = RT equation serves as a fundamental relationship for thermodynamic. The relationship between specific volume v, pressure P and temperature T is mathematically apparent and forms a key learning outcome for any thermodynamic course, namely how the properties are related to each other. Assessments within this topic often test the students’ ability to

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predict changes in properties as a function of the other and test their intuitive understanding of gas behavior. The same is true when dealing with real gases with the inclusion of compressibility factor. However, when switching to water as a working fluid, the underlying relationship between the various properties is rarely discussed explicitly. The reliance on steam tables, which are generated using a complex set of equations of state, hides the interdependence of properties. The emphasis of property values over relations hinders students’ ability to develop an intuitive understanding of water behavior. This idea is visually presented in Figure 1. As a result, assessments involving steam tables often test students’ ability to retrieve properties; which is a highly specific skill that is arguably obsolete within engineering practice. Instead an intuitive understanding can serve as a mental check for students later in their engineering careers. In other words, are there ways to emphasize relationships even if the equation of state for water is complicated. The equation of state for water are complex due to the phase change that occurs at typical engineering conditions, making them ill-suited for an approach similar to the treatment of ideal gases. Instead, property charts can not only supply the property values they allow students to visualize processes and cycles. Engineering students already sketch their cycles on temperature-entropy (T-s) charts, to study various modifications of the Rankine Cycle. Such qualitative property chart sketches are useful for highlighting how most ideal engineering processes follow constant property lines. Instead of the sketches serving a supplementary purpose, property charts can provide both the property values and the ability to visualize the interdependence of thermodynamic properties. Using property charts this way, not only allows students to retrieve state properties from a single chart but also contextualizes the state with respect to other relevant parameters surrounding the state (Dixon, 2001). Returning to Figure 1, we can replace the equation of states with property charts for each substance while equation of state serves a supplementary function. Property charts demonstrate how the properties will evolve as a result of a property change by navigating the two-dimensional chart. Repeated use of property charts can allow students to develop a mental model of water properties and its

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inherent relationships, even in the absence of an explicit equation of state. And considering the vast majority of thermal design and analysis within engineering practice relies heavily on computational resources, such models serve as important human checks for the advanced modeling. Therefore, developing an intuitive understanding of water property relations is a better student learning outcome for engineering thermodynamics courses. This chapter details an effective instructional framework for integrating property charts within thermodynamics courses to reinforce water property relations. Instructional framework and relevant tools are presented to facilitate adoption of this visual approach and advance thermodynamics instruction to match the current educational needs. The framework is supported by effectiveness studies and student feedback on the practice. A summary of the evidence is presented within this chapter. The outcomes are overwhelmingly positive and easily transferable to motivate broad pedagogical change within engineering thermodynamics.

BACKGROUND Numerous studies in the broad field of information visualization exist that demonstrate the utility of graphs in developing internal representations or mental models (Liu and Stasko, 2011 & Purchase, et al., 2008). Often these mental models are inaccurate but afford accessibility to the complex ideas. Within fluid mechanics, instructors do not provide the original tabulated values by Johann Nikuradse (1894-1979) for internal pipe flow. Instead, students are presented with the data on the ubiquitous Moody Chart, first presented by Lewis F. Moody in 1944. The Moody Chart lacks accuracy but makes up for it by presenting the broader trends. Within thermodynamics, psychrometric charts are extensively used to study moist air processes for HVAC applications (Baughn, 2007). Precise equations exist for moist air calculations, however the psychrometric chart visual interdependence of the various parameters. Both these examples forgo accuracy for convenience and improved learning outcomes (Manteufel,

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2013). The traditional reliance on the steam tables was justified in the absence of cheap computational tools. With readily available data, there is a need to reevaluate the utility of steam tables in the modern age and identify the fundamental knowledge that we wish to impart. Rather than focusing on the exact values that can be readily delivered by a personal device, we must refocus our attention on the property relationships to develop intuitive grasp of water behavior. Mental schemas are powerful for developing intuitive understanding of complex topics. Hence, a graphical approach to thermodynamic properties of water has been advocated by a number of other engineering educators, including Urieli (2010) and Maixner (2006). Pfotenhauer, et al., (2015) for instance, developed a three-dimensional gaming environment using the pressure-specific volume-temperature (PvT) space to visualize the interdependence of properties. Yet, these tools perform supplementary functions and are challenging to adopt without an effective instructional framework. A framework that is well integrated within thermodynamic courses and leverages the existing content. For instance, how is the new treatment of water properties map to the current discussion of ideal gas behavior? The existing pedagogy suggests the two fluids are conceptually disconnected – ideal gases have a simple equation of state, where as water does not (see Figure 1). Instead, instructors must identify the common framework between the two fluids that can augment the understanding of both. Displaying both the fluids on a property chart can bridge this gap. The instruction of thermodynamic properties of water still relies on the steam tables due to their historical integration within textbooks and the lack of effective instructional approach using property charts. Additional challenge to the adoption of property charts comes from the prevalent use of steam tables within the FE exam. To counter that we must recognize the FE exam already relies exclusively on the pressure-enthalpy (P-h) property chart for refrigerant R-134a to solve refrigeration problems; at the same time supplying the tables to solve steam problems. However, the greatest challenge to the introduction of property charts as an alternative to steam tables is inertia. The resistance by faculty who have always taught using steam tables to switch. This chapter is designed to provide step-by-step

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instructions and resources to facilitate this change. Including how assessments can be organized to accept a range of property values. The long term benefit of the shift away from steam tables is (a) improved learning outcomes, (b) reduced instructional load, and (c) assessments that are conducive to rapid-feedback. Furthermore, property charts fit on a single sheet of paper and thus eliminating numerous textbook pages that are dedicated to the steam tables.

INSTRUCTIONAL FRAMEWORK The instructional framework is developed based on past research studies presented later in the chapter. The framework has been tested and iterated for effectiveness. Eventually, a blended-learning approach was selected and presented here. A variety of supplemental tools were used to enhance student engagement with the alternate content. The frame is design to create connections with the existing content rather than replacing entire portions of an engineering thermodynamics course.

Teaching with Property Charts The instructional design focused on effective integration of property charts across multiple topics within thermodynamics courses. For this, property charts were used as a common thread as a way to visual properties of substances. The presentation of property chart requires a staged construction of trends. This visual nature of the property charts subsequently led to the development of instructional videos that were produced to incorporate the dynamic features of the water property chart construction. The instructional videos not only allow gradual buildup of the property charts but also enable students to review the content at a later stage. The introduction of instructional videos naturally paved way for a flipped classroom setting. The flipped classroom approach in turn reduced instructional burden and allowed more classroom time to be dedicated to

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practice. Thus, the instructional approach discussed below was developed over a number of semesters with resources evolving alongside. The approach is conceptually described to frame the instructional videos that have been developed. The instructional videos are publicly available for review and adoption. Instructors are encouraged to incorporate the resources that best suits their teaching style. 1. The ideal gas beginning. Engineering Thermodynamics begins with the discussion of substances and their properties. Figure 1 is a typical path of the content. Using the ideal gas model, students become familiar with the terminology and analytical approach. Students are also exposed to the concepts of properties, states, and processes within the content of the ideal gas equation of state. For the new implementation, the ideal gas equation of state Pv = RT, is deliberately presented on a two-dimensional PvT space, also known as the property charts (P-v, T-v, and P-T). Processes involving change of states is also depicted on property charts, as shown in Figure 2. It is important to emphasize the clear connection between the simple mathematical EOS and its twodimensional representation on a property chart. Identify the constant P, constant T and the constant v lines on the property charts and discuss how each point on the property chart is a manifestation of the underlying Pv = RT relationship. Instructors may ask students to generate a property chart using the ideal gas equation of state. Conversely, given a property chart of a gas, students can directly retrieve its state properties. 2. The Bridge over Water. The ideal gas model applies to idealized model of the gas molecules. This obvious limitation of the ideal gas model is discussed using the temperature-specific volume (T-v) chart shown on the right within Figure 2. Here the focus is on how real gases behave with phase change on a property chart. Figure 3 presents a key-frame schematic contrasting the ideal versus real gas condensation behavior used to introduce properties of water. This modified schematic demonstrates that as an ideal gas is

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cooled, the model relationship predicts smaller and smaller volume occupied by the gas until the specific volume v approaches 0 m3/kg. This apparent disagreement between the model and reality is corrected by the constant temperature phase change process (a.k.a., condensation) that is analogous to the familiar properties of another common substance, namely water. The students are also informed how the EOS for a real gas, or water for that matter, is more complicated than Pv = RT and must be corrected when used in engineering practice. Yet we can use such charts to acquire their properties if the correct values are plotted. Students are asked how the behavior of real gases can be captured by a mathematical equation. The subsequent discussion seeds the creation of a mental model for property relationships. Other relevant energy-based properties (internal energy, enthalpy, and entropy) are also discussed to emphasize their importance for thermodynamic analysis. 3. Water with Phase Change. Using the T-v chart and a pistoncylinder setup commonly found in textbooks, a single isobar is constructed step-by-step via a heating process. The construction process for the isobar is the reverse of the discussion around Figure 3. Additional isobars are plotted by increasing the weight on the piston. A similar approach is followed within most textbook treatment of water properties, however the bridging concept of real gas properties of often absent. This rudimentary property chart lays the foundation for a detailed chart that includes mass specific energy components. Figure 4 presents a simplified T-s property chart to demonstrate how properties relate to one another and that every point on that chart represents a state. At this point, entropy is presented as ‘just another thermodynamic property’ but the similarities between the Figure 4 chart and the T-v chart for water are highlighted. Several points on this chart are studied to retrieve P, T, v, h, u, and s values. The same chart is used to develop a definition of quality x within the saturated mixture region as a fractional distance across an isobar or an isotherm.

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Figure 2. Ideal gas model presented on the P-v, P-T, and T-v property charts. Lines of constant temperature (isotherms), constant specific volume (isochors), and constant pressure (isobars) are displayed.

Figure 3. A T-v property chart schematic contrasting ideal gas behavior and real gas behavior; and how real gas behavior is analogous to water. This key-frame schematic is used to launch a discussion of water properties.

Resources for Property Charts Instruction Instructional Videos The foregoing instructional framework has been produced in detail as a set of instructional videos. Three animated videos were produced addressing each stage of the framework. The videos discuss the utility of

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property charts within the ideal gas model (video titled, “Thermodynamic behavior of ideal gases”), how they can be a powerful tool for studying properties of water (video titled, “Thermodynamic Properties of Water”), and how detailed property charts can used to obtain water properties (video titled, “Using Property Charts of Water”). Each video is approximately 8 minutes long and illustrates the information in a dynamic and engaging fashion. Figure 5 provides screenshots of the videos produced. The students are asked to watch the videos outside of class time and respond to multiple choice questions to ensure they reviewed the content prior to class. While the concepts covered the videos can be covered during lectures, the highly visual and dynamic nature of the content is more effectively presented via the embedded animations. The last video goes through several sample retrieval steps across the property charts to elucidate the simplicity of this approach. The videos are freely available on YouTube.com for adoption and referenced within this chapter.

Figure 4. A simplified T-s property chart with constant pressure P, specific volume v, and constant enthalpy h lines plotted. This chart is used for instructional purposes only.

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Figure 4. Screenshots of (top) Thermodynamic behavior of ideal gases, (center) Thermodynamic behavior of water, and (bottom) Using Property Charts of Water videos animated and produced by the author. Each video is approximately 8 minutes long that include dynamic visual elements to illustrate key features of property charts and are shared publicly on YouTube.com.

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Detailed Property Chart Whether videos are used for primary instruction or a traditional inclass lecture, a substantially less time is dedicated to the mechanics of property retrieval using property charts due to the intuitive format of the data. An accurate T-s property chart by Cengel & Boles, Property Tables Booklet (2015) can be shared with the students to solve textbook engineering problems. Alternatively, instructors may use a P-h chart for the same effect. These paper-based property charts fit on a single sheet of paper and accommodate the typical pressure and temperature conditions seen by engineering students. Figure 5 presents a photo of students using a detailed property chart to retrieve thermodynamic properties. Class time is used to discuss any follow-up questions to the videos and clarify the steps of the retrieval process. The discussion usually takes 15-minutes. The discussion is followed by example problems involving water. Next, students participate in a think-pair-share activity to practice their retrieval skills for the rest of the class period. Past experiences suggest that students quickly become comfortable with property retrieval by the end of one 50min lecture period.

Figure 5. Students using a detailed temperature-entropy (T-s) property chart for retrieving thermodynamic properties. A complete set of property values are presented on a single page.

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Supplemental Media To further engage with the property charts, two more supplemental resources were developed and used. An iPad app, called Clausius, was developed to reinforce the concepts covered (https://youtube /U34Dn5NZacA; Bakrania & Carrig, 2016). Clausius presents T-s, P-h, and P-v charts with properties changing dynamically as the users glide their fingers across the tablet screen. The app is used during the introductory discussion to highlight the interdependence of the properties and how they evolve across the property charts. Figure 6 provides a photo of the Clausius app. Furthermore, each student in this class received a T-s chart t-shirt to prompt discussions outside the confines of the course (Bakrania, 2017; Bakrania & Mallouk, 2017). The t-shirts were designed to promote familiarity with the diagrams and encourage a sense of belonging to the course (Shwartz, Blair, & Tsang, 2016). The t-shirt chart is sufficiently detailed to estimate thermodynamic properties of water for common thermodynamic problems.

Other Impacts of the Instructional Framework Changes to the Assessments Students using the property charts may not arrive at the same answer. This inherent uncertainty can be addressed by allowing a small range of answers to be acceptable. For homework assignments, a 10% deviation from the textbook solution can be considered acceptable. Alternatively, acceptable final answer ranges can be supplied to further guide student assignment attempts. Ideally, assessments can digitized to accept multiple choice responses. This approach has the advantage of reducing evaluation load and provides rapid-feedback to the students. Previous work on developing a multiple-choice test that provide immediate feedback to the students using property charts has been shown to be effective (Wildgoose and Bakrania, 2017). Besides, delayed feedback during assessment has been shown to be detrimental to the student learning outcomes.

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Figure 6. (Left) A photo of the Clausius mobile app used to reinforce the interdependence of thermodynamic properties in real-time. Properties change in realtime in response to the user’s touch. (Right) T-shirt designed to engage with T-s charts outside the course.

Advanced Treatment The use of property charts does not end with the introduction of water behavior. The subsequent topics also require the use of steam properties. For instance, during Rankine Cycle analysis, students are asked to draw their processes directly on the paper-based T-s chart for a realistic depiction of change of states. Students can submit their annotated charts for assessments as well. While the existence of the steam tables (and their digital alternatives) is mentioned during instruction, these are not explicitly taught or used during lectures. The students are made aware of the need for accuracy within their professional careers and how to retrieve thermodynamic properties of various substances from digital resources – as it is done in engineering practice. This approach has been followed and improved four times in the past, with the latest iteration involving all three videos, and will be used in the future offering of the Thermal-Fluid Sciences course by the instructor.

EVIDENCE OF EFFECTIVENESS Research studies involving the use of property charts have consistently shown the broad benefits for instructors and students alike. Overall, the

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property charts reduce instructional load because of their intuitive nature and help students internalize property trends. Our first study with the property charts is presented in Bakrania and Carrig (2016). Here students that used the interactive property charts within the iPad app grasped the relations and were far better at answering property trend questions can those who used steam tables. A subsequent study used paper-based charts to show that students can predict changes in properties better with charts than with steam tables (Bakrania and Mallouk, 2017). Later, Bakrania and Haas (2019) highlighted that students perform better on engineering problems involving steam as a working fluid while using property charts. The benefits extend to the instruction of water thermodynamics as well, as detailed in Bakrania (2017). These studies corroborate similar investigations using visual representations for conceptual understanding. To capture the effectiveness of property charts instruction, a study exploring the ability of property charts to promote mental models among students is presented here. The study compares the accuracy of mental models generated by the students using steam tables versus paper-based property charts. To test the accuracy of the mental models, groups of students were asked to predict evolution of two arbitrary thermodynamic states. The study investigated the hypothesis that property charts allow students to develop a better mental model of property relationships. On the contrary, the use steam tables is not conducive to the development of a property relation mental model. The accuracy of the mental model was tested by its ability to predict changes in the properties, not unlike a test of any physical or mathematical model. It was thought that the group with better predictive abilities, possessed a better mental model of property trends. Students who participated were also surveyed for their feedback on the implementation.

Methodology The intervention was designed using a control group and a treatment group of students. A section of a Chemical Engineering (Ch.E.)

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Thermodynamics course was selected as the control group. A section of Mechanical Engineering (M.E.) Thermal-Fluid Sciences was selected as the treatment group. The control section relied on the steam tables for retrieving properties of water while the treatment section relied exclusively on property charts. The rest of the content surrounding thermodynamic properties of water was kept well-aligned between the two groups. Even though the control group relied exclusively on the steam table, they were nonetheless familiar with the qualitative T-s chart. The control section frequently sketched their thermodynamic processes on a qualitative T-s chart. The study was scheduled for the end of the term to ensure both sections were sufficiently experienced with the two distinct sources of water properties. Both sections were assessed for their ability to predict property changes for the two colored states shown on Figure 7. Students were presented with an identical chart and asked to how T, P, v, h and s properties will change as the states colored blue or yellow move from their original location to the two connected numbered state. The blue state moved states 1 and 2 and the yellow state moved from states 3 and 4. As a result of the change in states, the students were asked to select the options “increases”, “decreases”, “remains the same”, and “not enough information” as their predictions for each property. Importantly, the students had to predict without referring to either the steam tables or property charts. This restriction forced the students to consult their mental models to respond. If the hypothesis that property charts help students build a better mental model was correct, students in the treatment section would be better at predicting the evolution of properties as the dots move to the labeled states. Conversely, students who relied on the steam tables would find it challenging to predict the change due to the steam tables’ inherent focus on point values. The setup in Figure 7 does not necessarily place the control group at a disadvantage because these students regularly sketched processes on similar T-s charts.

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Figure 7. T-s sketch presented to the control and treatment sections for the effectiveness study. Students were tested on their ability to predict the thermodynamic property changes as the two colored dots moved to the labeled states.

Figure 8. A comparison of mean scores from the control and treatment sections. The scores represented percent correctness and the error bars represent a single standard deviation from the mean. The associated p-value was calculated to be less than 0.0001.

Results and Discussion The study was conducted during regular lecture period with instructor supervision. The intervention used Google Forms to collected student

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responses from the control section (N = 22) and the treatment section (N = 35). Students used their personal smartphones to work independently and without any other external resources to respond to the questions. The responses from the control and the treatment sections were collected while maintaining anonymity and analyzed for correctness. The mean percent correct predictions were computed for each section. Figure 8 presents the outcome from the analysis from the control and treatment sections. The treatment group performed statistically better than the control group with an associated p-value calculated to be less than 0.0001. Therefore, even with a slight overlap in the standard deviation displayed in Figure 8, the difference in performance was significant. The results depicted in Figure 8, show that the students who used the property charts were better at predicting changes compared to the students who used steam tables. Even if the students using steam tables were used to sketching the qualitative T-s charts, this additional visualization step did not help the control group develop a model of the property relations. Students using the property charts possessed a better model of the property relations compared to students using steam tables. The results from the effectiveness study highlight the distinct advantage of integrating property charts over the steam tables. Students internalize the property trends simply by using property charts without any additional specialized sketching steps. The current practice of using steam tables with a sketch is thus lacking in this regard. The advantages were also reflected in student feedback survey. To gather students’ insights on the new approach, the treatment group students were asked how property charts helped them. This was an openended survey where students were asked to comment with specific prompts. The responses were categorized by themes and are summarized here. Approximately 44% of the students surveyed noted that the property charts helped them visualize the thermodynamic relationships. The steam tables fail to augment student understanding of the trends. With property charts, property retrieval and visualization is combined in a single step. For instance, one student noted, “Visualization is key to understanding the processes”. Interestingly, 19% of the responders thought the property

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charts allowed rapid retrieval of properties when compared to the steam tables. How did they know if they only used property charts? A follow-up discussion with the class on this response revealed some students occasionally used the steam tables to check their answers for accuracy and hence recognized the involved nature of the steam table retrieval process. 14% of the responders felt the property charts helped them recognize property trends and another 14% felt the property charts helped them better understand property relationships. The remaining 8% of the responses were categorized as ‘Others’ with comments, such as, “Eliminates unnecessary calculations that are needed when using the steam table”, likely referring to the elimination of interpolation or saturation quality calculations that are common with steam tables. The combined outcome of the study and the feedback is that the effective use of property charts can approach the same level of comfort as the use of ideal gas model equation of state (EOS) does in a typical thermodynamic classroom as far as the overall relationship is concerned. Students are able to generate a mental model of the property relations without an explicit EOS; and they are able use these models to predict changes in properties – an outcome similar to various other visual approaches. From an instructional perspective, due to the visual nature of the property charts, less time is devoted to training students to use the steam tables and more time is allocated to the key concepts surrounding properties of water. Instructional videos further help students develop conceptual understanding and can augment or replace traditional instruction. A subsequent study on the use of the discussed instructional videos showed students relied heavily on them for review prior to assessments (Bakrania and Haas, 2019). Instructional videos, thus, can serve as an additional resource. Both the instructional effectiveness study and the student feedback support the switch to property charts. The replacement of steam tables with property charts imposes minimal instructional load with an appreciable gain in student learning outcomes. The instructional implementation combined with the resources presented here provide a strong foundation for a broader change in curriculum that is better aligned with the needs of the engineering profession. Specifically,

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more emphasis on the broader concepts that will guide their engineering intuition and less focus on the exact values that are abundantly and easily available in the digital age.

CONCLUSION Students naturally gravitate to readily available data using various digital thermodynamic tools. Instead of restricting the path of least resistance we can ease the acceptable path that benefits both instructors and students. A student who used the property charts stated, “Old school steam tables are a thing of the past with modern software”. These modern tools exacerbate the problems that began with the use of steam tables. The steam tables and their digital alternatives supply answers without reinforcing the fundamental thermodynamics concepts. They give preference to property values using an algorithmic process over property relationships. Instead, we must shift our focus away from the steam tables and adopt property charts for the introduction of water thermodynamics. In addition to supplying property values, property charts allow students to develop intuitive understanding of the property relations by building a mental schema. This chapter presented a detailed instructional framework along with supporting resources for integrating property tables within an engineering thermodynamics course. The resources are readily available for adoption. A number of studies have been conducted to demonstrate the effectiveness of this approach. The subsequent study, presented within this chapter, showed that students developed a better mental model of property relations using property charts over mental models generated using steam tables. Once the students develop a foundation in thermodynamic relationships, they can begin to explore computer-added thermodynamic design for advanced applications. This treatment is similar to the approach followed within solid mechanics and fluid mechanics courses. For instance, fundamentals of fluid motion are taught before embarking on CFD-based analysis of more complicated structures. The intuitive understanding developed during the introductory stages function as an

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evaluative tool when dealing with advanced systems. The outcomes and the overall proposal is well-supported by extensive literature on the impact of visualization on human cognition. The resources presented within this chapter provide sufficient guidance for the proposed instructional switch.

ACKNOWLEDGMENTS The author would like to acknowledge the following members of Rowan University community: the students who participated in the effectiveness study and the survey, Dr. Iman Noshadi from the Chemical Engineering Department, Dr. Fancis (Mac) Haas, Dr. Kaitlin Mallouk, and Dr. Tom Merrill from the HMR College of Engineering for contributing to the previous studies, and Dr. Krishan Bhatia from the Mechanical Engineering Department for pioneering the replacement of steam tables.

REFERENCES Bakrania, SD. “Are Steam Tables running out of steam?” American Society for Engineering Education, Zone II Conference, Puerto Rico, (March 2017). Bakrania, S; Carrig, A. “Touching Water: Exploring Thermodynamic Properties with Clausius App,” Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana, (June 2016). Bakrania, S; Haas, FM. “Teaching Thermodynamic Properties of Water Without Tears,” Paper presented at 2019 ASEE Annual Conference & Exposition, Tampa, Florida (June 2019). Bakrania, S; Mallouk, K. “Blowing Off Steam Tables,” Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio, (June 2017). Baughn, J; Maixner, M. “Teaching Psychrometry To Undergraduates,” 2007 ASEE Annual Conference & Exposition, Honolulu, Hawaii (June 2007).

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Cengel, YA; Boles, MA. “Property Tables Booklet for Thermodynamics: An Engineering Approach,” McGraw Hill: Boston, (2015). Clausius Introduction, Accessed October 30, 2019, https://youtu.be/ U34Dn5NZacA. Dixon, G. “Teaching Thermodynamics Without Tables Isn’t It Time?” 2001 ASEE Annual Conference, Albuquerque, New Mexico (June 2001). Engineering Equation Solver (EES), Accessed May 23, 2019, http://www.fchart.com/ees/. Hagge, M; Amin-Naseri, M; Jackman, J; Guo, E; Gilbert, S; Starns, G; Faidley, L. “Intelligent Tutoring System Using Decision Based Learning for Thermodynamic Phase Diagrams,” Advances in Engineering Education, 6 no. 1, (2017). IRC, “Fluid Property Calculator,” Accessed May 23, 2019, https://www.irc.wisc.edu/properties/. International Steam Tables IAPWS-IF97 app, Accessed May 23, 2019, https://itunes.apple.com/us/app/international-steam-tables/ id502937992?mt=8. Liu, Z; Stasko, J. “Mental Models, Visual Reasoning and Interaction in Information Visualization: A Top-down Perspective,” IEEE transactions on visualization and computer graphics., 16, (2011), 9991008. Maixner, M. “Interactive Graphic Depiction Of Working Fluid Thermal Properties Using Spreadsheets,” 2006 ASEE Annual Conference & Exposition, Chicago, Illinois (June 2006). Manteufel, RD; Karimi, A. “Influence of uncertainties and assessment of significant digits in thermodynamics” 2013 ASEE Annual Conference, Atlanta, Georgia., (June 2013). Miller, K. “A Survey On The Use Of Printed Vs. Electronic Vapor Tables” 2007 ASEE Annual Conference & Exposition, Honolulu, Hawaii, (June 2007). NIST Chemistry WebBook SRD 69, “Thermophysical Properties of Fluid Systems,” Accessed May 23, 2019, http://webbook.nist.gov/chemistry/ fluid/.

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Pfotenhauer, JM; Gagnon, DJ; Litzkow, M; Pribbenow, CM. “Game Design and Learning Objectives for Undergraduate Engineering Thermodynamics,” 2015 ASEE Annual Conference and Exposition, Seattle, Washington. (June 2015). Purchase, HC; Andrienko, N; Jankun-Kelly, TJ; Ward, M. “Theoretical Foundations of Information Visualization,” In: Kerren A., Stasko J.T., Fekete JD., North C. (eds) Information Visualization. Lecture Notes in Computer Science, vol 4950. Springer, Berlin, Heidelberg (2008). Schwartz, DL; Blair, KP; Tsang, JM. “The ABCs of How We Learn: 26 Scientifically Proven Approaches, How They Work, and When to Use Them,” W.W. Norton Company, (2016). SmoWeb, “Property Calculator” (based on CoolProp), Accessed May 23, 2019, http://platform.sysmoltd.com/ThermoFluids/ FluidPropsCalculatorView. Spallholz, L; Balmer, R. “21st Century Thermodynamics” 2006 ASEE Annual Conference & Exposition, Chicago, Illinois., (June 2006). Steam Tables app, Accessed May 23, 2019, https://itunes.apple.com/us/ app/steam-tables/id339948012?mt=8. Taylor, R; Chappell, J; Woodbury, K. “Introducing Excel Based Steam Table Calculations Into Thermodynamics Curriculum” 2008 ASEE Annual Conference & Exposition, Pittsburgh, Pennsylvania, (June 2008). Thermodynamic Properties in Python, Accessed November 4, 2019, http:// pyromat.org. Thermodynamic Behavior of Ideal Gases, Accessed October 30, 2019, https://youtu.be/W3GeydKjc60. Thermodynamic Properties of Water, Accessed October 30, 2019, https://youtu.be/rJR-6OEw09k Urieli, I. “Engineering Thermodynamics: A Graphical Approach,” Paper presented at 2010 ASEE Annual Conference & Exposition, Louisville, Kentucky, (June 2010). Using Property Charts of Water, Accessed October 30, 2019, https:// youtu.be/SFjNByAz03w.

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Wildgoose, A; Bakrania, SD. “Development and implementation of rapid feedback using cloud-based assessment tool,” Frontiers in Engineering, Indianapolis, Indiana., (October 2017). Steam, X. Thermodynamic properties of water and steam, Accessed November 4, 2019, https://www.mathworks.com/ matlabcentral/ fileexchange/9817-x-steam-thermodynamic-properties-of-water-andsteam.

In: Mechanical Engineering Education … ISBN: 978-1-53617-791-6 Editor: Charles E. Baukal, Jr. © 2020 Nova Science Publishers, Inc.

Chapter 11

MATERIALS EDUCATION FOR MECHANICAL ENGINEERS Matthew Cavalli1 and Surojit Gupta2 1

2

Western Michigan University, Kalamazoo, Michigan, US University of North Dakota, Grand Forks, North Dakota, US

Keywords: materials science, active learning, entrepreneurship, distance learning

INTRODUCTION Every mechanical engineering design requires the use of real materials. Understanding how those materials behave is an essential consideration in creating safe, effective, and efficient machines and structures. Many mechanical engineering curricula have a single required course that focuses on materials behavior. Other materials-focused courses may be offered as electives. Materials-related topics are sprinkled throughout a number of additional courses such as mechanics of materials, heat and mass transfer, and thermodynamics. What are effective techniques for teaching the

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concepts of materials behavior (and their relationship to mechanical engineering design) in that limited amount of time? This chapter will investigate the teaching of materials science to mechanical engineering students via the implementation of various active learning techniques including distance learning options. We will also touch on contemporary topics in materials education including entrepreneurship and sustainable materials in support of Circular Economy.

IMPACTFUL PRACTICES IN UNDERGRADUATE EDUCATION Learning (and, by extension, teaching) is a complicated endeavor. It involves a complex interplay of biological, social, and psychological factors. Recent work in neuroscience has provided deeper insights into the learning process than ever before. For example, students have long been told that successful learning starts well before class with a good night’s sleep, exercise, and good nutrition – and science has finally been able to show how each of these impacts the brain’s ability to learn (Doyle and Zakrajsek 2013). Instructors have long railed against extended ‘cram’ sessions as an ineffective way of studying – and research has shown how regular, short periods of intense study interspersed with sessions of unfocused relaxation (or even sleep) improve the brain’s ability to recall information and make new connections (Carey 2014; Oakley 2014). While the ability to study the physical basis of learning represents a huge advance in our understanding of learning and how to improve it, many of the implications are consistent with prior educational studies. As mechanical engineering students progress through their undergraduate degree program, their expertise within the field increases. Litzinger et al. (2011) presented an overview of the current understanding of how people develop expertise. They synthesized this with recommendations for educational practices based on students’ approaches to learning and the role of motivation in learning. The authors conclude that expertise is not simply about an increased quantity of knowledge compared to a novice. Expertise develops effectively when students

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cultivate deep conceptual knowledge and the ability to apply technical and professional skills, often by participating in multiple authentic engineering projects. That is not to imply that students are experts at graduation – but two critical functions of undergraduate education are to create a solid foundation of integrated knowledge and to provide graduates with the tools and skills to build on that foundation throughout their professional careers. Multiple researchers have shown that some educational approaches are more effective at this than others. Kuh (2008) synthesized recent educational research into what he termed “high-impact practices” or HIPs:          

First-Year Seminars and Experiences Common Intellectual Experiences Learning Communities Writing-Intensive Courses Collaborative Assignments and Projects Undergraduate Research Diversity/Global Learning Service Learning, Community-Based Learning Internships Capstone Courses and Projects

Each of the practices had been shown to significantly increase student success. The practices have been found to be even more effective when multiple HIPs are systematically integrated throughout a student’s degree program. Materials education can be integrated into most HIPs, regardless of the stage in the curriculum. All engineering designs make use of real materials. For example, the design of the frame for a Formula-1 car as part of a capstone senior design project requires not only an understanding of stress and strain, but also material properties like Young’s modulus and yield strength. Undergraduate research into innovative designs for phase change material-based heating/cooling systems requires an understanding of the enthalpy of phase changes as well as heat capacity and thermal

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conductivity in addition to the concepts of heat transfer. A communitybased project to design a new playground structure will need to consider materials issues like fracture toughness, corrosion, and stiffness. The level of detail of the materials information can be tailored, as needed, to match students’ background, in the same way that stress analyses in Mechanics of Materials are less involved than those in a Machine Design or Senior Design course. Chickering and Gamson (1987) made an extensive review of research on undergraduate teaching and learning. They distilled their conclusions into seven ‘good practices’ for effective undergraduate education:       

Student-faculty contact Active learning Prompt feedback Time-on-task High expectations Respect for diverse learning styles Cooperation among students

The authors highlighted the benefit of each of the practices on its own in supporting student learning and success. However, they stressed the multiplying factor that can occur when most or all are present for students. Christie and de Graaff (2017) present an excellent overview of theoretical support for active learning as well as its development and implementation over the past century or so. Freeman et al. (2014) performed a meta-analysis of 225 studies related to student performance in a variety of science, technology, engineering, and mathematics (STEM) courses. They showed that average exam performance for students who engaged in active learning techniques was approximately 0.47 standard deviations higher than for students taught with a ‘traditional’ lecture method. A much smaller portion of the active learners typically failed the course, as well (Freeman et al. 2014). As we will show below, despite the fact that ‘active learning’ was identified by Chickering and Gamson (1987) as a distinct good practice, its implementation has the potential to include

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most, if not all, of the other good practices, as well. It also turns out to fit well with our improved understanding of effective ways to learn.

ACTIVE LEARNING IN MATERIALS SCIENCE Most active learning approaches can be categorized as either collaborative or cooperative. According to Prince (2004), the primary distinction is in the final assessment of work. In collaborative work, the group is assessed as a whole, while in cooperative learning students work together but are assessed individually. Johnson and Johnson (2009) present a more complete summary of this definition of cooperative learning. Because the distinction between the two categories can be blurred in a given active learning implementation, we will use the term collaborative to encompass all such approaches for the remainder of this text. Smith (1994) highlights that collaborative learning groups can take multiple forms, from short-term, less structured groups to formal learning groups focused on completing a specific task, to cooperative base groups which are long-lasting and supportive in nature. The composition and function of student groups is critical to achieving the collaborative learning goals (Rossetti and Nembhard 1998). It is also important that the instructor makes clear distinctions between assessment of the group and assessment of group members. Assessment approaches that potentially penalize collaboration will undermine the stated goals of the activity (Rossetti and Nembhard 1998). Meyers and Jones (1993) highlighted four key aspects of active learning activities: talking (and listening), reading, writing, and reflecting. These are shown in Figure 1. Johnson and Johnson (2009) outlined five factors which are critical in determining the success of the collaborative learning, namely, “positive interdependence, individual accountability, promotive interaction, the appropriate use of social skills, and group processing.” Research has shown that working together can also increase student satisfaction (Maxwell-Stuart et al. 2018). Zemke et al. (2004) implemented 15 distinct collaborative learning events in a materials

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science course. Each event incorporated a different combination of educational approaches and pedagogies. The authors concluded that collaborative learning approaches that integrated both concepts and applications tended to be the most effective, regardless of the specific aspects of the educational intervention. In contrast, collaborative learning focused only on concepts or applications was less impactful. Kolb (1984) hypothesized that learning rests on a foundation of experience. He constructed what has come to be known as the Kolb Cycle for Learning (Figure 2). Kolb theorized that learning was most effective when the entire cycle was incorporated into the educational experience. As Linsey et al. (2009) point out, traditional lecture tends to focus on an isolated portion of the learning cycle (typically abstract hypothesis/conceptualization) at the expense of the other segments. This leads to incomplete learning and a restricted ability to integrate concepts and ideas across domains. Effective active learning, on the other hand, requires students to use the full learning cycle, though the specific mechanics of the process vary from technique to technique.

Figure 1. Four essential components of active learning (modified from Meyers and Jones 1993).

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Figure 2. The Kolb cycle for experiential learning (after Kolb 1984).

While significant literature is available about the benefits of active learning for student learning and success, a note of caution regarding active learning is warranted. Active learning is often implemented in a course setting in some form of small group activity. Careful monitoring of group dynamics is essential to ensuring that all members contribute to achieving the desired educational outcomes and that no students are marginalized. For example, Keogh et al. (2018) found that female students tended to take on non-technical roles in group activities at a disproportional rate compare to their male counterparts. Focus groups led by the researchers revealed a combination of lower self-efficacy amongst female engineering students and marginalization by their male team members tended to contribute to the skewed project roles. Similar phenomena have been observed among underrepresented minority engineering students (e.g., Beasley and Fischer 2012). Attentive oversight on the part of the instructor can help reduce the potential negative influences of active learning group activities.

Laboratory Activities Sheppard et al. (2009) point out that laboratory activities can reinforce multiple learning goals including: 

Learning fundamental concepts,

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Learning to apply concepts to solve practical problems, Learning to work with complex engineering systems, Learning how to communicate professionally, Learning to work in teams, and Learning to acquire attitudes of persistence, healthy skepticism, and optimism

Arrayed against the educational benefits of laboratory experiences are considerations like staffing, space, equipment, and supply costs. It is challenging to ensure that the laboratory component of a course or degree program is coordinated with the non-laboratory components. In addition, laboratory classes often require more student time commitment than are acknowledged in the course’s associated credit hours (Sheppard et al. 2009). We provide a short survey below of ways that materials sciencerelated laboratory activities can be integrated into the mechanical engineering curriculum so satisfy some subset of the learning goals listed above. Rynearson and Polasik (2019) reported on classes taught in a Classroom Laboratory setting in which the space for lectures/discussions and experiments are physically integrated. The ‘lecture’ portion of the course includes Jigsaw, Think-Pair-Share and Problem-based Learning activities (see below). Regular laboratory experiments complement the material presented and discussed in lecture. The overall class has been structured according to the Interactive, Constructive, Active, and Passive (ICAP) framework (Chi and Wylie 2004). The laboratory activities bear some resemblance to Jigsaw activities themselves, with students sharing results and observations amongst multiple teams, often resulting in new insights and conclusions. This laboratory structure requires significant investment with regards to the teaching space, limitations on section size, and access to equipment to achieve the educational outcomes but is expected to provide very positive results. Rather than a full semester-long lab component, targeted activities can be used, instead. Cavalli (2017) reported on the implementation of a semester-based laboratory design project in a composites material course

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that included fabrication and testing of composite beams. Preliminary portions of the project included analytical stress analysis and predictions of composite behavior. One to two lecture periods were used to meet in the workshop to fabricate and test fiber-reinforced polymer composite beams. Students then compared their predictions of beam behavior with the observed performance and commented on issues like the fabrication process and sources of error. Students enrolled in the course at a distance participated in the laboratory exercise via a kit of materials supplied to them, but they could have assembled the required materials from most home improvement or automotive repair stores. Both on-campus and distance students reported a positive value of the activity with regards to their overall understanding of course concepts (3.4/5 and 3.5/5 on a Likert scale). Many mechanical engineering students are interested in cutting-edge fields like nanotechnology or biomedical engineering. Laboratory activities can be an excellent way to excite those students into pursuing graduate education or participating in undergraduate research. Al-Haik et al. (2010) describe the implementation of multiple experiments related to nanotechnology and nanoscale measurements into a materials science course as part of a mechanical engineering curriculum. Students broadly supported the integration of the cutting-edge concepts into their general course and a number of participating students later decided to pursue nanotechnology research during their studies. Due to the associated costs of physical laboratories, the number of virtual lab exercises in engineering education continues to expand. For example, Cavalli and Bibel (2005) describe the development and implementation of a virtual lab on the processing and properties of steel that replaced a sequence of hands-on lab experiences. The use of a computer interface in place of actual testing equipment necessarily removes some of the hands-on benefits of experimentation. This was noted by some students in the course. However, aspects of experimentation like hypothesis testing and data analysis are conserved and some students appreciated the way that the virtual lab could target key concepts that sometimes get lost in the details of machine calibration and test setup. The

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quality and sophistication of virtual lab activities continues to improve; they can be a viable option, particularly when the alternative is no lab experience at all. Courses that are not ‘materials’ courses per se can also be improved through the implementation of targeted lab activities. Miller et al. (1998) detail the development and implementation of three experiments that can be used in multiple courses across a mechanical engineering curriculum including a three-point bend test for elastic modulus, a thermal conductivity determination, and stress concentrations via a photo elastic material. The experiments were used in a variety of sophomore-level courses including Materials, Mechanics (Statics/Dynamics), Continuum Mechanics (Mechanics of Materials), Thermodynamics, and Electrical Circuits and Electronics. The junior-level Machine Design course at the University of North Dakota includes regular course lectures along with two hands-on activities. One requires students to perform materials testing on a simple component (typically an Allen wrench) and the other is a designbuild project. The materials testing experience is done outside of class and students typically make use of either a superficial hardness tester or a universal testing machine. The focus on the experience is primarily on students getting used to using the testing equipment in preparation for their larger design/build project.

Case Studies One definition of a case study is a complex example that gives insight into the context of a problem while illustrating the main point(s) (Fry et al. 1999). Davis and Wilcock (2003; 2005) expand on this definition as, ‘student centered activities based on topics that demonstrate theoretical concepts in an applied setting.’ They have put together an excellent ‘howto’ guide for developing materials science-focused case studies (Davis and Wilcock 2003). Insights from their own experiences in developing, implementing, and assessing case studies in their teaching are included, as well as potential pitfalls to be avoided. Several sample case studies are

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provided including one based on the properties of chocolate, the explosion of the Space Shuttle Challenger, metallic bicycle components, windsurfing masts, and joining processes. Each sample case study includes sufficient information for instructors to determine whether it is appropriate for their course and the time/resources required to implement it. For example, the ‘Windsurfing Masts’ case study has stated goals of helping students apply theoretical concepts to sporting equipment and encouraging students to carry out independent research; it is expected to take a period of about three weeks and be appropriate for students with an introductory materialsscience knowledge. Note that the case study does not have to be implemented within a materials science course – case studies, like lab activities, can be selectively integrated into other mechanical engineering courses to tie together concepts. Multiple online databases of materialsrelated case studies developed for application in engineering education exist (e.g., University of Birmingham 2019, ANSYS Granta 2019).

Muddiest Points At the end of any class, it is inevitable that at least some of the topics discussed will still be somewhat unclear in the minds of some of the students. Asking students to reflect on their learning and identify the concepts that are still ‘muddy’ to them serves to both engage metacognitive processes on the part of the students and to help the instructor target interventions to ‘unmuddy’ the concepts. This process was first described in the literature by Mosteller (1988), who began by asking students in his statistics course to identify key points after each class period and then expanded his request to include concepts that were unclear or muddy. More recently, the muddiest point approach has been widely adopted in various engineering courses.

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Carberry et al. (2013) describe the implementation of muddiest points into a general materials science course. Based on the muddiest point responses, the authors generated YouTube videos, online tutorials, and an FAQ website resource for the course, in addition to addressing some of the muddiest points during subsequent class meetings. The researchers found that use of muddiest points and formative feedback opened a different mode of communication with students than had existed previously. As one instructor put it (Carberry et al. 2013), Initially, when I started to use ‘muddiest points,’ the goal was to elicit confusing concepts from students in order to respond to them; however, over six semesters, the ‘muddiest point’ strategy has evolved into much more than responding to student issues. It opened a channel of communication and mutual trust between the students and me, which turned an instructor’s monologue into a student-instructor dialogue.

Krause et al. (2013) explored additional feedback methods to clarify muddiest points (class warm-ups and revised notes posted electronically) as well as ways to help instructors visualize overall topical understanding like word clouds. Results showed that student exam performance significantly improved on topics for which formative assessment based on muddiest point submission was provided. Cavalli (2018) and Stuart (2015) separately implemented the muddiest point approach into courses on composite materials. Cavalli required regular muddiest point submission as part of the course grade. Students reported neutral to negative impressions on this implementation of muddiest points with the primary objection being the submission requirement because they felt that there were times when they had nothing to clarify. This is despite the fact pre- and post-tests clearly indicated that some concepts were unclear for a significant portion of students. Submission of muddiest points was not required by Stuart (2015); over 70% of students reported a positive impression of muddiest points in aiding their success.

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Flipped Classes In a flipped classroom, material that is traditionally presented in a lecture format during class is prepackaged to be accessed by students prior to coming to class. Typically, students are required to complete a pre-class assessment (“learning gate”) to verify they have completed the appropriate preparation. These assessments can also be used by the instructor to identify topics that are problematic for multiple students. Class time is then spent in discussions or group problem solving to apply the material. Implementation of the learning gates is critical to the success of the flipped course method. In order to actively participate in the discussion or group problem-solving session, students need to bring a baseline level of knowledge with them to class. Cavalli et al. (2015) set a minimum performance threshold of 80% on learning gates for one group of students and no performance threshold for another group (but still a requirement that the learning gates be completed). Both groups placed similar values on the class discussions with regards to their learning and both groups performed similarly on pre- and post-tests related to course concepts. Figure 3 shows a representation of Bloom’s Taxonomy for the Cognitive Domain (Bloom et al. 1956, Anderson and Krathwohl 2001). In theory, the flipped course structure allows students to move from comprehension/remember (where most lecture sessions remain) to application and, potentially, analysis. Cavalli et al. (2014) found that the flipped course structure in an introductory materials science course produced better student outcomes than the traditional course format. However, when both a flipped and traditional section of the same course were available in the same semester, students in the flipped section reported dissatisfaction at the extra time required for them to prepare for each class meeting compared to peers in the non-flipped section. This was at least partially offset by the flipped class meeting only twice per week for most of the semester compared to three times for the traditional class structure in order to cover the same amount of material.

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Figure 3. Bloom’s Taxonomy for the Cognitive Domain (Bloom et al. 1956, Anderson and Krathwohl 2001).

Flipping an entire class at once requires a significant upfront investment of time on the part of the instructor. A more manageable approach is to flip several weeks of a course at a time, choosing concepts that logically comprise one or more modules. Students will need to be carefully guided in the transition between the flipped and non-flipped segments, but instructors may find feedback on their initial efforts at flipping valuable before committing to an entire flipped course.

Jigsaw Aronson and coworkers (1978) describe the motivation for the Jigsaw method as turning a competitive classroom environment in which there is one major resource (the instructor) whose time each student competes to claim into a collaborative classroom in which students view each other as resources. The authors achieved this by turning each student into an expert on a segment of a larger body of knowledge. At the end of the activity,

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each student is responsible for a complete understanding of the issue being discussed or problem being solved. This requires that all students in the group work together to share their expertise with their compatriots – it is in this enforced interdependence amongst group members that the power of the Jigsaw method lies. Various researchers have applied the Jigsaw method to engineering education. Rynearson and Polasik (2019) used regular Jigsaw activities to help student learn about materials processing methods. Each small group of students (four in their case) would learn about a different method and discuss potential exam questions related to the method. The original groups would then reorganize so that each member of the new groups had knowledge of a different manufacturing process. Students were then responsible to share their knowledge of their assigned process as well as potential exam questions (and answers). Jordan (2018) implemented a Jigsaw activity around the concept of material selection for baseball bats in a materials science course. Expert groups were created around the properties, performance, and sustainability considerations of potential materials for bats (wood, metal, composite). Groups were then reconstituted with an expert on each material present and had to make a recommendation for a final material selection. Gomez et al. (2017) implemented the Jigsaw technique into a sophomore-level chemical engineering class about midway through the semester. The Jigsaw was implemented as part of a semester-long design project. The class had been divided into three large groups (~20 students), each of which had been completing separate project deliverables throughout the first half of the term. In the Jigsaw, representatives from each deliverable group formed new groups and had to update the other members on their original group’s key findings and how they influenced the progress of the overall design project. Students reported generally positive impressions of the Jigsaw experience, citing the fact that the Jigsaw helped them see the overall project from a different perspective than the one on which they were most focused. Pow-Sang and Escobar-Caceres (2017) provide a survey of Jigsaw implementations across engineering and computer science courses, highlighting unique implementations like the use of Jigsaws in laboratory

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settings or Jigsaws based on prior knowledge rather than material taught in class.

Think-Pair-Share Lyman (1981) explained his motivation for supporting the ListenThink-Pair-Share activity, at least in part, as the fact that, ‘all students require a responsive classroom, a cooperative design in which response is asked of everyone.’ In the activity, students are expected to first listen to a question posed by an instructor. They then think about their answer and discuss it with a neighbor. Finally, the pair is expected to provide a response to the class. Lyman reported that significantly more students were willing to raise their hands and volunteer and answer after having had a chance to ‘rehearse’ their response with a partner. The more well-known implementation of Think-Pair-Share is probably that of Mazur (1997), who applied the approach in large classes of university physics students. Kaddoura (2013) documented a statistically significant increase in critical thinking skills in undergraduate nursing students who used Think-PairShare compare to a control class that did not. Hufnagel and Reese (2013) applied Think-Pair-Share in an introductory materials science course and compared student knowledge gains between a section taught with the active learning method and a traditional section. Students who used ThinkPair-Share reported lower perceived self-efficacy than students who did not; but their actual knowledge (as demonstrated by end-of-semester concept inventory scores) were significantly higher. The reasons for this seeming contradiction were not immediately apparent. Yalisove and Daly (2014) eliminated 1/3 of the lectures in a large (170 student) materials science course. One day per week, students met in the same large lecture hall but grouped according to their recitation section of 25-30 students. The smaller group then participated in active learning activities like Think-PairShare. Comparison of student performance on exam questions between the active learning section and a similar section taught with three days of

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lecture showed significant gains (including through the final course exam) for the active learning students.

Problem-Based Learning Mechanical engineering is increasingly multidisciplinary, problemcentric, and team-based (Prince 2004, Andersen et al. 2019). Technologies in areas like materials, manufacturing, and design are constantly changing, requiring that students be trained to become lifelong learners. Unlike traditional lectures where material is presented to students individually in a sequential, structured manner, the problem-based learning (PBL) approach requires students to solve ill-posed, complex real-life problems in teams. Dochy et al. (2003) define problem-based learning as student-centered, occurring in small groups under the guidance of a facilitator, centered around authentic problems that are encountered as part of the learning, not after the learning has taken place, and requiring new information to be acquired via self-directed learning. The struggle towards the solution identifies knowledge gaps that the students fill via outside research and discussion with their colleagues. Multiple paths typically exist to the problem ‘solution.’ As a result, not every student group will have the same experience or gain the same knowledge even if all groups were trying to achieve the same objective. Appropriate problem definition is, not surprisingly, critical to the successful implementation of PBL (Stanford, 2001). The ill-posed problems can be pulled from contemporary discussions in the profession or constructed from scratch. Key attributes of the problems include: they require more information to understand/solve than is initially provided, they contain multiple solution paths, they change as new information is acquired, they prevent students from identifying a “correct” decision, they generate interest and controversy and motivate learners to ask questions, they are open-ended enough to require collaboration and thinking within the group, and they contain content that is authentic to the discipline. A meta-analysis of 43 PBL-based studies showed that all studies reported a

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moderate positive effect of PBL on student skills, and a small, potentially negative effect on overall knowledge. However, students exposed to PBL tend to remember more of the acquired knowledge compared to students who did not learn via PBL (Dochy et al. 2003). Jonassen and Khanna (2011) implemented PBL as part of a materials science course within a mechanical engineering curriculum. Students were broken into groups of about six. Seven different PBL activities were used over the course of the semester, covering topics including design of cassette plates for x-ray machines, silicon wafer orientation, variation of single crystal strength, failing aluminum brackets, improved automotive springs, and die forgings for connecting rods. The instructors’ overall impression of the implementation was not positive. Students reported confusion about the course structure and perceived themselves to be “jumping around on different subjects.” Similarly, the instructors perceived that students were not using the available group time productively, resorting to working separately and then combining individual pieces for submission rather than working collaboratively throughout. It is clear from this experience that instructor oversight of group discussions as well as formative feedback of the PBL process are important to the successful implementation of this approach.

Guided Inquiry Douglas (2008) promotes an active learning approach for materials education termed Process Oriented Guided Inquiry Learning (POGIL). During each class period, students divide into small groups (about four students). Each group is given a worksheet related to the topics being studied. The worksheets include background data and information, critical thinking questions related to the background information, and application exercises which ask students to apply their new knowledge about the day’s topic. In a follow-up study, Douglas (2011) presented qualitative evidence from students that they recognized the value of the additional effort required to work through a typical POGIL worksheet compared to

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receiving the same information in a lecture format. Direct assessment of student learning gains versus non-POGIL materials science courses were not presented, but the anecdotal evidence seems to support learning gains using this method. Additional information regarding Process Oriented Guided Inquiry Learning is available from the POGIL Project, https://pogil.org/.

Computational Materials: Selection, Modeling, Simulation, and Visualization Computational tools are an essential part of contemporary mechanical engineering education. Some tools, like CES Edupack (ANSYS Granta 2019), are based on databases of properties of existing materials and can aid designers in selecting materials to meet design constraints. Other tools help students visualize material phenomena that occur at scales too small (or too fast) to see. For example, Mansoor et al. (2018) describe the implementation of an augmented reality app (Holo-MSE) for visualizing Miller indices and crystal structures. Still another class of tools implements fundamental physical laws (with various degrees of sophistication) to allow students to model the behavior of current materials or even simulate the behavior of potential new material formulations. The nanoHUB website is an excellent resource for materials visualization, simulation, and modeling software that can be used for both research and education; the tools can be implemented in individual class modules or used as the basis for entire courses in materials behavior (nanoHUB 2019). Douglas et al. (2015) stress that the effective implementation of computational research tools into the undergraduate education environment requires careful planning and structure by the instructor. Special care must be taken to ensure that the educational objectives are not lost in the myriad details required to implement the computational tool. With proper planning and oversight, Coughlan et al. (2016) show how advanced computational materials tools can be effectively integrated into undergraduate courses as early as the second year.

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The development of modern materials integrates extensive numerical simulations with targeted physical testing. Most mechanical engineers will not be directly involved with the computational modeling of new materials. They may not need to know the exact details of the thermodynamics relationships that govern materials behavior. But they need to have an accurate conceptual understanding of, for example, how crystals tend to change during heat treatment. Blikstein and Wilensky (2009) present what they term an ‘agent-based’ method of teaching materials science via modeling. Rather than focusing on the details of the myriad equations which exists to describe grain growth, the authors implemented a simple energybased model which captures the general behavior of grains but also allows for a degree of randomness. Pre- and post-modeling interviews with students indicated that the use of the models corrected a number of theoretical misconceptions and increased student understanding of the associated phenomena. Such an approach lowers the knowledge threshold for non-materials scientists to gain appropriate insights into materials behavior.

MATERIALS-FOCUSED ACTIVE LEARNING IN OTHER ME COURSES As described in the section on laboratory activities, materials education need not be restricted to a materials science course (or courses) within the mechanical engineering curriculum. Practicing engineers know that a successful design is inextricably linked with the proper selection of both materials and manufacturing processes to meet the design requirements. There is no reason that concepts like stress calculation, design, and materials science cannot be taught in courses like Mechanics of Materials, Machine Design, and Senior (Capstone) Design, among others. For example, Linsey et al. (2009) describe the development and implementation of 28 active learning activities that can be integrated into Mechanics of Materials or Machine

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Design to teach accompanying concepts of materials science. One example is a comparison of the fracture behavior of a Tootsie Roll vs. chalk. Students are first asked to hypothesize about how the two materials will fail under two types of loading (bending or torsion). They then conduct an experiment (failing the Tootsie Roll and the chalk) and observe the results. Failure theories from solid mechanics are discussed at the appropriate level for the overall course. Students then reflect on why the two materials failed differently. At the completion of the activity, students have navigated through Kolb’s learning cycle (Figure 2). A full list of the materials-related active learning products (ALPs) along with teaching guides and notes can be found at the Active Learning Products for Engineering Education website (ALPs 2019). Pai et al. (2002) describe how a course that was initially focused primarily on the design of aluminum products evolved to incorporate additional focus on materials science and materials selection based on feedback from industry. In particular, the authors note the importance for students to understand structure-property-process-performance interactions, as shown in the traditional materials tetrahedron (Figure 4). A semester-long design project is proposed by an industry sponsor. This is in addition to the ‘regular course material’ related to material science theory, manufacturing processes, economic considerations, design principles, and case studies. To complete their semester design project, students are expected to implement not only stress analyses and design concepts, but to also consider issues related to materials selection and manufacturing. This balanced approach to design education is reported to be popular amongst both students and the industrial project sponsors (Pai et al. 2002). Other educators (e.g., Nitterright and Michael 2012) have also detailed increased student performance on design projects that focus not only on shape optimization (i.e., assume a material and then optimize geometry) but on complete design optimization (i.e., modify material and geometry options in concert to satisfy design constraints).

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Figure 4. Materials tetrahedron showing the link between material structure, processing, properties, and performance.

INNOVATION IN MATERIALS SCIENCE EDUCATION Online and Hybrid Learning According to latest data from the American Association of Colleges and Universities (AAC&U), traditional students (aged 18-24 years) comprise 40.9% of the student population (AAC&U 2019). Non-traditional students (age 24+ years) are now a significant majority of the college population. These students have additional responsibilities like family and work which compete with their educational requirements for time and attention (NCES 2019). Recently, hybrid (a mix of in-person and online experiences) and online courses have been identified as having the potential to serve the educational needs of students who are restricted from full-time, on-campus enrollment by other commitments. Cavalli and co-workers (2014, 2014a, 2015, 2017) have presented various results comparing active learning outcomes between on-campus and distance students in hybrid materials courses (ME 301: Materials Science and ME 420: Composite Materials). Active learning methods that are effective for on-campus students like small group discussions and problem solving, tend not to be as highly valued by distance students in an

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asynchronous learning environment. One option is to implement synchronous distance learning, but the time constraint can prevent distance students with work or other commitments from being able to enroll in the class. Cavalli found that distance students tended to outperform on-campus students in the more qualitative Materials Science course. The reverse was true in the more quantitative Composite Materials course that involved significant matrix math and computational analyses (Cavalli 2017). While the sample size is small, this is consistent with similar findings by Goodson et al. (2009). Designing laboratory-based problems and active learning activities for online students is a major hurdle. Both Augmented Realty (AR) and Virtual Reality (VR) have played a pivotal role in enhancing the learning outcomes of online students in laboratory-based problems (Fiorentino et al. 2009, Vergara et al. 2017). For example, by using VR the interaction between the VR platform and user can be tailored, and can be categorized into, (a) passive, (b) exploratory, and (c) interactive levels (Vergara et al. 2017). In addition, different experimental kits can be designed by the instructor, or ready-made kits can be purchased from the market (ACS 2019). Students can also be asked to perform materials-related experiments with household items. For example, in Composite Materials, we have asked students to design, fabricate, and characterize ice matrix composites by reinforcing ice matrix with natural fibers. Similar experimentation could be performed with clay, plaster of Paris, or commercially available fiberglass automotive repair kits.

Materials Education and Entrepreneurship Engineering training around entrepreneurship is a critical component of preparing students working at the cutting edge of new technologies. Entrepreneurship is an important tool for job creation but does not necessarily mean creation of a new company or entity. Venkataraman (1997) has defined entrepreneurship, which, “as a scholarly field, seeks to understand how opportunities to bring into existence ‘future’ goods and

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services are discovered, created, and exploited, by whom, and with what consequences.” Lumpkin and Dess (1996) stated, “the essential act of entrepreneurship is new entry.” New entry is the most critical term as it involves (a) the formation of start-up companies, (b) “internal corporate venturing,” or (c) leveraging the existing firm (Lumpkin and Dess 1996). Lumpkin and Dess (1996) further delineated the important facets of entrepreneurial process as autonomy, innovativeness, risk-taking, proactiveness, and competitive aggressiveness. Materials education can be explicitly linked to entrepreneurship in a variety of ways. Development of new materials and new applications for existing materials is innovation. Innovation is not enough to ensure that new technologies make an impact or are adopted by industry. By supplementing the technical components of materials education and mechanical design with short courses and workshops related to intellectual property (IP), writing a successful business plan, and presenting a successful investor pitch, students are prepared to speak the language of business and navigate the gauntlet of turning a great idea into a successful product. Bonnet et al. (2006) provide an excellent overview of how entrepreneurial education has been implemented as the backbone of multiple engineering and science degree programs at the Delft University of Technology.

Sustainable Materials and Circular Economy The concept of Circular Economy can be linked to the traditional sustainability mantra ‘reduce, reuse, and recycle.’ According to the Ellen MacArthur Foundation (2019), Circular Economy is based on three principles:   

Design out waste and pollution Keep products and materials in use Regenerate natural systems

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Geissdoerfer et al. (2017) highlight key similarities and differences between sustainability and Circular Economy, as typically used in the literature. Circular Economy is focused on viewing the entire economic cycle as a closed loop, with the ideal being a system requiring no new inputs into the system and having no leakage out of the system. The goals of sustainability, in contrast, tend to be more open-ended and depend strongly on the person/organization making them. Both Circular Economy and sustainability can support environmental, social, and economic goals, but Circular Economy (as the name suggests) prioritizes economy as opposed to the ‘triple bottom line’ of environment/society/economy for sustainability (Geissdoerfer et al. 2017). As described by multiple authors (e.g., Kuznetsov and Edwards 2010; Kopnina 2018), case studies, in particular, can be an effective method for teaching about both sustainability and Circular Economy. For example, Kopnina presents a case study of resuable water bottles. Comparisons are made related to environmental impacts, economics costs and benefits, and social factors around the choice of using single-use water bottles vs reusable containers. Differences between the stated goals of reusable bottle manufacturers and issues like pollution related to distribution and manufacturing can be explored. A full analysis shows that no existing reusable water bottles on the market truly achieve Circular Economy. This leaves potential room for students to propose their own innovations based on the information they have uncovered as part of the case study. In contrast, Kuznetsov and Edwards (2010) explore ‘sustainable’ energy generation (e.g., solar, hydrogen, thermoelectric) via the use of non-renewable source materials. The case studies can incorporate issues related to the reclamation of the non-renewable materials at the end of their design lifetimes or can focus more heavily on the initial costs of the installation and the ‘sustainable’ power produced.

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CONCLUSION Successful mechanical engineering designs rely on effective choices of materials and processing methods. As symbolized in the materials tetrahedron, material structure, properties, processing, and performance are interdependent. Mechanical engineers must develop insights into materials behavior to satisfy design constraints. In this chapter, we have discussed effective methods for teaching that also happen to be applicable to concepts of materials behavior. We have also explained how those concepts can be integrated throughout the mechanical engineering curriculum beyond the typical materials science course. Examples of active learning techniques for teaching materials topics at a variety of levels have been provided. We have shown that materials education can be a perfect vehicle for introducing contemporary topics like entrepreneurship and sustainability into the mechanical engineering curriculum. We have also shown how ‘hands-on’ experiences with materials can be implemented into online or hybrid courses to serve students who might otherwise not have access to a mechanical engineering education.

REFERENCES AAC&U. (2019). Facts & Figures: College Students Are More Diverse Than Ever. Faculty and Administrators are Not. AAC&U News, March. ACS – American Ceramics Society. (2019). Materials Science Classroom Kits, https://ceramics.org/professional-resources/teachers/materialsscience-classroom-kits, accessed November 3, 2019. ALPs – Active Learning Products for Engineering Education (2019). http://www.me.utexas.edu/~alps/, accessed November 3, 2019. Al-Haik, M., C. Luhrs, et al. (2010). Introducing Nanotechnology to Mechanical and Civil Engineering Students through Materials Science Courses. Journal of Nano Education, 2(1-2), 13-26. Andersen, A. L., T. D. Brunoe, and K. Nielsen. (2019). Engineering Education in Changeable and Reconfigurable Manufacturing: Using

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Problem-Based Learning in a Learning Factory Environment. Procedia CIRP, 81, 7–12. Anderson, L. W., and D. R. Krathwohl, eds. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Allyn and Bacon. ISBN 978-0-8013-1903-7. ANSYS Granta. (2019). https://grantadesign.com/education/ces-edupack/, accessed November 3, 2019. Aronson, E., N. Blaney, et al. (1978). The jigsaw classroom. Beverly Hills, CA: Sage. Beasley, M. A. and M. J. Fischer. (2012). Why they leave: the impact of stereotype threat on the attrition of women and minorities from science, math, and engineering majors. Social Psychology in Education, 15, 427-448. Blikstein, P. and U. Wilensky. (2009). An Atom is Known by the Company it Keeps: A Constructionist Learning Environment for Materials Science Using Agent-Based Modeling. International Journal of Computational Mathematical Learning, 14, 81-119. Bloom B. S., M. D. Engelhart, et al. (1956). Taxonomy of educational objectives: Handbook I: Cognitive Domain. New York (NY): David McKay. Bonnet, H., J. Quist, et al. (2006). Teaching sustainable entrepreneurship to engineering students: the case of Delft University of Technology. European Journal of Engineering Education, 31(2), 155-167. Carberry, A., S. Krause, et al. (2013). “Unmuddying” Course Content Using Muddiest Point Reflections. IEEE Frontiers in Education Conference, Oklahoma City, OK, IEEE. Carey, B. (2014). How We Learn. New York (NY):Random House. Cavalli, M. and G. Bibel. (2005). Virtual Steel Lab. Computers in Education, XV, 108-112. Cavalli, M. N., J. J. Neubert, et al. (2014). Comparison of Student Performance and Perceptions Across Multiple Course Delivery Modes. ASEE Annual Conference and Exposition, Indianapolis, IN, ASEE. Cavalli, M. N., J. J. Neubert, and D. Worley. (2014a). Comparison of OnCampus and Distance Learning Preferences in a Junior-level Materials

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Science Course. ASEE Annual Conference and Exposition, Indianapolis, IN, ASEE. Cavalli, M. N. (2015). Comparison of Learning Gate Completion Requirements in a Flipped Classroom. ASEE Annual Conference and Exposition, Seattle, WA, ASEE. Cavalli, M. N. (2017). Comparison of On-campus and Distance Learning Outcomes in a Composite Materials Course. ASEE Annual Conference and Exposition, Columbus, OH, ASEE. Cavalli, M. N. (2018). Comparing Muddiest Points and Learning Outcomes for Campus and Distance Students in a Composite Materials Course. ASEE Annual Conference and Exposition, Salt Lake City, UT, ASEE. Chickering, A. W. and Z. F. Gamson (1987). “Seven Principles for Good Practice in Undergraduate Education.” AAHE Bulletin, March, 3-7. Chi, M. T. H. and R. Wylie. (2014). The ICAP Framework: Linking Cognitive Engagement to Active Learning Outcomes. Educational Psychologist, 49(4), 219-243. doi: 10.1080/00461520.2014.965823. Christie, M. and E. de Graaff. (2017). The philosophical underpinnings of Active Learning in Engineering Education. European Journal of Engineering Education, 42(1), 5-16. Coughlan, A., T. A. Faltens, et al. (2016). Integrating a Research-Grade Simulation Tool in a Second-Year Materials Science Laboratory Course. ASEE Annual Conference and Exposition, New Orleans, LA, ASEE. Davis, C. and E. Wilcock (2003). Teaching Materials Using Case Studies. Liverpool, UK, UK Centre for Materials Education: 20. Davis, C. and E. Wilcock. (2005). Developing, implementing and evaluating case students in materials science. European Journal of Engineering Education, 30(1), 59-69. Dochy, F., M. Segers, et al. (2003). Effects of problem-based learning: a meta-analysis. Learning and Instruction, 13, 533-568. Douglas, E. (2008). Guided Inquiry Lessons for Introduction to Materials. ASEE Annual Conference and Exposition, Pittsburgh, PA, ASEE.

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Douglas, E. (2011). Student Construction of Knowledge in an Active Learning Classroom. ASEE Annual Conference and Exposition, Vancouver, BC, ASEE. Douglas, K. A., T. Faltens, et al. (2015). A Framework for Integrating Computational Simulations into Engineering Lessons. ASEE Annual Conference and Exposition, Seattle, WA, ASEE. Doyle, T. and T. Zakrajsek. (2013). The New Science of Learning. Sterling (VA): Stylus Publishing. Ellen MacArthur Foundation. (2019). https://www.ellenmacarthur foundation.org/circular-economy/concept, accessed November 3, 2019. Fiorentino, M., G. Monno, and A. Uva. (2009). Interactive “touch and see” FEM Simulation using Augmented Reality, International Journal of Engineering Education, 25-26, 1124–1128. Freeman, S., S. L. Eddy, et al. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Science, 111(23): 8410-8415. Fry, H., S. Ketteridge, and S. Marshall. (1999). A Handbook for Teaching and Learning in Higher Education. Glasgow, Scotland: Kogan Page. Geissdoerfer, M., P. Savaget, et al. (2017). The Circular Economy – A new sustainability paradigm? Journal of Cleaner Production, 143, 757-768. Gomez, J., V. Svihla, and A. K. Datye. (2017). Jigsaws and Parleys: Strategies for engaging sophomore level students as a learning community. ASEE Annual Conference and Exposition, Columbus, OH, ASEE. Goodson, C., S. Miertschin, et al. (2009). On-line Distance Education and Student Learning: Do They Measure Up? ASEE Annual Conference and Exposition, Austin, TX, ASEE. Hufnagel, T. C. and M. J. Reese, Jr. (2013). Deepening Conceptual Understanding in an Introductory Material Science Course through Active Learning Strategies. ASEE Annual Conference and Exposition, Atlanta, GA, ASEE.

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Johnson, D. W. and R. T. Johnson. (2009). An Educational Psychology Success Story: Social Interdependence Theory and Cooperative Learning. Educational Researcher, 38(5), 365–379. Jonassen, D. H. and S. K. Khanna. (2011). Implementing Problem Based Learning in Materials Science. ASEE Annual Conference and Exposition, Vancouver, Canada, ASEE. Jordan, W. M. (2018). Incorporating Active Learning and Sustainable Engineering Concepts into a Required Materials Class. ASEE Annual Conference and Exposition, Salt Lake City, UT, ASEE. Kaddoura, M. (2013). Think Pair Share: A Teaching Learning Strategy to Enhance Students’ Critical Thinking. Educational Research Quarterly, 36(4), 3-24. Keogh, M., M. S. Zarske, et al. (2018). Active Learning Group Work: Helpful or Harmful for Women in Engineering? ASEE Annual Conference and Exposition, Salt Lake City, UT, ASEE. Kolb, D. A. (1984). Experiential Learning: Experience as the Source of Learning and Development. Englewood Cliffs (NJ): Prentice Hall. Kopnina, H. (2018). Circular economy and Cradle to Cradle in educational practice. Journal of Integrative Environmental Sciences, 15(1), 119134. Krause, S., D. R. Baker, et al. (2013). Muddiest Point Formative Feedback in Core Materials Classes with YouTube, Blackboard, Class Warm-ups and Word Clouds. ASEE Annual Conference and Exposition, Atlanta, GA, ASEE. Kuh, G. D. (2008). High-Impact Practices: What They Are, Who Has Access to Them, and Why They Matter. Washington, DC, Association of American Colleges and Universities: 48. Kuznetsov, V. L. and P. P. Edwards. (2010). Functional Materials for Sustainable Energy Technologies: Four Case Studies. ChemSusChem, 3(1), 44-58. Linsey, J., A. Talley, et al. (2009). From Tootsie Rolls to Broken Bones: An Innovative Approach for Active Learning in Mechanics of Materials. Advances in Engineering Education, 1(3), 1-23.

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Litzinger, T. A. (2011). Engineering Education and the Development of Expertise. Journal of Engineering Education, 100(1), 123-150. Lumpkin, G. T. and G. G. Dess. (1996). Clarifying the entrepreneurial orientation construct and linking it to performance. Academy of Management Review, 21, 135–172. Lyman, F. T. (1981). The Responsive Classroom Discussion: The Inclusion of All Students. Mainstreaming Digest, 109-113. Mansoor, B., M. J. Makki, et al. (2018). Use of Mixed Reality Tools in Introductory Materials Science Courses. ASEE Annual Conference and Exposition, Salt Lake City, UT, ASEE. Maxwell-Stuart, R., B. Taheri, et al. (2018). Working together to increase student satisfaction: exploring the effects of mode of study and fee status. Studies in Higher Education. 43(8), 1392-1404, doi: 10.1080/ 03075079.2016.1257601. Mazur, E. (1997). Peer Instruction: A User’s Manual. Prentice Hall. Meyers C. and T. B. Jones. (1993). Promoting Active Learning: Strategies for the College Classroom. Jossey-Bass Publishers, San Francisco, CA. Miller, D., D. Lagoudas, et al. (1998). In Class Thermal Conductivity Experiment for Sophomore Materials Science and Continuum Mechanics Courses. ASEE Annual Conference and Exposition, Seattle, WA, ASEE. Mosteller, F. (1988). Broadening the Scope of Statistics and Statistical Education. The American Statistician, 42(2), 93-99. nanoHUB, https://nanohub.org/, accessed November 3, 2019. NCES - National Center for Education Statistics. (2019). Nontraditional Undergraduates: Definitions and Data. https://nces.ed.gov/pubs/web/ 97578e.asp, accessed November 3, 2019. Nitterright, F. A. and R. Michael. (2012). Design Optimization Problem in a Materials Engineering Course. ASEE Annual Conference and Exposition, San Antonio, TX, ASEE. Oakley, B. (2014). A Mind for Numbers. New York, NY, Tarcher Perigee.

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Pai, D. M., G. J. Filatovs, and J. Sankar (2002). Integration of Materials Science into an Industrially-Sponsored Engineering Design Course. ASEE Annual Conference and Exposition, Montreal, Quebec, ASEE. Pow-Sang, J. A., and P. Escobar-Caceres. (2017). A Systematic Literature Review of the Application of the Jigsaw Technique in Engineering and Computing. Interactive Collaborative Learning: Advances in Intelligent Systems and Computing, 544, 322-329. Prince, M. (2004). Does Active Learning Work? A Review of the Research. Journal of Engineering Education, 93(3), 223-231. Rosetti, M. D. and H. B. Nembhard. (1998). Using Cooperative Learning to Activate Your Simulation Classroom. Proceedings of the 1998 Winter Simulation Conference, Washington, D.C., IEEE. Rynearson, A. M. and A. L. Polasik. (2019). Interactive and Collaborative Materials Science and Processing Course with Integrated Lab. ASEE Annual Conference and Exposition, Tampa, FL, ASEE. Sheppard, S., K. Macatangay, et al. (2009). Educating Engineers: Designing for the Future of the Field. San Francisco, CA: Jossey-Bass. Smith, K. A. (1994). Cooperation in the College Classroom. Seminar Notes presented at Teaching Resource Center. University of Virginia. Stanford Center for Teaching and Learning (2001). Problem-Based Learning, Speaking of Teaching, 11(1), 1-7. Stuart, W. J. (2015). Enhanced Teaching Techniques Applied to an Upper Division Composite Materials Engineering Course with an Emphasis on Aerospace Applications. ASEE Annual Conference and Exposition, Seattle, WA, ASEE. University of Birmingham - Metallurgy and Materials Case Studies Teaching and Learning Resources (2019). https://www.birmingham.ac.uk/schools/metallurgymaterials/about/cases/group-work/index.aspx, accessed March 18, 2020. Venkataraman, S. (1997). The distinctive domain of entrepreneurship research. Advances in Entrepreneurship, Firm Emergence, and Growth, 3, 119-138.

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Vergara, A., M. P. Rubio, and M. Lorenzo. (2017). On the Design of Virtual Reality Learning Environments in Engineering. Multimodal Technologies and Interactions, 1(2), 11; https://doi.org/10.3390/ mti1020011. Yalisove, S. M. and S. R. Daly (2014). Eliminating Lectures (and video lectures) in Large Introductory Materials Science and Engineering Courses: Large Gains in Student Learning. ASEE Annual Conference and Exposition, Indianapolis, IN, ASEE. Zemke, S. C., D. F. Elger, and J. Beller. (2004). Tailoring Cooperative Learning Events for Engineering Classes. ASEE Annual Conference and Exposition, Salt Lake City, UT, ASEE.

In: Mechanical Engineering Education … ISBN: 978-1-53617-791-6 Editor: Charles E. Baukal, Jr. © 2020 Nova Science Publishers, Inc.

Chapter 12

REVERSE ENGINEERING: A PHILOSOPHY OF EXPLORATORY PROBLEM SOLVING Dominic Halsmer, P. Wesley Odom, Jessica Fitzgerald and Taylor Tryon Oral Roberts University,Tulsa, OK, US

INTRODUCTION TO REVERSE ENGINEERING The practice of engineering has been characterized in a multitude of ways, as seen in the fascinating compilation of twenty-one definitions of engineering by Landis, Peuker and Mott (2018, pp. 309-310). But if engineering generally involves the design of a system to solve a problem or serve a purpose, then reverse engineering is simply the exploration of an already existing engineered system to better understand the thinking that went into its design. Landis, Peuker and Mott (p. 43) define reverse engineering as “the process of taking apart a device, object or system to see how it works in order to duplicate or enhance it.” Reverse Engineering projects may be undertaken for a variety of reasons, from realizing the benefits that accrue from a deeper knowledge of a competitor’s product, to the satisfaction of your own curiosity about how something works.

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Everyone engages in reverse engineering activities to some degree. Whenever we are confronted with technology that is new to us, we explore this technology, even if only to figure out how to operate it. In fact, even from before the time of our birth, we explore how our bodies interact with our surroundings. Much to our mother’s consternation, we explore the flexibility of the womb wall by kicking at it with all of our tiny little strength. This exploration continues at a much more rapid pace after we are born. Most toddlers have solved the inverted pendulum problem 1 (experientially, at least) before their first birthday, and as a result are able to walk on their own. When understood in this general sense, reverse engineering is indeed a widespread and popular activity. But unfortunately, not much research has been conducted on the history, philosophy, and resulting methodology of reverse engineering. This chapter represents some initial thoughts in this direction.2 First of all, the success of any reverse engineering project is contingent upon a fundamental compatibility between the complexity of the object, and the technical and intellectual capabilities of the reverse engineer (or investigator). Although a small child may have mastered an experiential form of the inverted pendulum problem, it is highly unlikely that they could correctly diagnose the problem with a malfunctioning household appliance, while an adult with the proper training could easily handle this. Consider another example from the animal kingdom. Although an adult beaver would very likely be able to reverse engineer a dam of logs and brush constructed by one of its furry colleagues across a small creek, this same adult beaver would not be able to make heads or tails of any technological devices engineered by human beings. Not only would this task be beyond the beaver’s common experience, but totally beyond its mental capacities. Similarly, humans with training in engineering are often able to successfully reverse engineer technological devices that have been engineered by other human beings. This is to be expected. But, curiously 1

In a standing position, or any upright activity (i.e., walking), the human body is basically an inverted pendulum, which is inherently unstable in the presence of a gravitational field. 2 Some of the content for this chapter has been adapted from: Halsmer et al., 2013.

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enough, human beings have also had great success throughout history, and especially recently, in reverse engineering aspects and objects of the natural realm. That is, things arising through natural processes that are not at all engineered by other human beings. Now this has been conducted to such a great extent that it is often taken for granted, but when you think about it more deeply, it is a somewhat surprising state of affairs. It is understandable that we can reverse engineer human artifacts, but how is it that we are able to achieve such grand success in reverse engineering natural systems? The exploding field of biomimetics3 is powerful evidence that such is the case. Why do we enjoy this incredible match between the complexity of natural systems and the technological and mental capabilities of humans to unravel the secrets behind such systems? Indeed, we regularly marvel at the amazing ingenuity and efficiency of systems found in nature. The answer to this question, to a very large degree, lies in the existence and comprehensibility of the language of mathematics, and its unreasonable effectiveness in accurately describing the natural world. This strange utility of mathematics prompted Nobel Laureate Eugene Wigner (1960) to write, in a now famous paper, “The enormous usefulness of mathematics is something bordering on the mysterious…There is no rational explanation for it…The miracle of the appropriateness of the language of mathematics for the formulation of the laws of physics is a wonderful gift which we neither understand nor deserve.” We have no idea where math comes from, why we can even do math in the first place, why the universe is so mathematical, and why we find math so fascinating. While teaching about Poisson’s Ratio in my Mechanics of Materials course recently, I shared a bold quote by S. D. Poisson that made my students groan and roll their eyes in feigned disgust. The accomplished 19th century French mathematician, engineer and physicist is purported to have 3

Biomimetics is the study of the formation, structure, or function of biologically produced substances and materials (such as enzymes or silk) and biological mechanisms and processes (such as protein synthesis or photosynthesis) especially for the purpose of synthesizing similar products by artificial mechanisms which mimic natural ones (https://www.merriam-webster.com/dictionary/biomimetics, accessed on November 1, 2019). See Bar-Cohen, 2006.

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said (Barral, 1854, p. 662), “Life is good for only two things: to do mathematics and to teach it!” Although obviously overstated, I could tell that my students understood what Poisson was getting at. As sophomores in engineering, they were already experiencing the surprising power and beauty of mathematics for predicting the behavior of the natural world. And they were already becoming adept at using mathematical tools for creative problem solving. Engineers, in particular, seem to develop a keen sense of the immense value of mathematics and the enigma of its existence. Perhaps the field of mathematics is an important clue to the meaning of the universe? It appears to bear on some of our deepest questions. Philosopher and Christian apologist, William Lane Craig, in his debate with atheist philosopher Alex Rosenberg, offered the effectiveness of mathematics as one of his main reasons why he believes faith in God is a reasonable proposition (Miller and Gould, 2014). Renowned Oxford mathematical physicist, Roger Penrose, describes the “triple mystery of mathematics” that arises from the relations between the three known “worlds” of 1) our conscious perceptions, 2) the physical world, and 3) the Platonic world of mathematical forms. The first mystery is that the world of physical reality appears to obey laws that actually reside in the world of mathematical forms. Secondly, the stuff of our conscious perceptions (our brains) seem to emerge from the physical world. Thirdly, and to complete this circular mystery, our perceiving minds are somehow able to gain access to the world of mathematical forms. Instead of offering an explanation for this triple mystery, Penrose (2004, concludes by writing, “No doubt there are not really three worlds but one, the true nature of which we do not even glimpse at present.” I respectfully disagree with Penrose on this last point. Scientists and engineers have been gathering clues about the nature of our universe for a few millennia now, and this enterprise has been accelerating rapidly in the last several decades. I assert that the compilation and integration of all these clues at least gives us a small glimpse into, and slight inkling of, what the real world is truly like. Further discussion of these ideas can be found in my recent book (Halsmer,

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2019), Hacking the Cosmos: How Reverse Engineering Uncovers Organization, Ingenuity and the Care of a Maker.

A CASE STUDY IN REVERSE ENGINEERING FROM SHERLOCK HOLMES In view of this thinking, reverse engineering is very similar to the collection of clues and the ensuing analysis that accompanies a crime scene investigation. Of course, the leading expert of such activities is the preeminent, but alas, fictional, London detective, Mr. Sherlock Holmes, created by Arthur Conan Doyle (1930) near the turn of the 20th century. Let us dissect one of his shorter cases (pp. 53-82) to see what it can tell us about a philosophy and methodology of reverse engineering. In the “Adventure of the Dancing Men,” Holmes is contacted by a worried husband whose new wife has been terrorized by a series of cryptic notes she has received in the form of dancing stick figures, as shown in Figure 1. Upon receipt of the first such message, and given the wife’s reluctance to discuss the matter, Holmes admits that there is not enough evidence to make any significant progress. However, when a few more samples of the agitated stick men turn up, Holmes flies into action in an effort to decipher the meaning of these strange hieroglyphics. With his background in criminology, and coded messages, he recognizes these skinny, but happyfeeted revelers as a device that was created to convey secret communications. And with enough samples, and some clever analysis, such a device might be reverse engineered. Notice first of all that his background knowledge and observations of the wife’s reactions to these notes were useful in determining that something disturbing was indeed being communicated. Background knowledge from obscure fields, as well as direct observations, are often very useful in reverse engineering projects. During another adventure, when explaining his solution in “The Tragedy of Birlstone,” Holmes enlightens (Doyle, p. 218), “Breadth of view…is one of the essentials of our profession. The interplay of ideas and the oblique uses of knowledge are often of extraordinary interest.”

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Figure 1. The First Cryptic Message from Doyle’s (p. 55) “The Adventure of the Dancing Men”.

In reference to the antics of the dancing men, Holmes makes an interesting inference when he suggests (p. 74), “The object of those who invented the system has apparently been to conceal that these characters convey a message, and to give the idea that they are the mere random sketches of children.” He further conjectures that each of the symbols stands for a letter of the alphabet. But since the characters were all equally spaced, it was difficult to determine the size of each word. Knowing that E is the most common letter in English, by a fairly wide margin, he determines, with a good measure of confidence, which character stands for the letter E. He explains (p. 74), “Out of fifteen symbols in the first message, four were the same, so it was reasonable to set this down as E. It is true that in some cases the figure was bearing a flag, and in some cases not, but it was probable, from the way in which the flags were distributed, that they were used to break the sentence up into words. I accepted this as a hypothesis…” This insight suggests that even minor, or seemingly insignificant details, might be of vital importance for making progress in reverse engineering. At this point, Holmes made progress in small steps, by noting, for example, that one message was a single word of 5 characters with an E in the 2nd and 4th spots. He reasoned (p. 75), “It might be ‘sever,’ or ‘lever,’ or ‘never.’ There can be no question that the latter reply to an appeal is far the most probable, and the circumstances pointed to its being a reply written by the lady.” So now he knew what characters stood for N, V, and R. Another small step was achieved by realizing that the wife’s name, Elsie, might very likely turn up in the messages. In this way, Holmes was able to determine which characters stood for L, S, and I. And so it went, until

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finally, the code was broken, and the name and location of the antagonist were discovered. After suspecting that he was an American, since Elsie came from Chicago a year earlier, Holmes confirmed his identity by putting in a cable to his friend in the New York Police Bureau who recognized the name as “the most dangerous crook in Chicago” (p. 76). Once again, we see how Holmes’ singular resourcefulness greatly assisted in his reverse engineering of the situation. This crook, who had a previous relationship with Elsie in America, was here in England attempting to wrest her, against her will, from her new husband. Unfortunately, the case has a rather sad ending. As Holmes and his trusted sidekick, Dr. Watson rush to arrive at the couple’s home near the eastern coast of England, they discover that a gun battle has recently ensued, leaving the husband dead, with the perpetrator having fled to his temporary lodgings in a nearby village. Upon all these revelations, the local police inspector, who is on the scene, pushes for immediate pursuit of the criminal. But Holmes explains that there is no need since he has just sent a letter to the crook inviting him to return to the house. The conversation picks up with the police inspector, “But why should he come?” “Because I have written and asked him.” “But this is incredible Mr. Holmes! Why should he come because you have asked him? Would not such a request rather rouse his suspicions and cause him to fly?” “I think I have known how to frame the letter,” said Sherlock Holmes (p. 77).

If you have not already deduced it, Holmes wrote to the crook using the code of the dancing men. As far as the crook knew, he and Elsie were the only two in England who knew the code. So he was glad to return, thinking that Elsie had experienced a change of heart, and had sent the invitation to him. A few moments later he walked right in the front door, asking for Elsie, whereupon he was summarily handcuffed and arrested. Astonished when he discovered that Elsie was at death’s door after an attempted suicide, he asked,

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Dominic Halsmer, P. Wesley Odom, Jessica Fitzgerald et al. “If the lady is hurt as bad as you say, who was it that wrote this note?” “I wrote it, to bring you here.” “You wrote it? There was no one on earth outside the Joint who knew the secret of the dancing men. How came you to write it?” “What one man can invent another can discover,” said Holmes (p. 79).

Here we see Holmes making a very clear statement of reverse engineering. Whatever may be invented by someone can be discovered by another with enough knowledge base, resourcefulness, careful observation, and detailed analysis. In effect, Holmes not only reverse engineered the code, but also reengineered it by using it for a good purpose, i.e., establishing justice, perhaps for the first time, instead of actualizing criminal intent with it. There are a few other helpful clarifications by Holmes on this approach to problem solving, but they are found in other cases. In describing the way individual clues tend to accumulate and lead to a conclusion, Holmes states (p. 98), “Each fact is suggestive in itself. Together they have a cumulative force.” In describing backwards, or analytical, reasoning, which is crucial to reverse engineering, Holmes explains (p. 138), “In solving a problem of this sort, the grand thing is to be able to reason backwards. That is a very useful accomplishment, and a very easy one, but people do not practice it much. In the everyday affairs of life it is more useful to reason forward, and so the other comes to be neglected. There are fifty who can reason synthetically for one who can reason analytically…Most people, if you describe a train of events to them, will tell you what the result would be. They can put those events together in their minds, and argue from them that something will come to pass. There are few people, however, who, if you told them a result, would be able to evolve from their own inner consciousness what the steps were which led up to that result. This power is what I mean when I talk of reasoning backward, or analytically.”

With regard to the potential for this kind of reasoning, Holmes muses (p. 118),

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“The ideal reasoner…would, when he had once been shown a single fact in all its bearings, deduce from it not only all the chain of events which led up to it but also all the results which would follow from it. As Cuvier could correctly describe a whole animal by the contemplation of a single bone, so the observer who has thoroughly understood one link in a series of incidents should be able to accurately state all the other ones, both before and after. We have not yet grasped the results which the reason alone can attain to. Problems may be solved in the study which have baffled all those who have sought a solution by the aid of their senses. To carry the art, however, to its highest pitch, it is necessary that the reasoner should be able to utilize all the facts which have come to his knowledge; and this in itself implies, as you will readily see, a possession of all knowledge, which, even in these days of free education and encyclopedias, is a somewhat rare accomplishment.”

Once again, from this quote, we see the importance of drawing from all pertinent areas and forms of knowledge when conducting reverse engineering. He also stressed the importance of sifting the mountain of data and attending to only the relevant evidence, as seen in the following passage (p. 241), “The principal difficulty in your case,” remarked Holmes in his didactic fashion, “lay in the fact of their being too much evidence. What was vital was overlaid and hidden by what was irrelevant. Of all the facts which were presented to us we had to pick just those which we deemed to be essential, and then piece them together in their order, so as to reconstruct this very remarkable chain of events.”

It would have been interesting to see the character of Sherlock Holmes apply his singular reasoning abilities to the reverse engineering of natural systems. We know that he occasionally conducted chemical experiments, and was very interested in this area of study from the following passage where he states (p. 259), “…Of late I have been tempted to look into the problems furnished by nature rather than those more superficial ones for which our artificial state of society is responsible.”

One of the most fascinating passages related to this idea comes during a particularly gruesome case called “The Adventure of the Cardboard

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Box.” This story involves a jealousy-ridden love triangle that ended in a double murder. After which, the murderer terrorized a surviving sibling of one of the deceased by sending her a cardboard box containing her sister’s severed ears. Holmes is visibly shaken by the grotesque nature of the incident, and waxes philosophical for a few moments. The reader can almost hear the voice of Arthur Conan Doyle (p. 62) crying out through the pages for an explanation for all the evil, pain and suffering in the world. ‘“What is the meaning of it, Watson?” said Holmes solemnly as he laid down the paper. “What object is served by this circle of misery and violence and fear? It must tend to some end, or else our universe is ruled by chance, which is unthinkable. But what end? There is the great standing perennial problem to which human reason is as far from an answer as ever.”’

But in another passage, we find Holmes backing away significantly from this precipice of despair. In the middle of a not-so-unusual case, out of the blue, his gaze becomes fixed on a vase of roses, and he takes time to expound on the role of reason in religious faith, and articulates what has become known as the Providential Argument from Beauty. “There is nothing in which deduction is so necessary as in religion,” said [Holmes], leaning with his back against the shutters. “It can be built up as an exact science by the reasoner. Our highest assurance of the goodness of Providence seems to me to rest in the flowers. All other things, our powers, our desires, our food, are all really necessary for our existence in the first instance. But this rose is an extra. Its smell and it colour are an embellishment of life, not a condition of it. It is only goodness which gives extras, and so I say again that we have much to hope from the flowers.” (p. 220).

With that, let us leave Sherlock Holmes, Dr. Watson, and their creator, Arthur Conan Doyle to explore the ways reverse engineering is currently being incorporated into college and university curricula to enhance engineering education. We will return to this topic and consider additional philosophical perspectives towards the end of this chapter.

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THE ROLE OF REVERSE ENGINEERING IN ENGINEERING EDUCATION A recent article in ASEE Prism Magazine (Buki and Grose, 2012, p. 33) refers to a prominent astrophysical institute’s attempts at “reconstructing and visualizing the universe’s early days” as “the ultimate reverse engineering project.” This reference to science as the reverse engineering of natural systems is consistent with the National Academy of Engineering’s (NAE, 2013) recent announcement that one of their Grand Challenges for the twenty-first century is to “reverse engineer the human brain.” Many scientists and engineering educators are now beginning to recognize the value of the reverse engineering mindset, not only for unraveling the mysteries of nature, but also for teaching the intricacies of design in the engineering laboratory. The last two decades have seen a significant increase in the number of universities that have integrated this method into their teaching (Wu, 2008). Reverse engineering is simply taking an object apart and analyzing its “inner workings,” in order to understand the secrets behind its design operation. However, some researchers use a more specific term, Disassemble/Analyze/Assemble (DAA), for these activities (Ogot and Kremer, 2006). A study (Dalrymple et al., 2011) comparing the results of such activities to the more traditional laboratory approach concludes that DAA activities have the potential to increase student motivation and promote transfer. Transfer refers to the ability to apply or adapt knowledge when seeking a novel solution to a problem. New courses are being developed that make use of reverse engineering projects to help students observe actual designs during “incremental concrete experiences,” allowing them to reflect on the “big picture” of engineering (Wood et al., 2013). One such effort integrates the introduction to engineering course and the engineering graphics course around a reverse engineering project, making use of 3-D computer modeling and rapid prototyping of the disassembled parts for reengineering considerations (Barr et al., 2000). Another study compares eight different methods for teaching design to first-year students and concludes that a reverse engineering model is preferred (Burton and White,

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1999). Another institution has seen a surge in self-esteem and an increase in PRIDE (Purpose, Responsibility, Individuality, Determination and Excellence) after full implementation of a comprehensive reverse engineering module among first year engineering students (Mani and Rao, 2018). An attempt to increase student interest is made by creating a game whereby students are awarded achievement levels for gaining particular insights during reverse engineering activities (Foster et al., 2012). One engineering educator (Hess, 2002) went so far as to report that reverse engineering has “proven to be the instructor’s fire keg that lights the imaginations of the engineering students.” Another project (Campbell, 2002) attempts to “escape the tedium” of traditional instruction by charging students with the task of recreating an improved version of an existing mechanical artifact with the Lego MindstormsTM kits. This focus on improving an existing design is also emphasized in a course where reverse engineering activities lend insight into “evolutionary product design,” which also assists with student retention (Orono et al., 2005). In another course, students are encouraged to dissect “a device with some kind of malfunction to study the reason behind the unit breakdown,” and try to come up with conceptual ideas for improvement based on their studies (Rad, 2012). In a manufacturing processes course, reverse engineering of part of an airplane wing is conducted to generate a computer model that can be 3-D printed and subjected to finite element analysis to validate its reliability (Eslami, 2017). At other universities, reverse engineering projects are used to reinforce the elements of engineering graphics and CAD/CAM courses (Ansari, 2003; Ross, 2003). In another manufacturing related course, students reverse engineered electric drills, radio controlled cars, door knobs and Nerf guns, and addressed questions of why particular materials and manufacturing processes were chosen. They also explored alternative materials and processes, and discussed the resulting effects on the environment (Chattopadhyay, 2015, 2017).

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REVERSE ENGINEERING AND DESIGN RECOVERY These kinds of activities demonstrate the value of reverse engineering for answering deeper questions than simply how a product works. Good questions range from how the components are designed, what materials are used, how the parts are assembled and how each component functions as part of the whole, to what criteria and constraints are met, what primary need is addressed, what engineering disciplines contributed, what limitations exist in the design and how the product might be misused (Kellogg and Jenison, 1997). In this regard, some researchers make a distinction between reverse engineering (as artifact dissection for recovering its mechanisms of operation) and design recovery, which has “the goal of recovering the design processes that went into creating the artifact,” emphasizing the later, rather than the former. They argue that design recovery considerations are the key to confronting students’ common design misconceptions (McCracken and Newstetter, 2000). While somewhat dependent on the particular field of study, other researchers have a slightly different definition for design recovery. One helpful article, although dealing mainly with the reverse engineering of computer software, provides more detail on the concept of design recovery. Here, it is defined to be (Chikofsky and Cross, 1990) “a subset of reverse engineering in which domain knowledge, external information, and deduction or fuzzy reasoning are added to the observations of the subject system to identify meaningful higher level abstractions beyond those obtained directly by examining the system itself.” In other words, the goal of design recovery is to work out, at a higher level of understanding, what a system or component was engineered to do, and (to some degree of confidence) why, rather than just examining its subcomponents and their interrelationships. This generally involves extracting design artifacts, by detecting design patterns for example, and synthesizing abstractions that are less dependent on implementation. It is these higher level abstractions that are believed to be the key to fully reverse engineering complex natural systems. “Design recovery recreates design abstractions from a combination of code [system], existing design

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documentation (if available), personal experience, and general knowledge about problem and application domains…Design recovery must reproduce all of the information required for a person to fully understand what a program [or system] does, how it does it, why it does it, and so forth.” (Biggerstaff, 1989). The development of a comprehensive design recovery framework for mechanical components (Urbanic and El Maraghy, 2009) appears to be a promising approach that addresses these questions.

REVERSE ENGINEERING OF NATURAL SYSTEMS IN ENGINEERING EDUCATION Reverse engineering activities can even be helpful in advancing engineering education, and relating science and engineering, in the P-12 classroom. Such activities are advocated since younger students do not know all the intentions of the original engineer and must infer them by observing and systematically evaluating the causal relationships that produce functionality. “This process is not trivial and can involve very similar scientific inquiry skills used to understand natural systems” (Brophy et al., 2008). Indeed, natural systems have become popular objects of reverse engineering activities at the university level also. Students learn the biomedical engineering design process by developing 3-D physical models of human anatomy based on medical imaging data using rapid prototyping and reverse engineering (Sirinterlikci, 2012). Other modules have been developed to teach chemical engineering students how to apply engineering principles to living systems. These modules include reverse engineering of the human body, the beer making process, and the design of a microbial fuel cell (Hollar et al., 2003). Other students become familiar with various aspects of biomedical engineering by reverse engineering over-the-counter low-cost diagnostic devices such as pregnancy testing and drug testing kits (Staehle, 2018). Other studies highlight the multidisciplinary aspect of this approach. “The human body is an exquisite combination of interacting systems which can be analyzed using multidisciplinary engineering principles…Students

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are engaged in the scientific discovery process as they explore the engineering systems within the human body using exciting hands-on ‘reverse engineering’ methods.” (Farrell et al., 2004). Since students have a natural curiosity to learn how their own bodies work, such activities are thought to increase understanding and retention of new concepts, such as energy balances in the human body (Farrell et al., 2003). Other researchers use modules on the reverse engineering of living systems and ecosystems to teach concepts that are vital for sustainability (Jahan, 2007). Of course, the reverse engineering of the human brain (as per the NAE’s Grand Challenges) is also being addressed by engineering educators and researchers. In an introductory digital signal processing course, students learn about (Huettel, 2011) “the process of collecting and analyzing electroencephalography data in a local neuroscience research laboratory.”

A CURRICULAR MODULE ON THE HISTORY AND PHILOSOPHY OF REVERSE ENGINEERING IN BIOLOGICAL SYSTEMS While the studies referred to previously are certainly interesting and valuable for engineering education, background and context can be added to these activities by the addition of a curricular module on the history and philosophy of reverse engineering in biological systems. This module is not intended to be an extensive or exhaustive coverage of these topics, but rather will serve as a brief introduction to the subject. It is believed that this background information will further motivate and equip engineering students in the wise application of knowledge and stewardship principles when dealing with natural systems. Not much has been found in the literature on the history and philosophy of reverse engineering in biological systems, although some preliminary discussions have been offered (Halsmer et al., 2008, 2009). The module was developed by a multidisciplinary (engineering, biomedical engineering, and philosophy) group of five undergraduate honors students under the guidance of faculty members from engineering,

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biology and philosophy. The students met weekly to share research findings and discussion on the reverse engineering of natural systems. The module is being implemented and tested in an undergraduate honors course on the History of Quantitative Thought. This course is comprised of engineering majors, biology majors and some honors students in nontechnical majors. The learning outcomes entail that, upon completion of this module, students will be able to: 1. Discuss the history of reverse engineering in biological systems 2. Discuss the philosophical issues and implications associated with the reverse engineering of biological systems 3. Discuss current techniques and applications in reverse engineering 4. Conduct an effective reverse engineering investigation 5. Work toward reconciliation of findings in biology with personal spirituality and worldview 6. Apply principles of stewardship and wisdom in dealing with ethical dilemmas in biology and biomedical engineering This module contributes to the following educational objectives, which are among those that engineering graduates are expected to attain within a few years after graduation from this program: 1. Graduates will apply their technical knowledge to design and analyze systems and to solve ever-changing real-world engineering problems 2. Graduates will engage in lifelong learning and professional development 3. Graduates will apply wisdom in the administration of stewardship principles and discipline, being committed to professional and ethical standards of responsibility These learning outcomes will be accomplished (contributing to the realization of the educational objectives as well) through implementation of a curricular module consisting of the following elements:

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1. Overview of the concept and techniques of reverse engineering 2. Legitimacy of applying reverse engineering principles to natural systems 3. Brief history of reverse engineering in biological systems 4. Overview of philosophical issues arising from reverse engineering in biological systems 5. Reverse engineering laboratory exercise #1: Man-made object 6. Reverse engineering laboratory exercise #2: Natural object (second-hand) 7. Testing and assessment activities Several good books that discuss the concept of reverse engineering, its methods and techniques, are currently in use in academia and industry (Messler, 2014; Otto and Wood, 2000; Sweigers et al., 2012; Wang, 2011). The module draws from these sources, as well as journal articles (Caste and Doyle, 2002; Wilson and Rosen, 2007) in providing an overview, with examples of both artificial objects and natural objects. A classic example of the reverse engineering of a highly corrupted artificial object is illustrated in the case of the Antikythera Mechanism. This 100-year-saga of the design recovery of what is thought to have been the first analog computer, which simulated the motions of celestial objects, provides many insights into the potential and processes of reverse engineering (Marchant, 2009). Several working models of the Antikythera Mechanism have now been developed based on the results of this quintessential reverse engineering project, and more details are still being uncovered (Stikas, 2014). A fascinating example of the reverse engineering of a biological system is provided by a team of researchers who studied the mechanism by which E. coli bacteria withstand heat shock (El-Samad et al., 2005). Their study was reviewed by another team (consisting of an engineer and a pathologist) in a paper entitled, “Understanding Biology by Reverse Engineering the Control.” The original team applied techniques such as “subtract and operate” in systematically eliminating both feedback and feedforward information pathways and observed the resulting performance.

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In assessing the overall achievement and results of the study, the reviewers (Tomlin and Axelrod, 2005) concluded that “the mechanism used in E. coli to combat heat shock is just what a well-trained control engineer would design, given the signals and the functions available.” One can easily see that these kinds of conclusions naturally lead to interesting discussions about how such exquisite engineering can emerge by accident. Nevertheless, researchers continue to apply reverse engineering techniques to natural systems simply because it works. Biologist and philosopher E. O. Wilson (1999, p. 112) writes, “The surest way to grasp complexity in the brain, as in any other biological system, is to think of it as an engineering problem…Researchers in biomechanics have discovered time and again that organic structures evolved by natural selection conform to high levels of efficiency when judged by engineering criteria.” This is reason enough to legitimize its application to natural systems, and especially in “systems biology,” which has been defined as (IEEE, 2008) “the quantitative analysis of networks of dynamically interacting biological components, with the goal of reverse engineering these networks to understand how they robustly achieve biological function.” Several sources provide a step by step method for conducting a reverse engineering investigation. Although the paper cited earlier by Wilson and Rosen, entitled “Systematic Reverse Engineering of Biological Systems,” is primarily aimed at determining biological solutions for technological advancement, they provide the following helpful “Steps for Reverse Engineering Biological Systems.” 1. Identify and detail sub-function of interest 2. Identify candidate biological systems 3. Decompose architecture of biological system of interest (a) Decompose physical architecture (b) Decompose functional architecture 4. Identify state and function cycles 5. Develop behavioral model and truth table for functionality 6. Extract biological strategy in abstract form

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7. Idea generation (for engineering technologies to mimic this strategy) An illustrative example of reverse engineering bird wings to assist in the development of morphing aircraft wing structures is also offered in the paper (Wilson and Rosen, 2007). The structure of bird wings have recently received attention as elegant optimal solutions to multiple (Burgess, 2007) and particularly difficult (Forsching and Hennings, 2012) engineering problems. The next section contains examples of some material that will provide a historical perspective of reverse engineering. This material also provides a good lead-in to some of the important philosophical issues that arise.

HISTORICAL EXAMPLES FROM THE ANCIENT EGYPTIANS, GALEN, AND WILLIAM HARVEY The reverse engineering method, also known as “reverse problem solving,” has certainly been in use for a very long time. One of the most recent books on reverse engineering, by Robert Messler (2014), describes one of the earliest known examples involving the Great Pyramid of Khufu in Egypt around 2560 BC. During construction, workers noticed a crack forming in the 50-ton granite beam across the ceiling of the king’s burial chamber deep in the core of the pyramid. This imperfection indicated that a redesign was needed to deflect the huge load away from the center of this beam and out toward the supports at each side of the chamber. With some repositioning of structural elements, this was accomplished, and the Great Pyramid was saved (Messler, 2014, pp. 29-32). This example illustrates the importance of attending to the relationship between the engineer and his or her creation. In this case, the pyramid was crying out (through the crack) to its creators for help! Fortunately, the Egyptian engineers and architects had the wisdom to listen and respond appropriately. Good reverse engineering is attentive to the relationships among the triad of engineer, artifact, and user, which comprise the big picture of engineering design (Maier, 2011).

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Aelius Galenus, or otherwise known as Galen of Pergamon, was a well-known Roman scientist and physician around 170 AD. His anatomical discoveries about the physiology of the body were studied for hundreds of years after his death, even into the nineteenth century. As a student of Hippocrates, Galen was constantly focused on learning more about the world around him, especially when it concerned the workings of the body. Eventually finding himself at the prestigious medical school in Alexandria, he began to sharpen his understanding about the theories of the body at the time. He continued this education by taking a position as a surgeon to the gladiators, where he learned much about the treatment of injuries, maintenance of hygiene, and living anatomy. It wasn’t until 162 AD that Galen left for Rome to establish himself as a prominent physician. It was at this time, while he was the acting physician under Marcus Aurelius that Galen began to contribute to scientific understanding. Galen was most noted for his theories on the circulatory system. Since human dissection was banned in Rome during Galen’s time, he had to settle on dissecting pigs and other animals. It was through this that Galen learned much about the circulation of blood through the body and the various parts that perform those functions. However, dissections were not the only means by which Galen was able to make these discoveries. During this time, there were two main schools of thought relating to the proper way to do science. The first were the Empiricists, who believed that the only way to go about finding scientific truth was through direct observation of physical phenomena. The second were the Rationalists, those who held the belief that study of already established teachings through philosophical reasoning was the correct method. Galen, rather than choosing one side or the other, thought it best to combine the two schools of thought. This became known as being a Methodist. Galen understood the need for direct observation in scientific discovery (empiricism), but he did not discount the importance of philosophical reasoning in the pursuit of scientific truth (rationalism). Galen was very philosophical and was a firm believer in involving philosophy and purposeful thinking in scientific endeavors. In his work entitled That the Best Physician is also a Philosopher, Galen emphasized

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the fact that science should be considered a cross disciplinary art, making use of observation as well as philosophical thought. Galen also held the belief that “Everything in nature has a purpose, and that nature uses a single object for more than one purpose whenever possible.” This principle continues to evoke wonder, even today, as evinced by a recent article where a biological engineering researcher at MIT (Lang, 2007, p. 13) writes, “Nature, it appears, copies and pastes design elements for motors with similar roles and physical constraints. Despite different tracks and overall purposes for motility, all of the designs of common biological machines have been optimized.” This perceived economy of nature was what drove Galen to consider the way by which the blood received oxygen. He deduced that the heart was not only vital in driving the blood through the body, but also driving it to be resupplied with vital oxygen. Although some of his thoughts were incorrect, he established the main concept of the circulatory system through the use of direct observation and purposeful reasoning (Bos, 2011). Galen’s views on the anatomy of the circulatory system continued to be the standard for hundreds of years after his time. The view remained generally the same until a physician by the name of William Harvey challenged the teaching with his own new theory in the mid 1600s. William Harvey was the son of Thomas Harvey, a wealthy politician and governor of Folkestone, England. Being born into a privileged family gave young Harvey the chance of furthering his education and pursuing higher degrees. After graduating from Caius College in 1597, Harvey travelled to the medical college at Padua where he would find his calling as a physician. Harvey was an excellent student, familiarizing himself with all the theories and knowledge of the time. It was once said about him that he “conducted himself so wonderfully well in the examination and had shown such skill, memory and learning that he had far surpassed even the great hopes which his examiners had formed of him.” After graduating from Padua in 1602, the now 24-year-old Harvey travelled back to London to practice medicine. After some time, his skill showed through and he was eventually appointed to physician of King James I in the year 1612. It was in this station that Harvey began to start

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his research and further his understanding about the anatomy of the human body. Harvey spent these years focused upon the circulatory system of the body. He was fascinated with the process of blood circulation, and was continually searching for the truth behind the process. As “physician extraordinary” to James I, Harvey had some access to cadavers by which to conduct his research. This access to the human body proved invaluable in Harvey’s research, however he began to see things that did not match up to the commonly held teachings of the time. During the 1600s the view of the circulatory system was that of a body full of veins which could pump blood both to and from the heart simultaneously. Harvey, however, noticed a problem with this theory. He discovered that there was a network of valves placed throughout the circulatory system within the veins. These valves were shown to resist the flow of blood when closed. Also, they were shown to work in only one direction. Other scientists had noticed this anomaly as well, but had discounted it. In 1628, however, Harvey went against the commonly held view by publishing his work De Motu Cordis, in which he outlined his findings. Harvey postulated that there were actually two veinous systems involved in the circulatory system: veins and arteries. These were both one-way systems, with arteries carrying oxygenated blood away from the heart and veins carrying the deoxygenated blood back to it. This discovery was based upon his thoughts on the quantity of valves in the circulatory system. Harvey said, “I was invited to imagine that so provident a cause as nature had not placed so many valves without design: and no design seemed more probable than that... it [the blood] should be sent through arteries and return through veins, whose valves did not oppose its course that way.” Harvey, rather than merely looking at empirical evidence, chose to deduce that nature would design a system to work utilizing all of the components efficiently. Hence, Harvey’s theory on circulation would provide the “best” explanation of why there were so many valves present in the circulatory system. This theory was rejected by Harvey’s peers, and it wasn’t until much later that his work was fully appreciated and proven to be true. However, it is important to understand that his discovery of this system was brought about through a combination

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of empirical findings and rational reasoning with a reverse engineering mindset. Harvey went about science by considering natural systems as designed entities with purpose and intent in their function (Brecher, 1969; McMullen, 1998). Indeed, a recent article suggests that methodological naturalism may be too restrictive for achieving the most insight during reverse engineering investigations (Halsmer and Fitzgerald, 2011).

CONNECTIONS BETWEEN SCIENCE/ENGINEERING AND PHILOSOPHY Other researchers throughout the history of science and engineering have made similar connections to philosophy (consisting of the subdisciplines of epistemology, logic, ethics, metaphysics, and aesthetics). After detailed dissections and analyses of the human body, Leonardo DaVinci is credited with the following statement, “The human foot is a masterpiece of engineering, and a work of art.” A recent book on reverse engineering (Wang, 2011) claims, “The human body is a beautiful piece of engineering work in nature. Reverse engineering is the most effective way to reinvent the component parts of this engineering marvel due to lack of the original design data.” A recent article in Mechanical Engineering (Bhushan, 2012, p. 30) provides the following explanation, “Through biological evolution, Nature has conducted a 3.8 billion-year research and development program, and we find ourselves preparing to make commercial use of its discoveries.” This is a very interesting state of affairs, rife with metaphysical implications. In a new paper on the origin of life for the Royal Society journal Interface, cosmologist Paul Davies (Walker and Davies, 2012) writes, “To a physicist or chemist, life seems like ‘magic matter.’ It behaves in extraordinary ways that are unmatched in any other complex physical or chemical system. Such lifelike properties include autonomy, adaptability and goal-oriented behavior – the ability to harness chemical reactions to enact a pre-programmed agenda, rather than being a slave to those reactions.” This reference to “magic matter” is reminiscent of Eden

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Philpotts (1918) famous quote, “The universe is full of magical things, patiently waiting for our wits to sharpen.” It appears that even though he wrote this in 1918, the universe is still patiently waiting. Even the title of militant atheist Richard Dawkins’ latest book (Dawkins and McKean, 2012) refers to the “Magic of Reality.” Perhaps Arthur C. Clarke’s (1973, p. 39) Third Law is apropos, “Any sufficiently advanced technology is indistinguishable from magic.” But from where does such advanced technology come? Could it actually be obtained without intentionality, that is, by accident? Biologist Francois Jacob pictured evolution as a tinkerer, as opposed to an engineer (Jacob, 1977), but how is only a tinkerer able to produce “stunningly well-engineered” (Beckerman, 2005) systems? To be fair, not all scientists agree that the human body is so well-engineered. Biologist John Avise (2010) and science writer Philip Ball (2010) argue that such “shoddy” workmanship, as seen, for example, in disease-causing genetic malfunctioning, is not worthy of attributing to any form of intelligence. Even so, explanations for the “dark side” of the human condition that are consistent with new findings from the fledgling field of epigenetics have recently been offered (Halsmer and McDonough, 2011). A more recent article by biologist Uri Alon (2003) takes up Jacob’s discussion of tinkerer vs. engineer. In this article, Alon aims to compare evolution and evolutionary processes to that of a “tinkerer,” instead of an engineer. He says that unlike an engineer, tinkerers do not plan out what they are going to do, nor do they use any type of process to come to a certain solution. Instead, they use a process of guess and check, and try different things until they find something that works—a process of elimination. Though Alon makes this claim, that evolution is much like a “tinkerer,” he does state that the “solutions found by evolution have much in common with good engineering design.” He elaborates on three examples of these similarities between engineered systems and biological systems: modularity, robustness, and the use of recurring circuit elements. The first modularity, Alon defines as “a set of nodes [in a system] that have strong interactions and a common function.” In order for a set of nodes to be identified as a module, there must be input and output nodes,

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as well as internal nodes that do not have much effect on the nodes outside the module. Alon explains how networks with modularity, both in engineering and biological networks, make a system more adaptable to many different situations, because of the modules’ unique attributes. Though non-modular devices and networks exist in both engineered and natural systems, modular systems have the advantage because each component is not frozen by its inability to adapt; in modular networks, when new conditions arise, each can be configured and optimized to meet the new requirements—without the need for the whole system to change collectively. The second similarity between biological networks and engineered systems is the need for robustness. Alon points out that the systems that survive the most in nature have the ability to work under several different conditions, in several different environments. This need for robustness, Alon claims, “imposes severe constraints on its design,” thus narrowing the vast range of possible designs on paper to only a few that are adaptable enough to survive under the severe demands that are placed on the system due to changing conditions. The third, and final parallel found in both biological networks and engineered systems is the existence of recurring circuit elements. Both types of systems use repeated elements in a design to help the system carry out its function. These recurrences in biology are known as “network motifs,” and are found across several diverse systems; for example, similar network motifs are found in both the E. coli bacteria and the transcription network of yeast. Alon is careful to point out that just because network motifs exist; it does not mean that these similar circuits are duplicated one from another. It simply means that evolution, as a “tinkerer” seems to converge on the same network motifs time and again because these are the systems that have been proven to be the most effective for a wide range of biological systems. Once a motif has been defined, Alon claims, and a dictionary of sorts is completed, it will aid researchers in identifying different network motifs in each new network they come across, enabling a quicker identification and classification process.

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Each of these comparisons between biological networks and engineered systems may help humans understand and model natural systems. It is a huge task to undertake, to understand completely the vast range of cell-wide networks. The similarities between evolution as “tinkerer” and the engineer also raise the challenge, Alon states, of “understanding the laws of nature that unite evolved and designed systems.”

REVERSE ENGINEERING LABORATORY EXPERIENCE A key aspect of the module is the reverse engineering laboratory experience. The students first conduct a reverse engineering investigation into an artificial object, such as a simple electric motor or an aquarium pump. Dissection is conducted and reverse engineering techniques and methodology is followed to determine the nature of the device and the details of its design. Necessary tools and instruction are provided to allow the students to safely accomplish this investigation. The students are required to write a report that communicates the details of their investigation and the conclusions that were drawn. The students next conduct a reverse engineering investigation into a natural motor or pump that is regularly found in biological systems at the molecular level. Unfortunately, since not all the students in this class (History of Quantitative Thought) are biology majors, and hence lack the skill and equipment to conduct such an investigation first-hand, this is a “second-hand” or “virtual” reverse engineering investigation. In other words, students will gather digital information on the subject system reported by those who have conducted such investigations. Although perhaps not as valuable as a first-hand experience, students learn how to take advantage of the wealth of scientific and engineering information that is currently available on biological systems. Similar to the reverse engineering experience with the artificial object, students collect enough information to complete their investigation of the natural system, after which, they also write a report. In this report, they are asked to compare

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and contrast the workings of the artificial and natural systems, and draw conclusions with regard to the origins of each system. They are also asked to discuss any impact the module might have had on personal worldview and/or spirituality.

ASSESSMENT OF THE REVERSE ENGINEERING MODULE Laboratory reports are graded and returned to the students in a timely fashion. Midterm and final examinations are conducted that contain questions relating to material from the module on reverse engineering. Grades from the laboratory reports and examinations are used to assess the achievement of the learning outcomes. Pre- and post-module test/surveys are also conducted to help with assessment of learning outcomes. Student comments regarding the effectiveness (or lack thereof) of the module are also requested, both at the end of the module and at the end of the course. A shorter, preliminary version of this module has been conducted with small groups of undergraduate engineering and honors students over the past few years. An overwhelming amount of positive feedback, both anecdotally and from student opinion surveys, has encouraged further development of the more comprehensive module. Currently, assessment of the preliminary version, based on a five point Likert scale survey, indicates that participants are assisted in reconciling problems in science and religious faith (4.2), realize a greater sense of understanding of personal purpose (4.6), and are assisted in their ability to communicate with others on issues in science and religious faith (5.0) (Halsmer and Beck, 2012). In other words, all the surveyed participants strongly agreed that they had been assisted in communicating with others on issues in science and religious faith. The assessment instrument is currently being rewritten to cover the more complete set of learning outcomes and revised terminology associated with the comprehensive module. It is anticipated that assessment results from students taking the comprehensive module will be similar to those who have experienced the preliminary version.

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Assessment results for the comprehensive module on the history and philosophy of reverse engineering in biology are very encouraging. Based on a five point Likert scale, students generally agreed that participation in the module helped them understand the history of reverse engineering in biology (3.8), helped them understand the philosophy of reverse engineering in biology (4.4), helped them understand current techniques and applications in reverse engineering (4.2), taught them how to conduct an effective reverse engineering project (4.1), helped them reconcile modern biology with personal spirituality and worldview (4.3), and helped them apply principles of stewardship and wisdom in dealing with ethical dilemmas in biology (3.4). The relatively low score on the last item indicated that perhaps more time should be devoted to stewardship and ethics in the future. Ninety-two percent of respondents either agreed or strongly agreed that the module both helped them understand the philosophy of reverse engineering in biology, and helped them understand current techniques and applications in reverse engineering. Multiple respondents indicated their desire to have more time devoted to this subject. One respondent thought students should be “required to engineer/design/construct a device” rather than simply conduct a dissection. Another respondent appreciated being “asked thoughtprovoking questions in regard to the implications of our findings.” Although the sample size is relatively small (class of 14 with 2 absent on assessment day: N=12), the positive results and feedback are motivating and informing the continuing development of this module for implementation in other courses at Oral Roberts University such as Introduction to Engineering and Biomedical Engineering Survey.

CLOSING THOUGHTS ON REVERSE ENGINEERING With the new changes to ABET accreditation requirements for the 2019-2020 academic year, undergraduate engineering graduates will now be expected to have the ability to identify, formulate and solve complex engineering problems. According to ABET (2020), complex engineering

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problems include one or more of the following characteristics: they involve wide-ranging or conflicting technical issues, they have no obvious solution, they are not encompassed by current standards and codes, they involve diverse groups of stakeholders, they include many component parts or sub-problems, they involve multiple disciplines, and they have significant consequences in a range of contexts. This raises the bar for engineering students, requiring more than just the ability to solve typical back-of-the-chapter textbook type problems. Analytical thinking (as Sherlock Holmes called it) applied to reverse engineering problems appears to be tailor-made to address many, if not all, of these characteristics. Perhaps that’s why so many institutions are incorporating it into the engineering curriculum. But the application of this approach to natural systems raises some issues that certainly have significant consequences in a range of contexts. As a final example, two more recent studies confirm the fabulous control systems engineering that chugs along in our cells every moment, making life possible. An article in Nature describes how the integral feedback controller in our cells has finally been identified and mathematically modelled. But the researchers (Aoki et al., 2019, pp. 533-537) even went further, stating, “Here we prove mathematically that there is a single fundamental biomolecular controller topology that realizes integral feedback and achieves robust perfect adaptation in arbitrary intracellular networks with noisy dynamics…On the basis of this concept, we genetically engineer a synthetic integral feedback controller in living cells and demonstrate its tunability and adaptation properties.” In the second study out of the University of Arizona, “a team of…researchers has shown that cells and organisms evolved complex biochemical circuits that follow the principles of control theory, millions of years before the first engineer put pencil to paper…The team discovered that the coupling of two interconnected biochemical circuits within a cell…work like a thermostat to control the growth of cells in response to the availability of nutrients…The new research found that each pathway has its own distinct role and teased out exactly how and why the two pathways work together.” The results are described in an article (Mace,

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2019) entitled, “Control Theory: Mother Nature is an Engineer.” Having both studied and taught the subject of control systems, I am quite familiar with the difficulties involved in designing an appropriate and effective controller to stabilize an uncertain system while ensuring its required performance. The fact that these systems exist in nature and operate to such a high degree of perfection in mind-boggling. On the whole, the kind of analytical reasoning it takes to completely reverse engineer such systems is abductive reasoning.4 Although deductive and inductive reasoning are involved to some degree, an inference to the best explanation is required in the face of incomplete information. This is a skill in which human beings specialize and excel. It seems that we were made to make such inferences. Computing and Artificial Intelligence (AI) have accomplished great things in the realms of deductive and inductive reasoning, but appears to fall short in the arena of abductive reasoning (Littlefiedl, 2019). Philosopher Charles Sanders Peirce (1935) was the first to fully describe abductive reasoning. He asserted that abduction “is the only logical operation which introduces any new idea.” Human creativity, along with an innate value system to facilitate choice among an infinity of options, will ensure job security for humans when abductive reasoning is required, even if we may not be able to classify every reverse engineering problem as “elementary, my dear Watson.”

REFERENCES ABET, https://www.abet.org/accreditation/accreditation-criteria/criteriafor-accrediting-engineering-programs-2019-2020/. Alon, U. Biological Networks: The Tinkerer as an Engineer, Science, 301, pp. 1866-1867, September 2003.

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Abduction, or inference to the best explanation, is a method of reasoning in which one chooses the hypothesis that would, if true, best explain the relevant evidence. Abductive reasoning starts from a set of accepted facts and infers most likely, or best, explanations (https://www.newworldencyclopedia.org/entry/Abductive_reasoning). See Walton, 2005.

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Ansari, J. Computer Aided Reverse Engineering of a Toy Car, Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, GA, https://peer.asee.org/19333, June 2013. Aoki, SK; et al., A Universal Biomolecular Integral Feedback Controller for Robust Perfect Adaptation, Nature, 570, p. 533-537, June 2019. Avise, JC. Inside the Human Genome: A Case for Non-Intelligent Design, Oxford University Press, Oxford, 2010. Ball, P. What a Shoddy Piece of Work is Man, Nature News Online, May 3, 2010. Barr, RE; Schmidt, PS; Krueger, TJ; Twu, CY. An Introduction to Engineering through an Integrated Reverse Engineering and Design Graphics Project, Journal of Engineering Education, 89(4), pp. 413418, July 2000. Barral, JA. (ed.), Oeuvres complétes de François Arago ..., vol. II,a Paris, France: Gide et J. Baudry, 1854. Bar-Cohen, Y. Biomimetics: Biologically Inspired Technologies, CRC Press, Boca Raton, FL, 2006. Beckerman, M. Molecular and Cellular Signaling, New York: Springer, 2005. Bhushan, B. Nature’s Nanotechnology, Mechanical Engineering: The Magazine of ASME, 134(12), p. 30, December 2012. Biggerstaff, TJ. Design Recovery for Maintenance and Reuse, Computer, pp. 36-49, July 1989. Bos, FL. Genetic Regulation of Angiogenesis and Lymphangiogenesis: Visualization and Characterization of the Developing Embryonic Vasculation. Self-published, 2011. Brecher, GA. “History of Venous Research.” IEEE Transactions on Biomedical Engineering BME, 16(4), pp. 236-247, 1969. Brophy, S; Klein, S; Portsmore, M; Rogers, C. Advancing Engineering Education in P-12 Classrooms, Journal of Engineering Education, 97(3), pp. 369-387, July 2008. Buki, A; Grose, TK. Dream Factory: University Engineers Are Often the Hidden Stars Behind On-Screen Excitement, Prism, ASEE, Washington, D.C., September 2012.

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Burgess, SC. Multi-functioning and Multi-optimisation in Feathers, International Journal of Design and Nature, 1(1), pp. 1-10, 2007. Burton, JD; White, DM. Selecting a Model for Freshmen Engineering Design, Journal of Engineering Education, 88(3), pp. 327-332, July 1999. Campbell, MI. Teaching Machine Design through Product Emulation, Paper presented at 2002 Annual Conference, Montreal, Canada. https://peer.asee.org/11027, June 2002. Chattopadhyay, S. Assessments of Material Selection Activities in Undergraduate Reverse Engineering Projects, Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, WA, https:// peer.asee.org/18260, June 2015. Chattopadhyay, S. Material and Processing Basics through Reverse Engineering, Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, OH, https://peer.asee.org/28653, June 2017. Chikofsky, EJ; Cross, JH. II. Reverse Engineering and Design Recovery: A Taxonomy, IEEE Software, p. 15, January 1990. Clarke, AC. Profiles of the Future, Macmillan: New York, 1973. Csete, ME; Doyle, JC. Reverse Engineering of Biological Complexity, Science, 295(5560), pp. 1664-1670, March 2002. Dalrymple, OO; Sears, DA; Evangelou, D. The Motivational and Transfer Potential of Disassemble/Analyze/Assemble Activities, Journal of Engineering Education, 100(4), pp. 741-759, October 2011. Dawkins, R; McKean, D. The Magic of Reality: How We Know What’s Really True, Free Press, New York, 2012. Doyle, AC. The Complete Sherlock Holmes: The A. Conan Doyle Memorial Edition, Volume II, Doubleday, Doran & Company, New York: Garden City, 1930. El-Samad, H; Kurata, H; Doyle, JC; Gross, CA; Khammash, M. Surviving Heat Shock: Control Strategies for Robustness and Performance, Proceedings of the National Academy of Sciences, 102(8), pp. 27362741, February 2005. Eslami, AM. Integrating Reverse Engineering and 3-D Printing for the Manufacturing Process, Paper presented at 2017 ASEE Annual

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Conference & Exposition, Columbus, OH, https://peer.asee.org/28558, June 2017. Farrell, S; Kadlowec, J; Marchese, A; Schmalzel, J; Mandayam, S. Handon Experiments: Engineering and the Human Body, Paper presented at 2004 Annual Conference, Salt Lake City, Utah. https://peer.asee.org/ 13056, June 2004. Farrell, S; Savelski, MJ; Hesketh, R. A Hands-on Introduction to Energy Balances on the Human Body, Paper presented at 2003 Annual Conference, Nashville, Tennessee. https://peer.asee.org/11832, June 2003. Foster, JA; Sheridan, PK; Irish, R; Frost, GS. Gamification as a Strategy for Promoting Deeper Investigation in a Reverse Engineering Activity, Paper presented at 2012 ASEE Annual Conference & Exposition, San Antonio, TX, https://peer.asee.org/21419, June 2012. Halsmer, D. Hacking the Cosmos: How Reverse Engineering Uncovers Organization, Ingenuity and the Care of a Maker, Dubuque, IA: Kendall Hunt, 2019. Halsmer, D; Fitzgerald, J. Metaphysical Considerations Enhance Reverse Engineering Studies,” ASA Annual Meeting, North Central College, July 29 – August 1, 2011. Halsmer, D; Halsmer, N; Johnson, R; Wanjiku, J. The Applicability of Engineering Design Principles in the Formulation of a Coherent Cosmology and Worldview, Paper presented at 2008 Annual Conference & Exposition, Pittsburgh, Pennsylvania. https:// peer.asee.org/3501, June 2008. Halsmer, D; Beck, J. Encouraging Spiritual Vitality through Multidisciplinary Discussions on the Role of Engineering in Reconciling Science and Faith Issues, presented at the Spirituality and Honors Education Symposium, Indiana Wesleyan University, May 2931, 2012. Halsmer, D; McDonough, S. Affordance-based Reverse Engineering of Natural Systems with Possible Corruption, Proceedings of the Christian Engineering Education Conference, Trinity Western University, June 29 – July 1, 2011.

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Halsmer, DM; Odom, PW; Fitzgerald, J; Tryon, TG. Implementation and Assessment of a Curricular Module on the History and Philosophy of Reverse Engineering in Biological Systems, Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. (2013, June). https://peer.asee.org/19707. Halsmer, D; Todd, T; Roman, N. Integrating the Concept of Affordance into Function-based Reverse Engineering with Application to Complex Natural Systems, Paper presented at 2009 Annual Conference & Exposition, Austin, Texas. https://peer.asee.org/5761, June 2009. Hess, HL. Solid Modeling and Reverse Engineering: The Stimulus for Teaching Manufacturing, Paper presented at 2002 Annual Conference, Montreal, Canada. https://peer.asee.org/10347, June 2002. Hollar, K; Farrell, S; Hecht, GB; Mosto, PB. Integrating Biology and Chemical Engineering at the Freshman and Sophomore Levels, Paper presented at 2003 Annual Conference, Nashville, Tennessee. https://peer.asee.org/12102, June 2003. Huettel, LG. Connecting Theory and Practice: Laboratory-based Explorations of the NAE Grand Challenges, Paper presented at 2011 ASEE Annual Conference & Exposition, Vancouver, BC. https:// peer.asee.org/17655, June 2011. IEEE, IEEE Transactions on Automatic Control and IEEE Transactions on Circuits and Systems, p. 4, January 2008. Jacob, F. Evolution and Tinkering, Science, 196 (4295), pp. 1161-1166, June 1977. Jahan, K. The Aquarium Project: Teaching Engineering Principles and Sustainability, Paper presented at 2007 Annual Conference & Exposition, Honolulu, Hawaii. https://peer.asee.org/2055, June 2007. Kellogg, RS; Jenison, R. Utilizing Reverse Engineering to Explore the Design Process, Paper presented at 1997 Annual Conference, Milwaukee, Wisconsin. https://peer.asee.org/6892, June 1997. Landis, R; Peuker, S; Mott, J. Studying Engineering: A Road Map to a Rewarding Career, 5th ed., Discovery Press, Atascadero, CA, 2018. Lang, M. Lighting Up the Mechanome, The Bridge: Linking Engineering and Society, National Academy of Engineering, Winter, 2007.

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Littlefield, WJ. II. The Human Skill AI Can’t Replace, https:// quillette.com/2019/09/25/the-human-skills-ai-cant-replace/, September 25, 2019. Mace, M. Control Theory: Mother Nature is an Engineer, https:// uanews.arizona.edu/story/control-theory-mother-nature-engineer, August 7, 2019. Maier, J. Affordance Based Design: Theoretical Foundations and Practical Applications, Saarbrucken, Germany: VDM Verlag, 2011. Mani, BS; Rao, IJ. Stimulate PRIDE* in Freshmen with Reverse Engineering, Paper presented at 2018 FYEE Conference, Glassboro, NJ, https://peer.asee.org/31383, July 2018. Marchant, J. Decoding the Heavens, Da Capo Press, Cambridge, MA, 2009. McCracken, WM; Newstetter, W. Reverse Engineering or Design Recovery: Two Approaches to Uncovering Designing, Paper presented at 2000 Annual Conference, St. Louis, Missouri. https://peer.asee.org/ 8670, June 2000. McMullen, ET. William Harvey and the Use of Purpose in the Scientific Revolution: Cosmos by Chance or Universe by Design? Lanham, MD: University of America, 1998. Messler, RW. Reverse Engineering: Mechanisms, Structures, Systems and Materials, New York, McGraw Hill, 2014. Miller, C; Gould, P. ed., Is Faith in God Reasonable? Debates in Philosophy, Science and Rhetoric, New York: Routledge, 2014. National Academy of Engineering, Introduction to the Grand Challenges for Engineering, Washington, DC, www.engineeringchallenges.org, 1 Jan. 2013, http://www.engineeringchallenges.org/cms/8996/9221.aspx. Ogot, M; Kremer, G. Developing a Framework for Disassemble/ Assemble/Analyze (DAA) Activities in Engineering Education, Paper presented at 2006 ASEE Annual Conference & Exposition, Chicago, IL, https://peer.asee.org/1103, June 2006. Orono, PO; Ekwaro-Osire, S. Evolutionary Design Paradigm as a Retention Tool, Paper presented at 2005 Annual Conference, Portland, Oregon. https://peer.asee.org/15580, June 2005.

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Otto, K; Wood, K. Product Design: Techniques in Reverse Engineering, Prentice-Hall, Englewood Cliffs, NJ, 2000. Penrose, R. The Road to Reality: A Complete Guide to the Laws of the Universe, New York: Random House, 2004. Philpotts, E. A Shadow Passes, Cecil Palmer and Hayward, 1918. Peirce, CS. Collected Papers of Charles S. Peirce, Volume V, Section 172, Harvard University Press, Cambridge, MA, 1935. Rad, H. Reverse Engineering as a Learning Tool in the Design Process, Paper presented at 2012 ASEE Annual Conference & Exposition, San Antonio, Texas. https://peer.asee.org/21887, June 2012. Ross, DH. A Significant Reverse Engineering Project Experience within an Engineering Graphics Class, Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, GA, https://peer.asee.org/19116, June 2013. Sirinterlikci, A. Teaching Biomedical Engineering Design Process and Development Tools to Manufacturing Students, Paper presented at 2012 ASEE Annual Conference & Exposition, San Antonio, Texas. https://peer.asee.org/21996, June 2012. Staehle, M. Learning from Two Little Blue Lines: Introducing Biomedical Engineering by Reverse Engineering a Low-Cost Diagnostic Device, Paper presented at 2018 ASEE Annual Conference & Exposition, Salt Lake City, UT, https://peer.asee.org/30000, June 2018. Stikas, C. Antikythera Mechanism, The Book: Unwinding the History of Science and Technology, Athens, Greece, Constantin Stikas, 2014. Sweigers, G; Lehn, J; Benyus, J. Bioinspiration and Biomimicry in Chemistry: Reverse-Engineering Nature, Hoboken, NJ, Wiley, 2012. Tomlin, CJ; Axelrod, JD. Understanding Biology by Reverse Engineering the Control, Proceedings of the National Academy of Sciences, 102(12), pp. 4219-4220, March 2005. Urbanic, RJ; El Maraghy, WH. A Design Recovery Framework for Mechanical Components, Journal of Engineering Design, 20(2), pp. 195-215, April 2009. Walker, SI; Davies, PCW. The Algorithmic Origins of Life, Interface, (in press), December 2012.

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Walton, D. Abductive Reasoning, Tuscaloosa, AL: University of Alabama Press, 2005. Wang, W. Reverse Engineering: Technology of Reinvention, CRC Press, Boca Raton, FL, 2011. Wigner, Eugene P. “The Unreasonable Effectiveness of Mathematics in the Natural Sciences,” Communications on Pure and Applied Mathematics, vol. xiii, pp. 1-14, 1960. Wilson, EO. Consilience: The Unity of Knowledge, Knopf Publishing Group, Westminster, MD, 1999. Wilson, JO; Rosen, D. Systematic Reverse Engineering of Biological Systems, Proceedings of the ASME International Design Engineering Technical Conferences, September 2007. Wood, KL; Jensen, D; Bezdek, J; Otto, KN. Reverse Engineering and Redesign: Courses to Incrementally and Systematically Teach Design, Journal of Engineering Education, 90(3), pp. 363-374, January 2013. Wu, C. Some Disassembly Required, Prism, ASEE, Washington, D.C., pp. 56-59, October 2008.

In: Mechanical Engineering Education … ISBN: 978-1-53617-791-6 Editor: Charles E. Baukal, Jr. © 2020 Nova Science Publishers, Inc.

Chapter 13

PREPARING MECHANICAL ENGINEERING STUDENTS FOR INDUSTRY Charles E. Baukal, Jr.1, Mark Vaccari2, Thomas DeAgostino3, Carter Stokeld4 and Courtney Baukal5 1

John Zink Hamworthy Combustion, Tulsa, OK, US Oklahoma State University, Tulsa, OK, US Oral Roberts University, Tulsa, OK, US University of Tulsa, Tulsa, OK, US 2 John Zink Hamworthy Combustion, Tulsa, OK, US University of Tulsa, Tulsa, OK, US 3 University of Kansas, Lawrence, KS, US General Motors, retired Detroit, MI, US 4 Williams, Tulsa, OK, US 5 Boeing, Oklahoma City, OK, US

INTRODUCTION There can often be a rude awakening for engineering students transitioning from academia to the work world. Teachers can help make

354 Charles E. Baukal, Jr., Mark Vaccari, Thomas DeAgostino et al. that transition smoother by preparing students for full time employment which is often significantly different than academia in some important ways. An important lesson learned from an extensive study of engineering education is the “imperative of teaching for professional practice” (Shepherd et al, 2009, p. 169). Van Treuren et al. (2017, p. 2) wrote, “As educators, the foremost goal is to graduate students technically prepared to fulfil their degree requirements. While they may be technically competent, certified by diploma, have we as educators prepared our students to meet the challenges in the workplace, whatever they may be?” They further wrote (p. 2), “the transition of our students into being a productive adult in this field, beyond technical competency, still lies with the faculty and institution.” Wisler (2003) of GE Aircraft Engines wrote, As young engineers progress in their careers, they begin to understand that there is far more to being an outstanding engineer than they might have thought during their days as an undergraduate. In fact, some of the things they need to know weren’t necessarily learned in school. And this is understandable, given the relatively short time spent in school and the significant differences between the missions of academe and industry/government.

He listed twelve vital aspects of engineering usually learned after graduation which have an important impact on an engineer’s career: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Learn to be business oriented Expect tough multi-disciplinary problems Learn to work and network in a new environment Understand the differences between academe and industry Learn to differentiate (people and their leadership) all over again Understand the values, code of conduct and culture of your particular company Be open to ideas from everywhere Have unyielding integrity Make your manager a success Support your university and your technical society Have fun Manage your career

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Items 2-4 and 7 are discussed to some extent in this chapter. Many students believe engineering practice will mirror what they did in school which is typically not the case. While they develop their problem-solving skills in school, they may have the impression that all industry problems have a single answer like their textbook problems. That is rarely the case for industrial problems of any significance. A skill that many students have not learned is to check their results to make sure they make sense. This is particularly true for exams where there is a time limit. Students often run out of time and usually cannot go back and check their answers. They do the best they can in the amount of time they have. Unfortunately, that does not work in industry as an incorrect answer is of no value. Students may be used to getting partial credit on exams for whatever work they did on a problem even if the answer is wrong. It is particularly embarrassing for new engineers to generate answers to industrial problems that make no sense. In some cases, such as having an efficiency greater than 100%, a nonsensical answer may be easy to identify. However, in some cases it may not be so easy to see an invalid answer. Students need to learn to consider the validity of their answers before presenting them to colleagues and supervisors. New graduates may still feel pressured to deliver answers quickly when they go into industry which can also lead to errors. While there are clearly schedules in industry, it is more important to get correct solutions because there is usually no partial credit for wrong answers. Another aspect many students may not be prepared for is working at least 40 hours a week within a defined working time window. A potential problem for some newer graduates is maintaining a good work-life balance. They may work many hours in an attempt to make a good impression on their boss and colleagues. Students may also not be prepared for the lack of quantitative feedback on their performance in industry compared to the constant feedback they get in academia. The switch from quantitative grades in school to more qualitative feedback in industry takes some getting used to. Grades are constantly received in school where feedback in industry could be as infrequent as once a year.

356 Charles E. Baukal, Jr., Mark Vaccari, Thomas DeAgostino et al. This chapter will look at some of the differences between the theory learned in school compared to actual practice, consider a range of topics related to problem solving, look at some key differences between the industrial work environment and academia, get perspectives from some newer engineering graduates and from instructors with ample industry experience, and finish with some recommendations.

THEORY VS. PRACTICE Some professors spend valuable classroom time deriving formulas that have already been derived in the textbooks. This is not a productive use of time because undergraduates will not be asked by their industrial supervisors to derive equations that have already been derived. Classroom time would be more productively spent showing examples of how important equations are used so students will be better prepared to use them should they ever need to when they are working in industry. Another requirement for some classes is the memorization of equations. This is not the most productive use of students’ time either as they will not be expected to memorize all the equations they will need on the job. Rather, they will have access to reference materials and will be expected to find and use appropriate equations for the problems they need to solve. Unlike textbook engineering problems that are normally well-defined and have a single correct answer, real world problems are often ill-defined, have numerous constraints, and may not have a single correct answer. These are often referred to as ill-structured problems which typically lack definition in some respect (Simon 1973). Those problems have no answer in the back of the book and may lack precise input data. Despite the importance of these real-world problems, Jonassen (2000, p. 63) wrote, “Unfortunately, students are rarely, if ever, required to solve meaningful problems as part of their curriculum.” Solving ill-defined problems needs to be taught to students so they will be properly prepared when they enter the workforce. While they are normally exposed to this type of problem in

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their capstone projects, they usually encounter very little of it in their core engineering classes. These ill-defined problems may also be solved using a variety of methods, to arrive at differing answers that still reinforce one another. Where possible, teachers should give some open-ended assignments where there may be multiple acceptable solutions (Baukal 2017). Students need to determine appropriate boundary conditions and material properties for these “fuzzy” problems. Students must then defend their own solutions as is typically required in industry. This teaches them that most “real” problems do not have a single answer and how to communicate their work to others such as their boss, colleagues, and customers. While school can simulate schedules and project management that are encountered in industry, some other aspects such as budget cannot be as easily simulated in school, except possibly in capstone projects. One type of problem that can be used to illustrate ill-defined problems is in the case where iterative calculations are required to determine a solution (Baukal 2016). These are often best handled by computer. Besides reducing the computational time compared to hand calculations, parameters can be dynamically changed in the problem to show immediate results with software. Multiple problem formulations can be easily displayed simultaneously. Using high-level commercial software such as Matlab® or Excel (or similar alternatives) to formulate and iteratively solve standard calculations in any given industry has a couple of advantages: 1. It helps students develop critical thinking and logic skills, and 2. The programming need only be done once to iteratively solve repeated problems and calculations, such as the sizing of standard industrial components.

This method also provides an easy and fast way to look at a range of possible scenarios.

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PROBLEM SOLVING The ABET (2018) requirements for the 2019-2020 accreditation cycle include Student Outcomes 1, 2, and 7 which are all related to problem solving: 1. an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics 2. an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors 3. an ability to acquire and apply new knowledge as needed, using appropriate learning strategies

The ASME Center for Education (2011) commissioned a study of mechanical engineering education. One of the findings was that only 14% of the industrial supervisors surveyed believed that problem solving and critical thinking skills were strengths of recent ME graduates. Singer et al. (2012, p. 75) wrote, “problem solving may be the quintessential expression of human thinking.” Problem solving is an important skill for professionals (Eraut 1994). Problem solving may be one of the most fundamental processes for engineers (Aldridge 1994). Sheppard et al. (2009, p. 3) wrote, “Engineering practice is, in its essence, problem solving.” Jonassen (2014, p. 103) wrote, “Learning to solve workplace problems is an essential learning outcome for any engineering graduate. Every engineer is hired, retained, and rewarded for his or her ability to solve problems.” One goal of engineering education is to produce effective problem solvers which is central to the practice of engineering (Kober, 2015). Roth and McGinn (1997, p. 18) wrote, “Educating students to become problem solvers has been a goal of education at least since Dewey.” Jonassen (2011, p. xvii) argued “the only legitimate cognitive goal of education (formal, informal, or other) in every educational context

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(public schools, university, and [especially] corporate training) is problem solving.” The goal of most educational experiences is to increase the expertise of the learner. It is unlikely however that a student will become a true expert after completing a single class on a given subject. It normally takes a considerable amount of time and experience working in the subject area to become an expert. The knowledge of experts and novices differs both quantitatively and qualitatively (Litzinger et al. 2011). Bransford et al. (1986) argued that merely accumulating information does not make someone an expert; rather it is a process of making domain specific knowledge relevant to problem solving strategies. Instructors can help students “conditionalize” their knowledge by teaching them strategies to organize and apply that knowledge. Engineering students normally learn how to become good problem solvers by the time they graduate. However, those students may not have learned how to assess the validity of their solutions. This is a key skill they need to learn, preferably before entering the workforce. Engineering educators can facilitate the process of validating solutions by teaching students to constantly assess their results to make sure they pass reality checks. Engineering students at graduation with an undergraduate degree are generally considered novices and do not become experts until they have had considerable experience. A key difference between experts and novices is how each approaches problem solving. While problem-solving skills are expected in industry, the expertise level expectation will vary with experience. New graduates are not expected to be able to solve the same problems that more experienced engineers can solve. Problem-solving capabilities are expected to improve with work experience.

Critical Thinking An important skill engineering students need to learn is critical thinking (Baukal 2015). Brookfield (2012) defined critical thinking as

360 Charles E. Baukal, Jr., Mark Vaccari, Thomas DeAgostino et al. identifying assumptions, testing their validity, seeing things from different viewpoints, and taking informed action. Here, the concern is testing assumptions about valid solutions. Halpern (1998, p. 450) defined critical thinking as “the deliberate use of skills and strategies that increase the probability of a desired outcome” which is specifically used in problem solving. In this case, the desired outcome is a correct solution. Halpern argued students can become better critical thinkers through appropriate instruction and that enhancing students’ critical thinking ability is both challenging and rewarding for instructors. Snyder and Snyder (1995, p. 90) wrote, “Students who are able to think critically are able to solve problems effectively.” Critical thinking is a key metacognitive activity that should desirably occur during learning and problem solving (Masui and De Corte 1999). Critical thinking and judgment are required for engineering professionals to solve workplace problems (Stevens et al. 2014). This includes the ability to critically evaluate solutions. Merely generating an answer does not guarantee the result is credible and appropriate. In most university engineering courses, problems typically have a single correct answer that a student’s answer can be compared against. Many real-world engineering problems are not that simple and often don’t have a single correct answer. Students must develop the ability to critically assess their solutions for credibility since they will not be able to compare the results of solving real-world problems against an answer in the back of a textbook.

Reality Checks There are multiple ways solutions can be assessed for validity including the sign of the result, the range of the result, the order of magnitude of the result, the number of significant digits, and measurement error. These are all ways of critically thinking and reflecting on the solution.

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Positive or Negative One important check of the reasonableness of an answer (Moore et al. 1979; Stephan et al. 2018) is its sign. This is a gross error checking process that can be particularly important in certain subjects such as thermodynamics where there is a specific sign convention. Typically, in thermodynamics, work done by a system is positive while work done on the system is negative and heat transfer into the system is positive while heat transfer out of the system is negative. This convention is used because normally the objective of a power cycle is to burn a fossil fuel (heat transfer into the system which is positive) to produce work out of the system (which is positive). Different devices in thermodynamics should produce results with a particular sign. For example, heat transfer for condensers is negative because they remove heat, heat transfer for evaporators is positive because they add heat, work for a compressor is negative because it is added to the system, and work for a turbine is positive because it is produced by the system. Students need to be familiar with the sign convention in a discipline so they can at least make sure their solution is the correct sign or orientation. In these types of problems, students should know even before starting the solution process what the sign of the answer should be. Instructors can reinforce this by discussing the sign of the solution before they begin solving these problems. After this has been demonstrated several times, the instructor can then ask students what the sign of the solution should be before the instructor begins to work out example problems. In some problems there may be multiple devices and intermediate solutions where the instructor can demonstrate this process of checking results for sign appropriateness. Correct Range Another error check is to determine if a solution falls within the correct range. Most calculations have results that should be within a certain range to be valid. While some results can be either positive or negative (e.g., work and heat in thermodynamics), other results can only be positive. For example, calculating a negative mass does not make physical sense. For

362 Charles E. Baukal, Jr., Mark Vaccari, Thomas DeAgostino et al. other types of results, the range is narrower than merely being positive. For example, thermal efficiency ranges between 0 and 100%. In thermodynamics, the quality of a fluid ranges between 0 (all liquid) and 1 (all vapor). Emissivity, absorptivity, transmissivity, and view factor in thermal radiation all must range between 0 and 1. Another example is finding the temperature inside a solid wall where the hot side is at one temperature (Th) and the cold side is at another temperature (Tc) where a temperature inside the wall must be between Th and Tc, assuming there is no heat generation or removal inside the wall. Calculated results outside these ranges for those variables, by definition, do not pass the reality check. The credible range for certain types of solutions needs to be learned so students know if their results are at least plausible. Again, this can be demonstrated by the instructor by discussing possible solutions before solving example problems and eventually asking students what are reasonable solutions before starting a problem. Final solutions can then be assessed to see if they fall within a valid range.

Order of Magnitude Determining the appropriate order of magnitude for a result is often the most challenging for a novice. This type of knowledge usually only comes with a considerable amount of experience. The instructor can help students get a feeling for what are “reasonable” answers and what are not. A simple example in fluid flow relates to the difference between laminar and turbulent flow. If the flow is high speed, such as near or above Mach one, the flow will generally be turbulent as determined by calculating the Reynolds number. A common mistake students often make is not using the correct value for viscosity. Often, this is a number in a table multiplied by a small number such as x 10-3. If the student forgets to multiply by that small number, they can get a much smaller Reynolds number since the calculation includes dividing by the viscosity. This produces a result that can be off by orders of magnitude. Instructors should sensitize students to this type of mistake, especially to typical errors instructors commonly see, so students can make sure their results at least pass the order of magnitude check.

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Significant Digits While most engineering students are taught early in the curriculum about significant digits, they still seem to struggle with this concept even through graduation. Because a calculator or computer can generate many digits does not mean they are all significant. In many cases, only three or four digits may be significant. Failure to recognize this either means students do not understand the concept of significant digits or are not disciplined enough to apply it. Some actually appear to believe the answer is more accurate if they include more significant digits. Significant digits is an important concept that needs to be ingrained before starting full-time employment. A supervisor who is accustomed to working with real data will view results with too many significant digits as a poor reflection on the employee and potentially on the employee’s alma-mater as well. Significant digits should be emphasized at the beginning of all engineering courses. To show the importance, points may be deducted when too many significant digits are reported. After attempting other ways to break students of reporting way too many digits, one instructor has decided on exams to deduct a point for each extra (non-significant) digit in a result. In some cases, a student may lose more points than a problem is worth and receive a negative score for the problem. This usually only needs to happen once for students to recognize the importance of significant digits. The appropriate number of significant digits is something the instructor can discuss even before attempting to solve problems. Based on the input data, students should be able to state how many significant digits are appropriate for the given problem. Rounding should not be done until the final solution to avoid round-off errors. When a result is computed, the instructor should specifically state how many digits should be used for the given problem. This reinforces the concept and indicates its importance. Measurement Uncertainty Students are normally taught about measurement uncertainty, but either fail to understand the concept or forget it when reporting results for experiments. Some seem to believe their measurements are much more

364 Charles E. Baukal, Jr., Mark Vaccari, Thomas DeAgostino et al. accurate than they actually are. In many cases, university lab equipment may be old, outdated, and out of calibration. Experiments conducted in industry may be done to generate performance data, demonstrate the feasibility of a new technology, determine operating limits, or demonstrate compliance with permits and standards. In undergraduate laboratories, none of those are normally the objective; the lab is usually done to demonstrate a phenomenon or concept. Therefore, high accuracy is not normally an important consideration in most undergraduate university labs given the added time and cost that are usually required to get high accuracy. Unfortunately, results in lab reports seem to indicate otherwise, as too many significant digits are often reported. Where possible, students should determine the estimated uncertainty in their measurements so they can be reported accordingly (Kline and McClintock 1953). Failure to do so can imply the results are much more accurate than they really are. Uncertainty bars on the results provide a dose of reality for students about the potential inaccuracy in measurements. They may also help explain why experimental results sometimes do not follow theoretical predictions. This exercise can be particularly valuable for those students who will be conducting experiments in industry. Not only will it give them an estimate of the accuracy of their measured results, but it can also indicate what specific measurements should be improved if they need higher accuracy (Holman 2012). For example, it may be relatively easy and inexpensive to improve a temperature measurement, but it could be that for a particular experiment it does little if anything to improve the overall accuracy of the result. Maybe the mass flow rate has a much bigger effect on the results and that measurement is what should be improved. This is the kind of information that managers are looking for from engineers when making decisions. An additional area where students tend to struggle is understanding the difference between error and uncertainty. Error refers to the difference in the measurement value and the value of the specific quantity subject to measurement (measurand). The measured value may be very close to the measurand while still having a large uncertainty. Students need to know

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the differences between the two and be able to articulate those when reporting results.

Documentation An important aspect of good engineering practice is proper documentation. This means showing calculations, listing sources for equations and data, enumerating assumptions, and discussing any simplifications or weaknesses of their analyses. Engineering students are taught early in their curriculum to work out units to demonstrate they are properly accounted for. Units on the left side of an equation need to equal those on the right side. Some equations require conversion factors that are obvious when the units are shown. Unfortunately, many students often neglect to show units and assume they will work themselves out. This is poor practice as it can lead to mistakes that may be difficult to find if the units are not shown. Teachers should make it a point to show units when they work problems and require their students to do the same. Appropriate point deductions should be made when students fail to show units, even if they get the correct answer. Losing points seems to be the only motivator for some students to work out units in their calculations.

INDUSTRIAL WORK ENVIRONMENT Many students are used to staying up late at night and waking up late in the morning. Unfortunately, that schedule is usually counter to most industrial workplaces where employees are generally expected to start before 9 am. They are also expected to work at least 40 hours a week. While that part is not usually a problem for engineering students who probably work that many hours between attending classes, doing homework, writing lab reports, and studying for exams, they may not be used to the expected regularity of the workplace.

366 Charles E. Baukal, Jr., Mark Vaccari, Thomas DeAgostino et al. Another aspect of the workplace that some students may not be prepared for is the importance of deadlines being met. Some students seem to believe that deadlines are just a target and are not that firm. Even when teachers impose a late penalty, that may not deter some students. However, that attitude will not serve them well in industry where they need to assume deadlines should be met unless they have negotiated something to the contrary with their management. Instructors can help enforce that belief by imposing stiff late penalties or by even rejecting late assignments. Another difference between academia and industry is that there are generally no grading curves in the latter. Supervisors expect solutions to be correct, where 80% correct may be considered good in academia but unacceptable in industry. Considerable amounts of money may be spent on designs that must be correct. Additionally, many projects on which engineers work deal with the safety of the public. Here, the bar for what is “correct” is even higher. Students must have the mindset that their solutions cannot be just good enough but that they must be correct. They may learn this principle while working on their capstone projects, but it would be better if they learn it much earlier in the curriculum. A related difference is that sometimes only an estimate is needed where a more accurate solution may take a considerable amount more time and work. Estimates are often used in developing budgets both for internal purposes as well as for clients. Experienced engineers can usually generate estimates fairly quickly because of the intuition they have developed. One way that professors might be able to help students develop estimation capability is to ask students before beginning to solve problems what the solution may look like. This will give them a basis to determine if the actual solution is reasonable, but also get them used to estimating. University students get feedback on a regular basis from their professors. They receive grades for quizzes, homework, lab reports, exams, and many other types of assignments. However, in industry there are few if any “graded” assignments. They will rarely get regular quantitative feedback from their supervisors. Most employers require at least annual performance reviews. Some employers have more frequent reviews for new employees to make sure they do not get too far off track. While it may

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not be reasonable to expect teachers to change their grading patterns, they could warn students not to expect such frequent feedback from industrial supervisors. Written and verbal communication skills are very important in most industrial engineering positions. While engineering students need to communicate in many of their classes, they will most likely be doing even more communicating on the job and less calculating than they did in school. This is especially the case in some industries where many of the calculations are computerized. Where appropriate, instructors should provide their students with opportunities to communicate their results and to give them critical feedback to help prepare them for industry. An important skill new graduates must quickly learn is to write documents based on the intended audience. For example, upper level management is unlikely to read long detailed reports. Those reports should have a brief executive summary which may be the only thing upper management reads. Key points must be clearly made so they will not be lost in the minute details. Recommendations must also be clearly identified if the author expects any action to be taken as a result of the project. One of the previous ABET Student Outcomes that is not part of the 2019-2020 criteria was related to students’ commitment to lifelong learning. While this was often difficult to measure, it showed the importance of this attitude, particularly for engineers as technology changes rapidly. A revelation for many students is that their learning has only just started – their degree has prepared them to learn the specifics of their job that cannot be taught in school because there would be far too many subjects, many of which are constantly changing. Some students have the mistaken belief that their learning is complete when they earn their degree, when in reality they need to continue learning throughout their entire career. An important aspect of most industrial work environments is working in teams. ABET (2018) Student Outcome 5 is “an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives.” Instructors should look for opportunities for group

368 Charles E. Baukal, Jr., Mark Vaccari, Thomas DeAgostino et al. projects so students will become more familiar with working on teams. An example is a summative heat transfer project to design a house and size the heating and air conditioning units based on solar radiation, forced convection (wind), natural convection, and conduction through a multicomponent exterior wall including parallel and serial elements (Baukal 2017). Another important element of teams in industry is that employees rarely get to decide what teams they will work on. Teams are normally selected by management. At least some student teams should be determined by instructors rather than letting students always select their friends to be on their school project teams.

NEWER GRADUATE PERSPECTIVE An aspect new engineers will have to accept as they graduate from college is that in the professional world they will not have all the answers, nor are they expected to. In the classroom setting, students pass or fail on their own merit. Even in group projects, professors watch how much each member contributes. While it is true that team members in the professional world are expected to pull their own weight, the engineer is not expected to encompass the whole team. For example, there is a natural relationship between the engineer and the technician: engineers usually deal with the scope of the project and “why” it needs to be done, while technicians typically deal with the more specified details regarding the “how” and “where.” The ability to find balance within the team between different skill sets is a quintessential step for the progression of the engineer. Along these same lines, there are rare moments in the college experience where the engineering student learns to bring their designs to life or even pass their designs on to someone else to complete. Most classes do not require the engineer to fabricate their own designs or have their designs fabricated at all. Therefore, there is no practice appreciating the work that must be done, the relative timeline to completion, and the amount of money required to complete the project. The back-and-forth communication regarding specifications, questions, and timetables are a

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critical and normal part of the project life cycle that most new graduates have no knowledge of or practice in prior to starting full time employment. Another reality that newly hired engineers must face is that they will be an expense before they become an asset. They probably do not bring much inherent value at first because they have not been trained in the company’s specific skill set. The engineer must first acknowledge this and respond appropriately. The appropriate response is different than in academia, which usually constitutes of digging into a book and reading the required chapters. The response in the professional world includes getting to know the project’s requirements and specifications, familiarizing themselves with the teams of people who collaborate together to take the product from conception to market, understanding the needs of the technicians as they enact the project, getting a grasp on the environment of this new workspace, and so on. A detrimental side-effect of not acknowledging this toxic yet enticing notion is the false belief that the “engineer knows best” solely because of their degree. The harsh reality is that the degree does not equal inherent value addition, but rather that the engineer is qualified to be responsible for the project once adequately trained. This thought process can unfortunately have professional ramifications within the teams of those who have been working on these projects for years prior to the addition of the newly graduated engineer. Only after sufficiently partaking in a company-sponsored onboarding process can the engineer begin creating value for company. The new graduate will have growing pains as well when it comes to finding resources to complete specified projects. In college, the number of resources is limited to notes, books, and faculty. In the professional world there are a multitude of resources needed to complete a project, with varying amounts dependent on the complexity of the project. The new graduate must learn to adhere to industry standards, federal regulations, corporate policy, environmental and safety regulations, and contractual obligations, among others for their baseline design parameters. Then once these have been addressed, they must navigate through less rigid specifications such as best practices, personal preferences, return on investment, innovative techniques, weather conditions, scope, schedule,

370 Charles E. Baukal, Jr., Mark Vaccari, Thomas DeAgostino et al. budget, work permits, management of change, and other legal requirements. All of this can be addressed if the engineer has a supportive team at the corporation, but this is not taught as an expectation in the classroom. In addition to that, many of these parameters can change midproject, thus increasing project complexity for the engineer. Where possible, schools should incorporate nebulous design specifications with evolving expectations to bring the early support the new graduate will need to learn to adapt to the professional world. New graduates may need to learn to set their own goals since their teachers have typically set the goals at least in their classes. New graduates may need to learn how to network to successfully move up. That was not likely a skill they needed in school where there was only one position, student, and who they knew did not impact their grades. Volunteering for extracurricular work activities at work may provide enhanced visibility and increased contacts that could influence future promotions. Industrial supervisors often assign mentors to new graduates. Those mentors may provide more feedback to the new graduate than their actual supervisor. Also, a new graduate’s colleagues, especially those in their group or on their project teams may be a good source of feedback. New graduates should seek those out wherever possible so they can find out how they are doing. Because of the nature of academia where time is broken up into semesters, students are rarely exposed to long term projects except possibly for their capstones. Depending on the company and industry, engineers may work on much longer projects in the workplace. Those longer-term projects also often include large project teams. Goals and team members may change over the course of a project. In college, it is a lot of short-term projects so many new graduates are not prepared to work on the same project for an extended amount of time. Undergraduates may get some exposure to this when they work with professors on their research projects. A successful engineer in industry requires more than just good problem-solving abilities; they must also possess good judgment. There are many other factors such as cost and delivery that students usually did not

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need to be concerned with in school, except possibly for their capstone project. They also need to be able to prioritize many competing objectives. For example, it is often possible to speed up delivery on equipment but at some cost. Is that additional cost justified or not? That depends on the criticality of the delivery of that equipment. Another aspect of industry that may be challenging for new graduates is working with potentially much older and more experienced engineers. In school, most students are at approximately the same age in a given course so there are not usually any significant age barriers to consider. However, in industry they may work on teams with engineers having a wide range of experience. New graduates need to show appropriate respect for their more experienced colleagues without being intimidated as they have projects to complete. Depending on what industry a new graduate enters, they may work with standards. Students get very little exposure to these standards while in school. Their main exposure to them is often in their capstone project. More exposure to at least some of the major standards in a given discipline, such as the ASME standards for mechanical engineers, would better prepare students for using these standards. An important ABET (2018) requirement is solving complex engineering problems that include considering current standards and codes.

INDUSTRY INSTRUCTOR PERSPECTIVE There are many ways to use engineers from industry to benefit university engineering programs. They can teach courses where they have specific expertise that may not be available within the faculty (Baukal et al. 2010). Industry can help provide new course content, for example for emerging technologies, which can be taught by academia (KorhonenYrjänheikki 2007). Adjunct instructors from industry can temporarily replace faculty on sabbatical or on leave (Gosink & Streveler 2000; Baukal et al. 2010), handle temporary increases in student course enrollments (Rose & Voigt 2008), relieve full-time faculty so they can do research

372 Charles E. Baukal, Jr., Mark Vaccari, Thomas DeAgostino et al. (Varma 2009), or co-teach with full-time faculty to help bring professional practice into the classroom (Akili 2005). Adjunct instructors can also teach specific topics in a course where faculty are less knowledgeable (Dunn 2009; McManus 2007), teach entire courses outside the specific area of expertise of the faculty (Gunnerson et al. 2002; Rose & Voigt 2008), and teach courses at off-campus locations (Farr & Verma 2002). Industry can partner with universities to provide guest speakers to tell students about various aspects of the “real world” of engineering (Massie 2004). Many schools have a seminar series where different guest speakers from industry present each week to give students a broader view of various engineering disciplines. Companies can host field trips where universities visit local industrial facilities to see actual equipment in operation (Fournier & Gaudet 1999). Cooperative positions and internships allow students to work side-by-side with engineering professionals to see how what they have learned in class is applied in practice (Dabipi & Arumala 2007; Keil & Basantis 2000). Industry can sponsor senior design projects to produce some type of low stakes product or process of interest to them while simultaneously educating students by allowing them to apply their knowledge and skills to an actual problem (Keil & Basantis 2000). Some universities have used industry to help teach senior design courses as part of capstone projects (Knox et al. 1995); these adjuncts are often referred to as “Professors of Practice.” Industry can sponsor research projects with faculty that also include student workers. Industry can also provide facilities for students to conduct research if these are not available at the university. Industry can provide formal mentors for university students and participate in supervisory thesis committees for graduate students (Gunnerson et al. 2002), including sponsoring industrial theses that are carried out in industry (Massie 2004). Industrial instructors can also help introduce students to the dynamics of the industrial workplace that are typically not taught in the classroom. These include how design equations are selected, how much measurement/calculation accuracy is required, secrets of career progression, and the existence of workplace politics.

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Not every design equation or model used in industry is derived from first principles. Many are empirical and are only valid over a relatively narrow range of operating conditions. Many students, especially graduate students, struggle switching from a world where all equations are derived from first principles to a world where equations are purely empirical (and are often simple polynomials). Equation forms are selected purely for their quality of fit of the experimental data. Phenomena that are being modeled in industry are often large, complicated, and coupled systems that do not lend themselves to analytical derived equations. Students (especially graduate students) typically try to make every measurement as accurately as possible. In industrial applications, measurements with national lab accuracy levels are often not required. The models developed from these data are often proprietary and will not be shared with the public, so data are typically collected only for the regimes of interest, with little attempt to develop comprehensive correlations. Students (both in high school as well as in college) are continually told that being in leadership positions (in clubs, student government, etc.) is imperative and extremely important to getting ahead in their careers. In reality, the vast majority of people in industry are in individual contributor positions. Leadership positions in industry are relatively few and far between. When leadership positions do become available, the most qualified person is not always chosen. Sometimes, favoritism by more senior leadership is what drives the promotion decision. A study by the McDonough School of Business at Georgetown University showed that 92% of senior business executives say that favoritism occurs in large organizations (Gardner 2011). The study further shows that 96% of these executives who had pre-chosen an employee for promotion, ended up promoting their chosen employee. Additionally, almost a third of the executives admitted to only considering one candidate (their pre-chosen employee) for promotion. This suggests that new graduates desiring to progress in their career should ingratiate themselves with their supervisors. Even if promotion is not a new graduate’s goal, ingratiating oneself to his/her supervisor has benefits. Annual performance reviews serve not only as an opportunity for feedback from one’s supervisor, but they are also

374 Charles E. Baukal, Jr., Mark Vaccari, Thomas DeAgostino et al. typically used in determining compensation adjustments for the coming year. Utilizing “impression management” on one’s supervisor has proven to have a significant impact on one’s performance review (Wayne & Liden 1995). An anecdotal observation is that many students have the mistaken belief that they will become a vice president within a few years after graduation. That is highly unlikely to happen as it often takes many years to reach such a level of management including going through many intermediate levels first. Those same students with these unrealistic expectations may not actually want that position if they knew exactly what vice presidents do. Professors can help set more realistic student expectations by discussing the long and arduous path in academia to reach higher levels such as dean and provost which is comparable to paths to reach upper management in industry. These are some of the many ways that academia can work with industry to provide opportunities for students to learn more about engineering work after graduation. The key element is for both academia and industry to seek out engagement. Both industry and academia should ensure that the engagement has sufficient depth of purpose and meaning to allow for student learning.

RECOMMENDATIONS Internships, co-ops, and industry-sponsored capstone projects are invaluable for preparing engineering students to become full-time working engineers. They help to show students that theory is not always realized in actual practice and that problems in industry are often open-ended. Students should be encouraged to work in and with industry wherever possible. There are many ways that professors can help prepare their students for life after graduation. Unfortunately, a challenge is that most engineering faculty have little if any industrial experience (Stevens et al, 2014, p. 119). A report by the ASME Center for Education (2011) recommends an

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increase in faculty expertise in professional practice such as through sabbaticals in industry. The first suggestion for faculty is setting appropriate expectations and then sticking to them. This means setting deadlines for assignments and then assessing significant penalties for late submissions up to not accepting them at all. While students can often be very creative in justifying why they did not complete an assignment on time, the “dog ate my homework” excuse does not work in most industrial settings. Academicians can make sure their students properly document their assumptions, where their data came from, what equations they used to solve the problems, that units are consistently worked out, and that an appropriate number of significant digits are presented in the final solution. Many engineering professors spend valuable classroom time deriving equations. This is a waste of time for undergraduates who will never be asked by their industry supervisors to derive equations that someone else has already derived. However, they will be expected to know how to use those equations. Professors should spend as much time as they can working problems and using important equations so students will become more familiar with them and will be able to use them when needed. A general problem that new graduates may encounter in industry is the significant differences in generations. Most supervisors will be from a later generation that was raised differently than today’s graduates. For example, everyone did not receive approbation for participation in previous generations and the newest graduates are unlikely to get those from their industry supervisors. In fact, they may get relatively little tangible feedback like they are used to getting in academia where they get graded exams and assignments throughout the semester from multiple courses. They will get feedback but not in the form of a grade. They will also be judged on perceptions rather than solely on specific grades which they are used to getting. An introverted engineering student can often do very well in college classes with relatively little interaction with their fellow students and teachers. However, an introverted engineer may have problems on the job as they are expected to interact daily with all types of people, not only

376 Charles E. Baukal, Jr., Mark Vaccari, Thomas DeAgostino et al. other engineers. Communication, both written and verbal, is a critical skill that engineering students need to learn by the time they graduate. The purpose of this chapter is not to change academia into industry as there are clear and distinct purposes for both. However, engineering faculty can better prepare their students to enter the work world which has some significant differences compared to academia. The objective should be to develop successful engineers who will be ready when they enter the workforce. This will create a positive impression of the school that could have long term dividends such as possible internships, capstone projects, and industry involvement in the classroom.

REFERENCES ABET, Criteria for Accrediting Engineering Programs: Effective for Reviews During the 2019-2020 Accreditation Cycle Incorporates all changes approved by the ABET Board of Delegates Engineering Area Delegation as of November 2, 2018, retrieved May 18, 2019, https://www.abet.org/wp-content/uploads/2018/11/E001-19-20-EACCriteria-11-24-18.pdf. Akili, W., Integrating practical experience in a geotechnical/foundation engineering class: The role of the adjunct faculty, proceedings of the 2005 American Society for Engineering Education (ASEE) Annual Conference & Exposition, pp. 8423-8436. Aldridge, M.D. Professional Practice: A Topic for Engineering Research and Instruction, Journal of Engineering Education, 83(3), 231-236, 1994. ASME (American Society for Mechanical Engineers), Vision 2030: Creating the Future of Mechanical Engineering Education, Phase 1 – Final Report, Center for Education, New York: ASME, 2011. Baukal, C. “Promoting Critical Reflection During Problem Solving: Assessing Solution Credibility,” presented at the American Society for Engineering Education (ASEE) Zone III Meeting 2015, Springfield, MO, September 24, 2015. Baukal, C. Using Worked Examples in Equation Solver Software to Enhance Students’ Contextual Problem Solving, presented at the

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American Society for Engineering Education (ASEE) Midwest Section Conference, Manhattan, Kansas, September 25-27, 2016. Baukal, C. Summative Heat Transfer Project: Designing a House, presented at 2017 American Society for Engineering Education (ASEE) Midwest Section Conference, Stillwater, OK, September 2017. Baukal, C., J. Colannino, W. Bussman, and J. Matsson, IndustryUniversity Partnership Case Study, presented at the American Society for Engineering Education (ASEE) Midwest Conf., September 22-24, 2010, Lawrence, KS.Baukal, C., J. Colannino, W. Bussman, and G. Price, Industry Instructors for a Specialized Elective Course, Paper AC 2010-67, proceedings of 2010 American Society for Engineering Education (ASEE) conference, June 20-23, 2010, Louisville, KY. Bransford, J.R., Sherwood, N. Vye, and J. Rieser, Teaching Thinking and Problem Solving, American Psychologist, 41(10), 1078-1089, 1986. Brookfield, S.D. Teaching for Critical Thinking, Jossey-Bass, San Francisco, 2012. Dabipi, I. and J. Arumala, Enhancing engineering education through reallife projects, proceedings of the 2007 American Society for Engineering Education (ASEE) Annual Conference & Exposition, paper AC 2007-3031. Dunn, P., Creating industrial partnerships with construction-management technology programs, proceedings of the 2009 American Society for Engineering Education (ASEE) Annual Conference & Exposition, paper AC 2009-1114. Eraut, M. Developing Professional Knowledge and Competence, Falmer Press, London, 1994. Farr, J.V. and D. Verma, Involving industry in the design of courses, programs, and a systems engineering and engineering management department, proceedings of the 2002 American Society for Engineering Education (ASEE) Annual Conference & Exposition, pp. 495-502. Fournier, D.J. and C. Gaudet, Creating relationships with industry to advance new programs, proceedings of the 1999 American Society for Engineering Education (ASEE) Annual Conference & Exposition, pp. 1405-1410. Gardner, J., Executive Masters in Leadership Capstone Project, unpublished, Georgetown University, Washington, DC, 2011.

378 Charles E. Baukal, Jr., Mark Vaccari, Thomas DeAgostino et al. Gosink, J.P., and R.A. Streveler, Bringing adjunct engineering faculty into the learning community, J. Engineering Education, 89(1), 47-51, 2000. Gunnerson, F.S., R.T. Jacobsen and G. Pillay, A strategic alliance between regional universities and industry at a national laboratory, proceedings of the 2002 American Society for Engineering Education (ASEE) Annual Conference & Exposition, pp. 3895-3903. Halpern, D.F. Critical Thinking for Transfer Across Domains, American Psychologist, Vol. 53, No. 4, pp. 449-455, 1998. Holman, J.P., Experimental Methods for Engineers, 8th ed., McGraw-Hill, New York, 2012. Jonassen, D.H. Toward a Design Theory of Problem Solving, Educational Technology Research and Development, 48(4), 63-85, 2000. Jonassen, D.H. Learning to Solve Problems: A Handbook for Designing Problem-Solving Learning Environments, Routledge, New York, 2011. Jonassen, D.H. Engineers as Problem Solvers, Chapter 6 in Cambridge Handbook of Engineering Education Research, edited by A. Johri and B.M. Olds, Cambridge University Press, New York, 2014. Keil, Z.O. and M. Basantis, An industrial internship program to enhance student learning and marketability, proceedings of the 2000 American Society for Engineering Education (ASEE) Annual Conference & Exposition, pp. 845-850. Kline, S.J., and F.A. McClintock, Describing Uncertainties in SingleSample Experiments, Mechanical Engineering, 75(1), 3-8, 1953. Knox, R.C., D.A. Sabatini, R.L. Sack, R.D. Haskins, and S.W. Fairbairn, A practitioner-educator partnership for teaching engineering design, J. Engineering Education, 84(1), 1-7, 1995. Kober, Linda, Reaching Students: What Research Says About Effective Instruction in Undergraduate Science and Engineering, Washington, DC: National Academies Press, 2015. Korhonen-Yrjänheikki, K., T. Tukiainen, and M. Takala, New challenging approaches to engineering education: Enhancing university-industry co-operation, European J. Engineering Education, 32(2), 167-179, 2007. Litzinger, T.A. L.R. Lattuca, R.G. Hadgraft, and W.C. Newstetter, Engineering Education and the Development of Expertise, Journal of Engineering Education, 100(1), 123-150, 2011.

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Massie, W.W., Bringing practitioners (and practice) into the curriculum, proceedings of the 2004 American Society for Engineering Education (ASEE) Annual Conference & Exposition, pp. 1303-1311. Masui, C. and E. De Corte, Enhancing learning and problem solving skills: orienting and self-judging, two powerful and trainable learning tools, Learning and Instruction, 9(6), 517-542, 1999. McManus, K., The effects of integration of industry faculty into a construction management postgraduate coursework program in the Australian environment, proceedings of the 2007 American Society for Engineering Education (ASEE) Annual Conference & Exposition, paper AC 2007-1562. Moore, R.F., S.C. Tyne, S.L. Norman, C.M. Hamielec, D.C. Ross, et al., Developing Style in Problem Solving, Engineering Education, 69(7), 713-717, 760, 1979. Rose, A. and N. Voigt, The role of adjunct faculty in future engineering, proceedings of the 2008 American Society for Engineering Education (ASEE) Annual Conference & Exposition, paper AC 2008-2046. Roth, W-M and M.K. McGinn, Toward a New Perspective on Problem Solving, Canadian Journal of Education, 22(1), 18-32, 1997. Sheppard, S.D., K. Macatangay, A. Colby, and W.M. Sullivan, Educating Engineers: Designing for the Future of the Field, Jossey-Bass, San Francisco, 2009. Simon, H.A. The Structure of Ill Structured Problems, Artificial Intelligence, 4(3-4), 181-201, 1973. Singer, Susan, Natalie Nielsen, and Heidi Schweingruber (eds.), Discipline-Based Education Research: Understanding and Improving Learning in Undergraduate Science and Engineering, Washington, DC: National Academies Press, 2012. Snyder, L.G. and M.J. Snyder, Teaching Critical Thinking and Problem Solving Skills, Delta Pi Epsilon Journal, L(2), 90-99, 1995. Stephan, Elizabeth, David Bowman, William Park, Benjamin Sill, and Matthew Ohland, Thinking Like An Engineer: An Active Learning Approach, New York: Pearson, 2018. Stevens, R., A. Johri, and K. O’Connor, Professional Engineering Work, Chapter 7 in Cambridge Handbook of Engineering Education Research, edited by A. Johri and B.M. Olds, Cambridge University Press, New York, 2014.

380 Charles E. Baukal, Jr., Mark Vaccari, Thomas DeAgostino et al. Van Treuren, K.W., C.C. Fry, W.M. Jordan, and J.E. Miller, Helping Engineering and Computer Science Students Find Joy in Their Work, 2017 ASEE Annual Conference & Exposition, Paper ID #19158, Columbus, OH, June 24, 2017. Varma, V., Practitioners as adjunct clinical professors: Their role in teaching real-world engineering applications in design and construction, proceedings of the 2009 American Society for Engineering Education (ASEE) Annual Conference & Exposition, paper AC 2009-304. Wayne, S. and R. Liden, Effects of Impression Management on Performance Ratings: A Longitudinal Study, Academy of Management Journal, 38(1), 232-260, 1995. Wisler, D.C., Engineering – What You Don’t Necessarily Learn in School, Proceedings of the ASME Turbo Expo 2003, Power for Land, Sea, and Air, June 16-19, 2003, Atlanta, GA.

In: Mechanical Engineering Education … ISBN: 978-1-53617-791-6 Editor: Charles E. Baukal, Jr. © 2020 Nova Science Publishers, Inc.

Chapter 14

FUTURE RESEARCH AREAS Charles E. Baukal, Jr. John Zink Hamworthy Combustion (Tulsa, OK) Oklahoma State University (Tulsa, OK) Oral Roberts University (Tulsa, OK) University of Tulsa (Tulsa, OK)

Keywords: mechanical engineering education research

INTRODUCTION There continue to be calls for improving engineering education. The U.S. National Academy of Engineering (2004) established a Committee on Engineering Education to answer the question “What will or should engineering be like in 2020?” The Phase 2 report (National Academy of Engineering, 2005) from that committee titled Educating the Engineer of 2020 calls for the reinvention of engineering education. An important recommendation of that study was the importance of academic management such as engineering deans endorsing engineering education. This includes studying how undergraduate engineering students learn to

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determine how to better serve students with different learning styles and what pedagogical approaches excite them. The report notes that while progress has been made in determining best practices for engineering education, much remains to be done. The Journal of Engineering Education (Anonymous 2006) recommends five research areas for engineering education: engineering epistemologies, engineering learning mechanisms, engineering learning systems, engineering diversity and inclusiveness, and engineering assessment. Duderstad (2008) recommends (p. v) “a systematic, research-based approach to innovation and continuous improvement of engineering education.” The U.S. National Academy of Engineering (2008) identified 14 grand challenges in engineering. One of those challenges is to advance personalized learning that recognizes individual preferences and aptitudes to help motivate learners to become more self-directed. While that challenge was targeted at the development of learning software by computer engineers, it applies to all types of learning and learners, including engineering students. Johri and Olds (2014) have edited a substantial book on engineering education research which is divided into six parts and contains 36 chapters. In that book, Male and Baillie (2014) argue that teaching practices should be research-based with proven improvements in student learning. Another chapter by Atman et al., (2014) recommends that research into engineering design specifically should be based on solid research. Fortenberry et al., (2007, p. 1175) argue the ultimate aims of engineering education research should include:     

the creation of education programs that attract more, and more diverse, students to the study of engineering, retain more of the students who are enrolled, deepen students’ understanding of engineering concepts, broaden students’ appreciation of engineering’s role in meeting the needs of a global society, and better prepare students for further study or professional practice.

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A task force commissioned by the American Society of Mechanical Engineers or ASME (2011) calls for reform of the mechanical engineering curriculum. They describe the education challenge to produce engineering professionals (p. 6), “In addition to being skilled in working collaboratively and in virtual design teams, mechanical engineering practitioners need innovation skills that encompass practical understanding of how things are designed, produced and supported in a global marketplace.” Flumerfelt, Kahlen, Alves, and Siriban-Manalang (2015) argue that while most agree reform is needed in the mechanical engineering (ME) curriculum, little has been done to make this happen. As discussed in Chapter 1 and in Baukal and Ausburn (2014), research has shown that mechanical engineering students have much different learning strategy and verbal-visual preferences than the general population. ME students prefer more problem solving and more visual materials. The research-based learning preferences of ME students suggests that ME education should be designed differently than traditional education which is often highly verbal and less visual with little problem solving. Further research in this area is discussed below. There are many other topics related to ME education that would benefit from further research. Two of the major topics include how can individual courses and curricula be improved to enhance learning. This chapter focuses on potential future mechanical engineering education research topics, although many of them will also apply to other engineering disciplines. It is not intended to be exhaustive or even comprehensive. It is clearly influenced by the author’s experiences and biases. It is intended to present a number of topics that in the opinion of the author could benefit from disciplined research study.

CURRICULUM It is an ongoing challenge to determine which courses to include in the ME curriculum and which to either exclude or to offer as electives. Technology changes rapidly which necessitates constant evaluation of

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what courses should be included. This is particularly challenging for smaller ME programs that have a limited number of faculty with specific areas of expertise. The broader the range of courses offered, the more difficult it can be to have appropriate instructors to teach the content. One area of possible research is to look into alternative methods of delivering some courses such as with adjuncts or through online courses. In the former case, adjuncts from industry with particular expertise could teach courses that may benefit their employers by providing access to students getting training in technologies of interest to the company. In the latter case, a network of universities could potentially offer a much wider range of courses online than each school individually could. This should be researched to determine optimal methods of providing a wider range of courses, effective ways of delivering the content, and how such courses impact learning. Duderstadt (2008, p. iii) wrote, “The changing workforce and technology needs of a global knowledge economy are dramatically changing the nature of engineering practice, demanding far broader skills than simply the mastery of scientific and technological disciplines.” Maintaining the right balance of theory and practice in any engineering education curriculum can be very challenging because of the rapid advancements in technology. A survey of engineering managers shows that industry prefers engineering graduates trained in the latest technologies (ASME, 2011). A dichotomy is that engineering professors are often at the forefront of technology in their areas of specialty, while many undergraduate engineering programs lag behind more recent technological developments due to the costs of updating (e.g., equipment for labs) and the problem of deciding what topics should be removed to make room for the new technologies. There can be a lot of inertia in academia to change the culture of learning which means a significant and ongoing effort is needed by educators to constantly evaluate their courses and money needs to be available to get the necessary equipment for new technologies. An example of a newer technology with exciting potential is 3D metal printing, also known as additive manufacturing. At the time of this writing, the equipment is expensive, the size of the parts that can be made is

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limited, the range of materials that can be reasonably printed is limited, and the speed is generally too slow for mass production. However, that will change in the future as the equipment becomes faster, has the capability of producing larger parts, and can print fast enough to be competitive with traditional manufacturing processes. Ideally, industry can supply some or all of that new 3D printing equipment which can potentially benefit their future sales to those students after graduation and also better prepare graduates to work in industries using that new technology. Another example is the use of certain types of software for manufacturing. Anecdotal evidence suggests that most schools are not teaching students how to use that software despite the fact that industry needs graduates with that training. While new graduates lack the overall experience of engineers who have been working for some time, that may not be as much of a disadvantage if they have knowledge of advanced technologies that more experienced engineers may not have. Research could explore effective partnership arrangements that could mutually benefit industry and academia for educating ME students in the latest technologies. In a review article, Francis Kulacki (1996) shows that there was a significant change in the mechanical engineering curriculum between 1953-55 and 1987-89 where there was an increased emphasis on humanities, social sciences, and technical electives with a significant reduction in laboratory work. The ASME (2011) Vision 2030 report notes the most significant change in the mechanical engineering curriculum since the 1970s has been the inclusion of more technical course content and more emphasis on design. Possibly more than any other engineering discipline, design is a key element of mechanical engineering. While research on design in the engineering curriculum, more research is needed, particularly for mechanical engineering which is one of the more designheavy engineering disciplines. ABET (2020) does not proscribe exactly what courses should be taught, only the minimum number of credits in math/science and engineering courses. The problem with re-engineering the curriculum is that it is already full, with relatively little flexibility to make changes unless a more innovative approach is taken. If that were done, then a

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program would have to convince ABET program evaluators who are very familiar with “typical” curricula that they should approve a curriculum that could be significantly different than what they have seen. Research is recommended to evaluate more innovative curricula to find the most effective blends of courses for training ME students. While change is often slow, that should not discourage engineering faculty and administration from trying new things to continuously improve. ABET could help encourage change by advocating less traditional curricula that are proven to enhance learning and preparation for ME students. One of the advantages and challenges of mechanical engineering is that it is very broad-based and includes a wide range of jobs in a wide range of industries. The curriculum must adequately prepare ME students to be able to work in numerous possible positions. To address that conundrum, many mechanical engineering programs cover multiple content areas such as mechanics, thermo-fluids, and controls where students are required to take a minimum number of credits in each content area. The ASME (2011) Vision 2030 report recommends more curriculum flexibility for students to focus on areas of interest to them. A potential research area is to investigate letting students choose which area(s) they want to specialize in and allowing them to take more courses in that area or those areas and fewer if any in the other areas. This assumes the students know in advance which area(s) they want to work in which is clearly not always the case. Introductory courses in the topic areas could help students choose which one(s) they prefer. Of course, students would not be precluded from taking courses in all the topic areas, but at least they would be given the choice. Unfortunately, most ME curricula have very few courses where students can select what they want to take because there are so many courses deemed to be mandatory by faculty and administration. Research is recommended to find out the appropriate blend of mandatory and elective courses. Another area of potentially fruitful research is for practice vs. theory. Industry recommends more practice-based education (ASME, 2011) to produce graduates more ready to work. While ME students get exposure to practice in their capstone projects, they often get little if any in most of

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their other ME courses. A likely reason for that is that engineering faculty have less industry experience than in the past (Graham, 2012). This balance between practice and theory is discussed in some detail in Chapter 13. Research could be done to find out what types of practice-based instruction are the most effective for preparing ME students for industry. That is consistent with the ASME (2011) Vision 2030 report which recommends more professional practice experience and expertise for engineering faculty so they can teach more about professional practice to their students.

MECHANICAL ENGINEERING STUDENTS’ LEARNING PREFERENCES One of the major goals of discipline-based education research (DBER) related to engineering education is to “understand how people learn the concepts, practices, and ways of thinking of science and engineering” (Singer et al., 2012). A National Academy of Engineering report (2005) argues the importance of pursuing student-centered education and to find out how students learn as well as what they learn. As noted in Chapter 1 of this book, mechanical engineering students have statistically significantly different learning preferences compared to the general population. Their learning strategy preference has a much higher proportion of problem solvers and their verbal-visual preference is much more highly visual. There are many other areas that could be researched related to those preferences. Are the preferences of U.S. students significantly different than for international students? Are there any differences for ME students compared to other engineering disciplines, which could be important for freshmen and sophomore level classes that often have students from multiple disciplines? If there were significant differences, would they justify separating classes by discipline? More research into how ME students learn could provide useful information on how to more effectively teach those students to enhance learning.

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IMPROVED COURSE CONTENT One of the nine grand engineering education challenges given by Cary Sneider (2016) is developing new course materials. Instructors may need to constantly update course content for those areas with rapidly changing technologies such as in the general area of materials. Chapter 11 suggests incorporating materials in a range of ME courses. Updating course content is more likely to impact higher-level courses compared to lower-level courses which are based on well-established theories and principles that do not typically change. However, even in those courses, research should continue on finding the most effective ways to teach the materials. For example, Chapter 10 in this book suggests a better way of teaching steam tables used in thermodynamics. The traditional method has been shown to be problematic for many students. A current trend for many of the foundational courses such as thermodynamics is to use online homework problems now offered by publishers. Research is recommended to determine the effectiveness of this approach. Having personally used this technique, I have seen students get frustrated when the online system mistakenly marks their problems wrong when their answers are correct and match those in the system. While these computer mistakes are correctable, they still cause unnecessary stress for students, particularly when they wait till the last minute to complete assignments. Research could determine if there are more effective methods for online homework. Another potential future research area related to course content is the use of multimedia. It has already been shown in Chapter 1 that ME students are statistically significantly more visual than the average person. Further, younger people tend to prefer more visual content on their phones. However, there is relatively course content that uses, for example, students’ cell phones such as apps for MEs. Research is recommended to explore the use of more visual content and cell phones for ME education.

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COMMUNICATION Communication is a core professional skill for engineers (Bordogna, 1997; Paretti et al., 2014). ABET (2020) student outcome number three is “an ability to communicate effectively with a range of audiences.” A diversity of methods has been used to study this topic, but Paretti et al., (p. 609) argue there is a need for “methodological diversity and crossdisciplinary collaboration.” As expected, communication is an important skill for mechanical engineers (ASME, 2011). The scope and complexity of problems continues to increase which means even more communication is needed to ensure all issues are considered and addressed. Unfortunately, a survey of companies hiring mechanical engineers showed that new hires have weak oral and written communication skills. Current methods of instruction related to communication do not appear to be effective enough. An important challenge for instruction on communication in engineering education is that most of the instructors have not worked as engineers in industry and therefore have little if any direct knowledge of how engineers communicate with their colleagues, management, clients, vendors, and government. This problem is further exacerbated by the fact that many of the required English composition-type courses taken by engineering students are taught by professors of English who are not even trained in engineering. This author has personal experience of mechanical engineering students trained in English composition, rather than in engineering composition. For example, engineering students are taught to write out “degrees Fahrenheit” rather than using normal engineering practice of using simply “°F.” Those students need to relearn how to write as engineers, not as English majors. Either English professors teaching engineering students need to learn engineering composition, or engineering professors need to teach the English composition courses. Research is needed in this area to produce engineering graduates better prepared to communicate as professional engineers.

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ASSESSMENT Learning assessment is generally an inexact science. While exam scores may seem to indicate a quantitative measure of a student’s knowledge, that assumes a test is representative of the content and a test only measures a student’s knowledge at a given point in time. Some students are better at taking exams than others so a test score may not indicate the true learning for poor test-takers. For many students, they forget much of what they have learned shortly after taking an exam and shortly after finishing a course. This is confirmed by professors teaching a course that has a prerequisite when much of the information needed from that prerequisite has already been forgotten. Clearly improvement is needed in assessing ME students’ knowledge and skills. More innovation is recommended in the area of assessment. A particular challenge is assessing capstone projects which often vary widely. Some student teams may have gotten a “good” project that includes realworld design with a company that provides effective mentors. Other student teams may not have gotten a “good” project where their projects may include little if any real design and where there is little feedback from company mentors. This is often through no fault of their own as they may have been assigned their particular project. In many cases, there may not be enough “good” projects to go around. How then should teams with less than desirable projects be properly judged, compared to other teams with strong and realistic projects? A potential area of research could be on how to effectively assess a wide range of capstone projects to ensure fairness but also to provide valuable feedback to the students. Another area of assessment that could benefit from research is innovation and entrepreneurship (this topic is discussed in more detail below but not from an assessment perspective). The challenge with both innovation and entrepreneurship is that they are much more subjective than other courses such as statics and dynamics that typically have problems with single correct answers. How does one properly assess these subjects? Research might investigate effective methods for assessment in more subjective subjects such as innovation and entrepreneurship.

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VIRTUAL REALITY Two important areas of emerging engineering education research are spatial ability and the use of representations, although relatively little research in these areas has been done to date (Singer et al., 2012). Kober (2015, p. 4) wrote, “Animations, interactive computer simulations, virtual models, and other technology-based representations are widely used in the practice of science and engineering and are becoming increasingly popular in undergraduate education. This is a potential area of future research.

Figure 1. Multimedia Cone of Abstraction.

The ASME (2011) Vision 2030 report recommends increased use of virtual reality in engineering education. VR is an effective tool for communicating complex concepts, particularly those with a significant visual component (Baukal et al., 2019). Mechanical engineering students in particular could benefit from the increased use of VR. In the Multimedia Cone of Abstraction (Baukal et al., 2013) as shown in Figure 1, VR is considered to be one of the most concrete forms of multimedia for instructional purposes, which is particularly important for students who normally do not have much knowledge in a given subject which of course is why they are taking the course in the first place. However, VR itself is

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still relatively new at the time of this writing and much more research needs to be done to find out the most effective ways to use it in ME education. The cost of the equipment has come down dramatically to the point that it is very feasible to use in the classroom. Software to develop VR is still developing but should also become very feasible for students and faculty to use.

CODES AND STANDARDS One of the ABET (2020) requirements for curriculum (Criterion 5) is “a culminating major engineering design experience that 1) incorporates appropriate engineering standards and multiple constraints, and 2) is based on the knowledge and skills acquired in earlier course work.” The ASME (2011) Vision 2030 report found that employers noted the general lack of knowledge of codes and standards as a weakness of mechanical engineering graduates. There are many potential codes and standards that might be used by mechanical engineers, so the purpose of incorporating codes and standards into the ME curriculum would not to exhaustively look at every one that an ME might use, but rather to expose students to the major ones so they will get some familiarity with how they work and where to find them. One area of research might be to determine which codes and standards would be best to include in the ME curriculum. Another possible area of research could be how to best incorporate them into the curriculum, including which courses would be best to include them. This research should include feedback from industry to find out which codes and standards are most relevant to them.

INNOVATION/ENTREPRENEURSHIP Bordogna (1997) recommends that the engineering education curriculum should be designed to develop innovation capability in

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students. The National Academy of Engineering report (2004) The Engineer of 2020 recommends that creativity be introduced early in the engineering education curriculum. Tryggvason and Apelian (2006) note that the engineer of the 21st century will be entrepreneurial and will (with some exaggeration) know everything, will be able to do anything, will be able to work with anybody anywhere, and will be able to make imagination into reality. A report for the Royal Academy of Engineering (2007) identifies creativity and innovation as important attributes for engineering graduates. The National Science Board (2007) notes the importance that engineering is a key component of innovation in a technological society. Its keystone recommendation (p. 4) for improving engineering education is “The National Science Foundation should expand and reinvigorate its efforts to stimulate and disseminate innovation in engineering education.” Duderstadt (2008, p. iv) argues that technological innovation including the transformation of knowledge into products, processes, and services is critical for U.S.-trained engineers to be competitive in a global economy. He argues that both innovation and entrepreneurship are needed in the engineering education curriculum. Mechanical engineering underpinned the innovation leading to the Industrial Revolution and has been an important discipline in the subsequent waves of innovation (Marjoram, 2010). Kulacki (1996) argues there is a need to include innovation in the ME curriculum. A recommendation of the ASME (2011) Vision 2030 report is that mechanical engineering curricula should inspire innovation and creativity. Despite the numerous calls to add innovation (creativity) and entrepreneurship to the curriculum, relatively little has been done to accomplish that. Research is needed to determine the most effective ways to do this. It is suggested this start early in the curriculum to help increase interest, motivation, and retention. Innovation and entrepreneurship should be included throughout the curriculum. In many curricula today, the only significant exposure many students have to these topics is in their capstone

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project. While that useful, it is too late if that is the only significant exposure students have before graduation.

MULTIMEDIA Manjit Sidhu (2010) argues there is a need for developing multimedia software for engineering. Chapter 1 in this book shows that ME students are statistically much more visual than the average person which means they prefer more multimedia such as drawings, pictures, videos, animations, and virtual reality (discussed above). Many ME courses could benefit from the use of more multimedia. One area of research would be to find out what types of multimedia are best to teach each particular type of content. For example, animations should be useful in teaching a course like dynamics which has objects that are moving. On the other hand, drawings should be appropriate for a subject like statics where the objects are stationary. It is possible that different types of multimedia should be used at different times during a course. For example, showing students a complicated VR simulation at the beginning of the semester when their subject matter knowledge is relatively low may not be as effective as showing them static graphics such as pictures and drawings first until the student has sufficient knowledge to effectively learn from dynamic graphics such as videos, animations, and VR. Research could also consider methods for developing multimedia. For example, more knowledgeable students (such as those who have already completed a given course) might be a possible source for generating multimedia for educational purposes. Students in a course might also be a source since they are closest to the content and may know best what kind of information they would have liked to have had while they were learning a particular subject. Students may also have more knowledge and expertise to develop multimedia than faculty. Students and faculty could work together to generate multimedia to enhance engineering education.

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ONLINE LABORATORIES A National Academy of Engineering report (2005) recommends more research into the use of web-mediated learning to determine its effectiveness for teaching engineering content. Something that continues to grow in popularity is the increase in online laboratories. While it might be argued that ME students in particular need to have at least some hands-on experiments to develop experience and skills with using actual equipment, there is a role for remote experimentation. With the growth of online degrees, online labs may be the only way for remote students to get laboratory experience. Even face-to-face students could benefit from some online labs. There are at least two different types of online labs: remote manipulation of actual equipment and completely virtual. In the former, students can remotely access equipment in a laboratory to run an experiment, usually under the control of a lab assistant present in the lab. This remote operation of equipment has been done for many years in industry and would provide valuable and relevant knowledge and skills for the engineering graduate to make them more valuable to industry. The latter type of lab could provide the students with opportunities to experiment with situations they may not be able to in a physical lab. For example, a student would probably not be able to test load conditions on a large structure such as a bridge because it would be much too expensive and possibly dangerous if an actual bridge failed under a student-imposed load. However, structural loading of a virtual bridge would be possible to simulate without the inherent cost and danger of doing so on a real bridge. A possible significant advantage of a virtual lab is that it can be shared among universities at a much more reasonable cost compared to building physical laboratories that must be designed, built, and maintained with the proper equipment. As technology changes, it is also easier to upgrade a virtual lab compared to a remote physical lab as no new equipment would need to be bought in the former case. Research is recommended to determine effective design and implementation of online labs to find those that are most effective for learning. Further research could explore how

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universities could collaborate to develop virtual labs. Industry should also be involved in the process to recommend labs that would be most beneficial to them and make ME graduates more valuable to industry.

CONCLUSION AND RECOMMENDATIONS Duderstadt (2008) argues, “the goal of American engineering schools and industry training programs should be to focus more on quality, producing engineers capable of adding exceptional value through innovation, entrepreneurial skills, and global competence.” Many of the recommended areas for future research above attempt to address that recommendation. As has been well documented in many of the reports cited here, changing the engineering curriculum continues to be a slow and difficult process for many reasons. However, that should not be an argument for abandoning efforts to enhance engineering education. Given all of the demands on today’s engineering professors, any recommended changes must take into consideration how they can be done with as little interruption as possible to the faculty. Preferably, they should make the faculty’s life easier if at all possible. For example, the advent of online homework by many textbook publishers can reduce the amount of tedious work done by the faculty, particularly those at smaller schools who do not usually have teaching assistants to help with grading. Another example could be online laboratories that could have assessment built into them. This would give students immediate feedback when the lab is freshest in their minds and save the faculty from grading labs. The challenge of engineering education research is translating research results into effective practice in the classroom (Singer et al., 2012). National surveys reviewed by Singer et al., have shown that engineering faculty are among the least likely to employ student-centered or collaborative instructional techniques. That may be surprising to some since engineering faculty, at least those involved in research, are often involved in cutting-edge technology. While there are many possible reasons for that state of affairs, the situation must change to better prepare

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engineering graduates for industry and possible graduate school. The purpose of this chapter was not to offer solutions to that problem, although some solutions have been suggested in certain areas. Rather, this chapter has suggested a wide range of engineering education subjects in general and ME education in particular that would benefit from more research. The discipline of engineering education continues to expand rapidly which bodes well for the future, assuming that faculty and administration implement research-based results that are demonstrated to improve student learning. Accreditation standards and proper incentives are two methods of driving change into the curriculum. Those are some other general subjects that could benefit from further research.

REFERENCES ABET, Criteria for Accrediting Engineering Programs, 2020-2021, https://www.abet.org/accreditation/accreditation-criteria/criteria-foraccrediting-engineering-programs-2020-2021/, accessed January 20, 2020. Anonymous, “The Research Agenda for the New Discipline of Engineering Education, Journal of Engineering Education, 95(4), 259261, 2006. ASME (American Society for Mechanical Engineers), Vision 2030: Creating the Future of Mechanical Engineering Education, Phase 1 – Final Report, Center for Education, New York: ASME, 2011. Atman, Cynthia, Ozgur Eris, Janet McDonnell, Monica Cardella, and Jim Borgford-Parnell, “Engineering Design Education,” in Aditya Johri and Barbara Olds (eds.), Cambridge Handbook of Engineering Education Research, New York: Cambridge University Press, 2014. Baukal, C. E. and L. J. Ausburn, Learning Strategy and Verbal-Visual Preferences for Mechanical Engineering Students, 2014 American Society for Engineering Education Annual Conference & Exposition, paper 8682, Indianapolis, IN, June 15-18, 2014.

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Baukal, C., F. Ausburn, and L. J. Ausburn, “A Proposed Multimedia Cone of Abstraction: Updating a Class Instructional Design Theory,” Journal of Educational Technology, 9(4), 15-24, 2013. Baukal, Charles, Bjorn Olson, and Richard Ernst, “Virtual Reality for Continuing Professional Development,” paper 26721 proceedings of the American Society for Engineering Education (ASEE) Annual Conference and Exhibition, Tampa, FL, June 2019. Bordogna, Joseph, “Making Connections: The Role of Engineers and Engineering Education,” The Bridge, 27(1), Spring 1997; https://www.nae.edu/7498/MakingConnectionsTheRoleofEngineersan dEngineeringEducation, accessed January 24, 2020. Duderstadt, J. J. (2008). Engineering for a changing world: A roadmap to the future of engineering practice, research, and education. Ann Arbor, Michigan: The Millennium Project, The University of Michigan. Retrieved from: http://milproj.dc.umich.edu/. Flumerfelt, Shannon, Franz-Josef Kahlen, Anabela Alves, and Anna Bell Siriban-Manalang, Lean Engineering Education: Driving Content and Competency Mastery, New York: ASME Press, 2015. Fortenberry, Norman, Jacquelyn Sullivan, Peter Jordan, and Daniel Knight, “Engineering Education Research Aids Instruction,” Science, 317, 1175-1176, August 31, 2007. Graham, Ruth, Achieving Excellence in Engineering Education: The Ingredients of Successful Change, London: Royal Academy of Engineering, 2012. Johri, Aditya and Barbara Olds (eds.), Cambridge Handbook of Engineering Education Research, New York: Cambridge University Press, 2014. Kober, Linda, Reaching Students: What Research Says About Effective Instruction in Undergraduate Science and Engineering, Washington, DC: National Academies Press, 2015. Kulacki, Francis, “The Education of Mechanical Engineers for the 21st Century,” Japanese Society of Mechanical Engineers International Journal, Series A, 39(4), 467-478, 1996.

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Male, Sally and Caroline Baillie, “Research-Guided Teaching Practices,” in Aditya Johri and Barbara Olds (eds.), Cambridge Handbook of Engineering Education Research, New York: Cambridge University Press, 2014. Marjoram, Tony, “4.2.2 Mechanical engineering,” in United Nations Educational, Scientific and Cultural Organization (UNESCO), Engineering: Issues, Challenges and Opportunities for Development, Paris: UNESCO Publishing, 2010. National Academy of Engineering, The Engineer of 2020: Visions of Engineering in the New Century, Washington, DC: National Academies Press, 2004. National Academy of Engineering, Educating the Engineer of 2020: Adapting Engineering Education to the New Century, Washington, DC: National Academies Press, 2005. National Academy of Engineering, Grand Challenges for Engineering, Washington, DC: National Academies Press, 2008. National Science Board, Moving Forward to Improve Engineering Education, Washington, DC: National Science Foundation report NSB-07-122, November 19, 2007. Paretti, Marie, Lisa McNair, and Jon Leydens, “Engineering Communication,” in Aditya Johri and Barbara Olds (eds.), Cambridge Handbook of Engineering Education Research, New York: Cambridge University Press, 2014. Royal Academy of Engineering, Educating Engineers for the 21st Century, London: Royal Academy of Engineering, June 2007. Sidhu, Manjit, Technology-Assisted Problem Solving for Engineering Education: Interactive Multimedia Applications, Hershey: Engineering Science Reference, 2010. Singer, Susan, Natalie Nielsen, and Heidi Schweingruber (eds.), Discipline-Based Education Research: Understanding and Improving Learning in Undergraduate Science and Engineering, Washington, DC: National Academies Press, 2012. Sneider, Cary, “Grand Challenges for Engineering Education,” in Leonard Annetta and James Minogue (eds.), Connecting Science and

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Engineering Education Practices in Meaningful Ways, Heidelberg: Springer, 2016. Tryggvason, Gretar and Diran Apelian, “Re-Engineering Engineering Education for the Challenges of the 21st Century, Journal of the Minerals, Metals and Materials Society, 58(10), 14-17, 2006.

ABOUT THE EDITOR Charles E. Baukal, Jr. Director, John Zink Institute Adjunct, Oral Roberts University Adjunct, University of Tulsa, Adjunct, Oklahoma State University Email Address: [email protected] Charles E. Baukal, Jr. is the Director of the John Zink Institute which is part of John Zink Hamworthy Combustion (Tulsa, OK) where he has been since 1998. Prior to that he worked for Air Products and Chemicals (Allentown, PA) for 13 years. His areas of expertise include industrial combustion, heat transfer, air pollution, and engineering education. He has been teaching as an adjunct instructor since 1984 and is currently an adjunct at Oral Roberts University, the University of Tulsa, and Oklahoma State University, all in Tulsa, OK. He has a Ph.D. in Mechanical Engineering and an Ed.D. in Applied Educational Studies. He is a licensed Professional Engineer in the state of Pennsylvania, has authored approximately 250 publications including 16 books, and is an inventor on 11 U.S. patents. He is a member of many organizations and honorary societies and serves on many advisory boards.

LIST OF CONTRIBUTORS Sara Atwood Elizabethtown College, Elizabethtown PA, US Email Address: [email protected] Sara A. Atwood is Associate Professor and Chair of the Engineering and Physics Department at Elizabethtown College (Elizabethtown, Pennsylvania) where she has been since 2010. She has been teaching in engineering higher education since 2007 in the areas of mechanical and biomedical engineering as well as engineering design and design thinking. She has a PhD. in Mechanical Engineering from the University of California at Berkeley (Berkeley, California). She has over 50 publications/presentations in the fields of engineering education, biomaterials, and biomedical device design. She has received approximately $1 million in NSF funding for engineering education grants focusing on women in engineering, first-generation low-income students’ professional engineering identity and internship experiences, and outcomes-based pedagogies. She is a member of the Society of Women Engineers, the American Society for Mechanical Engineers, and the American Society for Engineering Education.

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Diana Bairaktarova Virginia Polytechnic Institute and State University, Blacksburg, VA, USA Email Address: [email protected] Diana Bairaktarova is an Assistant Professor in the Department of Engineering Education at Virginia Tech (VT), an Affiliate faculty in the Department of Mechanical Engineering and faculty in the Human-centered Design Program also at VT. She earned her BS and MS in Mechanical Engineering from Technical University of Sofia, Bulgaria, an MBA from Hamline School of Business in St. Paul Minnesota and PhD degree in Engineering Education from Purdue University. She has over fifteen years of experience working as a Design and Manufacturing Engineer. Before joining Virginia Tech, Diana started her academic career as a Professor of Practice in the School of Aerospace and Mechanical Engineering at University of Oklahoma. Her current research projects investigate how mechanical aptitude and spatial ability, interest, and manipulation of physical and virtual objects influence learning and performance in engineering. By providing applications of real-world engineering tasks in the exploration of new designs that stimulate creativity and visual reasoning, Dr. Bairaktarova aims to prepare her students with innovative thinking, helping them to face rapidly changing technologies.

Smitesh Bakrania Mechanical Engineering, Rowan University Glassboro, NJ, US Email Address: [email protected] Smitesh Bakrania is an associate professor in Mechanical Engineering at Rowan University. He received his Ph.D. from University of Michigan in 2008 and his B.S. from Union College in 2003. His research interests include combustion synthesis of nanoparticles and combustion catalysis using nanoparticles. He is also involved in developing educational apps and tools for instructional and research purposes. He has over 60 publications with journals, book chapters, and conference publications

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combined. Dr. Bakrania actively participates in the ASEE and FIE conferences. He has a keen interest in advancing engineering education pedagogy that engages a diverse student population. He received the 2018 Fulbright Scholar award to visit New Zealand and explore pathways to enrich engineering education. He teaches Thermal-Fluid Sciences and Nanotechnology courses and continues to build resources that support these topics. He is a member of the American Society for Mechanical Engineers, and the American Society for Engineering Education.

Kimberly Bigelow Department of Mechanical and Aerospace Engineering, University of Dayton, Dayton, OH, US Email Address: [email protected] Kimberly E. Bigelow is an associate professor in the Department of Mechanical and Aerospace Engineering at the University of Dayton (Dayton, Ohio) where she has been since 2009. She previously worked as a research engineer at Bertec Corporation (Worthington, Ohio). She has been working since 1999 in the general area of clinical biomechanics. She has a Ph.D. in Mechanical Engineering from Ohio State University (Columbus, Ohio). She has approximately 100 publications/presentations. She has received funding from National Institutes of Health and National Science Foundation for her work in assistive device design and has participated in the Oak Ridge Institute for Science and Education Faculty Research Participation Program with the US Air Force Research Laboratory. She is a member of the American Society of Biomechanics, the American Society for Mechanical Engineers, and the American Society of Engineering Education. She recently completed a three-year term as Education Chair on the American Society of Biomechanics’s Executive Board and currently serves as an Associate Editor for the Journal of Applied Biomechanics.

Courtney Baukal Boeing, Oklahoma City, OK, US Email Address: [email protected] Courtney E. Baukal has been a Structural Analysis Engineer on the KC-135 Stratotanker at Boeing in Oklahoma City since graduating from the University of Oklahoma in 2018. She has her Engineering Intern

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certification and is currently pursuing her M.S. in Engineering and Technology Management at Oklahoma State University. She has coauthored several publications. She is a chair for Boeing’s OKC Society of Women Engineers and Boeing’s young professional group, REACH. She also volunteers at the Boys and Girls Club of Oklahoma County (BGCOKC) where she teaches physics in the Drone Class and is on the Junior Committee for the BGCOKC as well.

Matthew Cavalli Western Michigan University, Kalamazoo, Michigan, US Email Address: [email protected] Matthew N. Cavalli is a Professor of Mechanical Engineering and Associate Dean for Undergraduate Academic Affairs in the College of Engineering and Applied Sciences at Western Michigan University in Kalamazoo, MI, where he has worked since 2018. Previously, he has served as Chair of the Mechanical Engineering Department at the University of North Dakota and Associate Dean in UND’s College of Engineering and Mines. He has been a faculty member and engineering administrator in various capacities since 2003. He completed his Ph.D. in Mechanical Engineering from the University of Michigan (Ann Arbor, Michigan). He is a licensed Professional Engineer in the states of North Dakota and Michigan. He has over 25 peer-reviewed publications on both technical topics (primarily solid mechanics and materials science) and engineering education. He authored a chapter on strength of unidirectional lamina in Comprehensive Composite Materials II. He has received over $2M in funding for his research activities. He is a member of the National Society of Professional Engineers, the American Society of Mechanical Engineers, and the American Society for Engineering Education. He is a member of several honor societies including Tau Beta Pi, Pi Tau Sigma, and Phi Kappa Phi.

Thomas DeAgostino University of Kansas, Lawrence, KS, US Email Address: [email protected] Thomas DeAgostino is the P.J. and Barbara Adam Associate Professor of the Practice at the University of Kansas Mechanical Engineering Department (Lawrence, Kansas) where he has administered the Capstone program since 2015. Prior to holding several other academic

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positions at various institutions, he worked for over 25 years in the automotive industry at both General Motors and Ford Motor Co. He holds an M.S. in Engineering Science from Rensselaer Polytechnic Institute (Troy, New York). He has authored or co-authored several book chapters, and multiple conference papers for ASEE and ASME. He has been twice awarded Outstanding Mechanical Engineering Faculty at the University of Kansas, and is also the recipient of the John J. and Mary Gelet McKetta Excellence in Teaching Award in Engineering from Trine University. He is a member of Pi Tau Sigma, the American Society for Engineering Education, the American Society of Mechanical Engineers, and the Society of Manufacturing Engineers.

Kurt DeGoede Elizabethtown College, Elizabethtown, PA, US Email Address: [email protected] Kurt M. DeGoede is Professor of Engineering and Physics at Elizabethtown College (Elizabethtown, PA) where he has been on the faculty since 2000. Between degrees he worked at Ford Motor Company (Dearborn, MI), and has worked professionally as a mechanical engineer since 1993 in project management, biomechanics, and education. He has a Ph.D. in mechanical engineering from the University of Michigan (Ann Arbor). He has authored or coauthored 9 journal papers and 31 peer reviewed conference papers. He received the Rackham Distinguished Dissertation award (University of Michigan, 2000) and the Kreider Prize for Teaching Excellence at Elizabethtown College (2018). He is a member of the Sigma Pi Sigma (Physics) and Phi Beta Kappa (Liberal Arts) honor societies, and the American Society of Mechanical Engineering, the American Society for Engineering Education, and the American Society of Biomechanics.

Kimberly Demoret Assistant Professor of Aerospace Engineering, Florida Institute of Technology, Melbourne, FL, US Email Address: [email protected] Kimberly B. Demoret has been a professor at Florida Tech (Melbourne, Florida) since 2015, and she focuses on freshman and senior design classes in aerospace engineering. Prior to joining Florida Tech, she worked for eight years at Kennedy Space Center on development of launch

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systems in support of NASA's space exploration goals. She also spent 20 years in the Air Force as a developmental engineer and manager, earning her Ph.D. in Mechanical Engineering at the Air Force Institute of Technology in 1994. She is a retired Lieutenant Colonel and a licensed professional engineer in the State of Florida. Her current research interests include engineering education and the psychology of student teams, and she has authored 12 papers and technical reports on various topics. She is a member of American Institute for Aeronautics and Astronautics and the American Society for Engineering Education.

Jessica Fitzgerald Oral Roberts University, Tulsa, OK, US Email Address: [email protected] Jessica E. Fitzgerald is a Bioengineering Ph.D. candidate at Northeastern University. She began her graduate degree studies in Fall of 2014 and will graduate with her doctorate degree in May of 2020. She is currently a research assistant in the Biomedical Optics group at Northeastern, working specifically on optical sensing platforms for disease monitoring and detection. Jessica has taught several semesters as a course instructor and teaching assistant, and has enjoyed assisting students with scientific communication as a Northeastern University Communications Lab fellow. Jessica has a B.S. degree in Engineering Physics from Oral Roberts University. Jessica currently has 6 first-author journal articles with 2 more in progress as of February 2020, with approximately 20 other publications and presentations to date. These include authoring 2 book chapters in the Springer Methods in Molecular Biology special issue on Biomimetic Sensing for which she also served as co-editor. Jessica is the recipient of the Distinguished Dean’s Fellowship at Northeastern University, and is a member of the Biomedical Engineering Society.

Andrew Gerhart Lawrence Technological University, Southfield, MI US Email Address: [email protected] Andrew L. Gerhart is a Professor of Mechanical Engineering at Lawrence Technological University (Southfield, MI) where he has been since 2002. His primary focus areas include fluid mechanics, thermodynamics, heat transfer, active and collaborative learning, and engineering education. He has a Ph.D. in Mechanical Engineering from the

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University of New Mexico (Albuquerque, NM). He has over 80 publications including authoring/editing 8 books in fluid mechanics, power generation, and engineering professional development. In addition, he has developed and has facilitated over 80 workshops worldwide, having trained approximately 1500 faculty members in active, collaborative, and problem-based learning, as well as training professional engineers and students in creative problem solving and innovation. He has served on the leadership team for many grants totaling over $3.8M. Dr. Gerhart was awarded the 2010 Michigan Professor of the Year (by the Carnegie Foundation for the Advancement of Teaching and the Council for Advancement and Support of Education), Lawrence Tech’s highest teaching and faculty awards, and two leadership awards from the Engineering Society of Detroit (ESD). He was elected to ESD’s College of Fellows, and is actively involved with The American Society of Mechanical Engineers serving on the Performance Test Code Committee for Air-cooled Condensers. He is a member of the American Society for Engineering Education and has received four best paper awards for their Annual Conferences.

Surojit Gupta University of North Dakota, Grand Forks, North Dakota, US Email Address: [email protected] Surojit Gupta is an Associate Professor of Mechanical Engineering in the University of North Dakota. He has published over 50 technical papers, and 6 patents (granted and pending – US and International). In addition, he has given over 20 invited/keynote talks and over 70 contributed presentations in several international, national and local conferences. Dr. Gupta also has an h-index of 25. Previously, Dr. Gupta was employed at The Rutgers University. Prior to that, he was a postdoctoral fellow at The Pennsylvania State University. Earlier, Dr. Gupta finished his doctoral studies from Drexel University and an MBA from the University of Massachusetts, Amherst. Dr. Gupta has won several awards like Du Co Ceramics Award, Chatwal Memorial Award, TMS Young Professional Development Award, Dean Teaching Professorship, Dean’s Award for the Best Faculty (2016), Global Young Investigator (ECD, ACerS, 2016), ASM/IIM Lectureship (2016) etc. Dr. Gupta is also a passionate educator and has supervised several graduate theses. Currently, Dr. Gupta is serving as the Chair of the Engineering Ceramics Division of the American Ceramics Society.

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Dominic Halsmer Oral Roberts University, Tulsa, OK, US Email Address: [email protected] Dominic M. Halsmer is a Professor of Engineering in the College of Science and Engineering at Oral Roberts University (Tulsa, Oklahoma) where he has been since 1992. He previously worked for 5 years at Hughes Aircraft Company, Space and Communications Group (El Segundo, California). He has been working since 1981 in the general areas of aerospace and mechanical engineering, reverse engineering and engineering education. He has a Ph.D. in Mechanical Engineering from the University of California at Los Angeles (Los Angeles, California). He is a licensed Professional Engineer in the State of Oklahoma. He has approximately 500 publications/presentations including 4 books or book chapters in the fields reverse engineering and engineering education. He is a member of the American Society for Mechanical Engineers, and the American Society for Engineering Education.

Matthew Jensen Assistant Professor of Mechanical Engineering, Utah Valley University, Orem, UT, US Email Address: [email protected] Matthew J. Jensen is an assistant professor of Mechanical Engineering at Utah Valley University in Orem Utah where he started in the Fall of 2019. He previously held the same position at Florida Institute of Technology as part of his 9 years as a mechanical engineering faculty member. He has a Ph.D. in Mechanical Engineering from Clemson University and earned his Bachelor of Science degree in Mechanical Engineering from Rose-Hulman Institute of Technology. He currently has 22 refereed publications including 9 journal and 12 conference proceedings. He received the Ferdinand P. Beer and E. Russell Johnson Jr., Outstanding New Mechanics Educator Award in 2015 from the Mechanics Division of the American Society of Engineering Education (ASEE). His research includes funding of over $600,000 from various industry and government agencies. He is a member of the American Society of Mechanical Engineers (ASME), ASEE, and Society of Automotive Engineering (SAE), as well as a member of Kappa Theta Epsilon National Co-Op Honor Society and Phi Beta Delta Honor Society for International Scholars.

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John Krohn Arkansas Tech University, Russellville, Arkansas, USA Email Address: [email protected] John L. Krohn is a Professor of Mechanical Engineering at Arkansas Tech University (Russellville, Arkansas) where he has been on faculty since 1991, serving as Department Head from 1995 to 2011. He previously worked for 8 years at the Texas A&M Nuclear Science Center (College Station, Texas). He has been working since 1983 in engineering education concentrating in thermal and energy systems. He is currently serving as Professor and Interim Department Head at Arkansas Tech and continues to teach in the thermal and energy systems areas. He has a Ph.D. in Nuclear Engineering from Texas A&M University (College Station, Texas). He is a licensed Professional Engineer in the State of Arkansas. He has approximately 12 publications and numerous professional and public presentations on energy topics. He has been PI or co-PI on grants totaling over $150,000. He is a member of the American Nuclear Society, American Society of Mechanical Engineers, and the American Society for Engineering Education where he serves as the Secretary/Treasurer for the Midwest Section. He is a member of several honor societies and serves as an officer or board member for a number of local non-profit entities.

Dustin McNally University of North Dakota, Grand Forks, North Dakota, US Email Address: [email protected] Dustin P. McNally is a Senior Lecturer in the Mechanical Engineering department at the University of North Dakota (Grand Forks, ND), where he has been since 2013. He has been working in areas of alternative energy, hydrogen production and safety, and high temperature materials since 2006, and has taught an introductory mechanical engineering course since 2007. He has been the Faculty advisor of the student chapter of American Society of Mechanical Engineers at the University North Dakota since 2013, as well as advisor of a competition team of students in the American Society of Mechanical Engineers’ annual student design competition, who have competed at the international level three times since 2013. He has received certification in Hydrogen Safety Engineering from the University of Ulster and the International Association of Hydrogen Safety. He is also an engineer in training in the state of North Dakota, a member of American Society of Mechanical

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List of Contributors

Engineers, and serves as paper reviewer for American Society of Engineering Education.

James Mynderse Lawrence Technological University Email Address: [email protected] James A. Mynderse is an Associate Professor in the A. Leon Linton Department of Mechanical, Robotics and Industrial Engineering at Lawrence Technological University (Southfield, Michigan) where he has worked since 2012. He serves as director for the B.S. in Robotics Engineering and M.S. in Mechatronic Systems Engineering programs and faculty advisor for the LTU Baja SAE team. He has a Ph.D. in Mechanical Engineering from Purdue University (West Lafayette, Indiana). He has over 60 publications/presentations/workshops. He is a member of the American Society for Mechanical Engineers, the American Society for Engineering Education, and SAE.

P. Wesley Odom Oral Roberts University, Tulsa, OK, US Email Address: [email protected]

Krishna Pakala Boise State University, Boise, ID, US Email Address: [email protected] Krishna Pakala is an Assistant Professor in the Department of Mechanical and Biomedical Engineering at Boise State University (Boise, Idaho) where he has been since 2012. He is the Faculty in Residence for the Engineering and Innovation Living Learning Community and the Faculty Associate for Accessibility and Universal Design for Learning. He is also the Director for the Industrial Assessment Center at Boise State University. He served as the inaugural Faculty Associate for Mobile Learning. He has a Ph.D. in Mechanical Engineering from the University of Wyoming (Laramie, Wyoming). He has approximately 25 publications/ presentations. He is a member of the American Society for Engineering Education (ASEE). He is the recipient of David S. Taylor Service to Students Award and Golden Apple Award from Boise State University. He is also the recipient of ASEE Pacific Northwest Section (PNW)

List of Contributors

413

Outstanding Teaching Award, ASEE Mechanical Engineering division’s Outstanding New Educator Award and several course design awards. He serves as the campus representative (ASEE) for Boise State University and as the Chair-Elect for the ASEE PNW Section.

Jeffrey Shelton Purdue University, West Lafayette, IN, US Email Address: [email protected] Jeffrey N. Shelton is a Lecturer for the School of Mechanical Engineering at Purdue University (West Lafayette, Indiana) where he has been since 2016. He previously owned and operated Quarter 20 Engineering, LLC, an engineering consultancy, for a dozen years. He has worked in the general fields of product development and machine design since 1982. He has a Ph.D. in Mechanical Engineering from Purdue University (West Lafayette, Indiana), and is a licensed Professional Engineer in the State of Indiana. He is an inventor on 3 U.S. patents. He is a member of the American Society for Engineering Education.

Carter Williams Stokeld Williams, Tulsa, OK, US Email Address: [email protected] Carter Stokeld is a Supervisor in the Measurement Department for Williams in Tulsa, OK. Since starting at Williams in 2014 he has worked as a Construction Engineer, Technical Services Engineer, Environmental Specialist, Commodity Marketer, Business Intelligence Analyst and other various positions in both engineering and business departments. He earned a Bachelors of Science in Mechanical Engineering from Oklahoma State University. He has passed the Fundamentals of Engineering exam, has presented for different Measurement Symposiums, and has voluntarily taught college courses at the University of Tulsa.

Taylor Tryon Oral Roberts University, Tulsa, OK, US Email Address: [email protected]

414

List of Contributors

Mark Vaccari John Zink Hamworthy Combustion, Tulsa, OK, US Email Address: [email protected] Mark Vaccari is a development engineer at John Zink Hamworthy Combustion (Tulsa, Oklahoma), where he has been since 2012. He is also an adjunct professor at the University of Tulsa (Tulsa, Oklahoma), where he has taught since 2014. He has a Ph.D. in Chemical Engineering from the University of Tulsa. He is a licensed Professional Engineer in the State of Oklahoma. He was an editor of A Gallery of Combustion and Fire, and a contributing author of Mathematical Modelling of Gas-Phase Complex Reaction Systems: Pyrolysis and Combustion. He is a senior member of the American Institute of Chemical Engineers.

Kenneth Van Treuren Baylor University, Waco, TX, US Email Address: [email protected] Kenneth W. Van Treuren is the Associate Dean for Research and Faculty Development in the School of Engineering and Computer Science at Baylor University. He started in the Department of Mechanical Engineering in 1998. Dr. Van Treuren previously worked for 21 years as an officer in the United States Air Force where he flew refueling aircraft such as the KC-135 and KC-10 for almost 10 years. He also spent 8 years teaching in the Department of Aeronautics at the USAF Academy such topics as thermodynamics, fluid dynamics, and heat transfer, as well as aircraft and gas turbine propulsion design. Research areas are low pressure gas turbine flow separation, urban wind turbines, small Unmanned Aerial System propeller design, and engineering education. He earned a B.S. in Aeronautical Engineering from the USAF Academy, a M.S. in Engineering from Princeton University, and a D.Phil. in Engineering Science from the University of Oxford, UK. He has approximately 130 peer reviewed journal/conference proceedings and is the author of four book chapters/contributions. He is a member of the American Institute of

List of Contributors

415

Aeronautics and Astronautics, American Society of Mechanical Engineers, American Society for Engineering Education (ASEE), and the SAE International. He serves on the Board of Directors for ASEE and is on the Central Texas Science and Engineering Fair Board.

INDEX # 3D printing, 186, 222, 385

A ABET, xv, xvii, xxiii, xxvi, xxx, xli, xlii, xlix, lii, 86, 90, 93, 97, 98, 101, 107, 147, 172, 173, 179, 190, 193, 342, 344, 358, 367, 371, 376, 385, 389, 392, 397 academic learning, 41 academic performance, xxxv, 207 academic success, 148 accelerometers, 245 access, xlii, xlv, xlvi, 40, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 56, 58, 59, 83, 128, 181, 240, 288, 306, 318, 336, 356, 384, 395 accessibility, 44, 259 accommodations, 58, 85 accountability, 130, 285 accreditation, xxix, xxxi, xxxviii, xli, xlii, xlix, 172, 342, 344, 358, 376, 397

active learning, ix, x, xix, xlvii, xlviii, lii, 38, 41, 44, 60, 61, 62, 73, 81, 88, 103, 104, 109, 111, 125, 132, 138, 139, 144, 174, 177, 209, 281, 282, 284, 285, 286, 287, 296, 298, 300, 302, 303, 306, 308, 309, 310, 311, 312, 379 actuators, 148, 149, 153, 161, 162 adaptation, 343 additive manufacturing, 384 ADHD, 66 adjunct instructors, 371 adjuncts, 372, 384 adult learning, 8, 32 adults, 32 advancement, xii, xiii, xiv, xl, xlii, 38, 89, 104 advancements, xxiv, 384 advocacy, 242, 243, 244, 248, 249 aerospace, ix, xiv, xlvii, 103, 104, 105, 109, 112, 126, 135, 144, 192, 203 aerospace education, 103 aerospace engineering, xiv, 104, 192 aesthetics, 195, 220, 337 age, xii, xviii, 3, 9, 10, 11, 12, 19, 20, 26, 27, 256, 260, 275, 302, 371

418

Index

aggressiveness, 304 agriculture, xxiv Air Force, 111, 127, 142, 143 airports, xiv, 104 algorithm, xviii, 256 American Society of Mechanical Engineers (ASME), xxv, xxxiii, xxxvi, xlii, xlix, l, 122, 141, 144, 174, 228, 229, 230, 231, 232, 233, 234, 345, 351, 358, 371, 374, 376, 380, 383, 384, 385, 386, 389, 391, 392, 393, 397, 398 Analysis and Design of Propulsion Systems, 104, 109, 140, 141 anatomy, 328, 334, 335, 336 Angola, 15 animations, 265, 394 annotation, 45, 50, 62, 68 anxiety, 85 apathy, 139 aquarium, 340 Argentina, xliv armed forces, xxxix arteries, 336 articulation, xxviii ASME Center for Education, xxxiii, 358, 374 Assessing The Learning Strategies of AdultS (ATLAS), 5, 6, 8, 17, 30, 31 assessment, vi, x, xii, xxi, xxvii, xxx, xxxi, xxxv, xxxviii, xlviii, l, lii, 37, 43, 50, 55, 56, 59, 63, 71, 72, 73, 78, 82, 83, 84, 85, 86, 87, 89, 93, 94, 95, 97, 98, 99, 100, 104, 107, 108, 123, 128, 135, 136, 138, 139, 142, 144, 145, 156, 157, 159, 171, 172, 173, 174, 175, 182, 186, 187, 188, 190, 220, 232, 249, 268, 277, 279, 285, 292, 293, 299, 331, 341, 342, 348, 382, 390, 396 assessment techniques, xxvii assessment tools, 104, 249 assimilation, 3 assimilators, 4

ATLAS, 5, 6, 8, 17, 30, 31 attitudes, 116, 238, 288 authenticity, 39 authorities, 205 authority, 192 automation, 170 autonomy, 148, 174, 183, 304, 337 awareness, 95

B background information, 298, 329 bacteria, 331, 339 bad day, 85 barriers, xvii, xxxi, 65, 179, 187, 371 base, 117, 145, 184, 225, 285, 322 batteries, 229 beams, 85, 289 beer, 328 behaviors, xv, xvi, 3, 146, 147, 160, 165, 166, 167, 168, 171, 238 benchmarking, 199 bending, 222, 301 benefits, xvii, xx, 41, 62, 97, 98, 182, 188, 212, 236, 237, 240, 243, 244, 248, 269, 275, 287, 288, 289, 305, 315, 373 bias, 128 bimodal, 7 biological systems, 329, 330, 331, 332, 338, 339, 340 biomechanics, 332 blends, 386 blogs, 99 blood, 334, 335, 336 blood circulation, 336 blueprint, 251 boat, 197 bone, 323 brain, 282, 332 brainstorming, 114, 151, 199 breakdown, 244, 326

Index breast cancer, 66 budget(s), 195, 196, 198, 199, 219, 220, 223, 224, 225, 227, 229, 230, 245, 248, 357, 366, 370 budgeting, 225, 248 burn, 117, 119, 361 business model, 237

C CAD, 186, 195, 199, 203, 215, 226, 228, 326 calculus, 66 caliber, xxix calibration, 152, 245, 289, 364 CAM, 326 capstone projects, xxxiv, 94, 230, 357, 366, 372, 374, 376, 386, 390 Caribbean, xliv, 205 case studies, xxxviii, 290, 301, 305 case study, 43, 64, 114, 290, 305 casting, 222 catalyst, 62 CATME, 107, 108, 109, 111, 123, 124, 127, 128, 135, 136, 138, 142, 143, 144, 192, 222, 232 Caucasians, 30 causal relationship, 328 CEE, xlv cell phones, 388 Ceramics, 306 certificate, 61 challenges, xi, xvi, xx, xxi, xxviii, xxx, xxxi, xxxii, xxxvii, xl, 2, 42, 83, 106, 117, 127, 140, 154, 178, 180, 182, 183, 185, 194, 230, 238, 249, 354, 382, 386, 388 Chamber of Commerce, xiv, 104 chemical, xxxix, xlv, 7, 295, 323, 328, 337 chemical reactions, 337 Chicago, 35, 88, 141, 277, 278, 321, 349

419

childhood, 40 children, xxxvii, 60, 68, 320 China, 15 chi-square analysis, 17 circular economy, 310 Circular Economy, xix, 282, 304, 305, 309 circulation, 334, 336 cities, xxiv citizenship, 42 clarity, 96 class in school, xii, 3, 11 class period, 99, 130, 133, 134, 154, 227, 230, 267, 291, 298 class size, 188 class teaching, 225 classes, xiii, xviii, xxxvii, 47, 63, 68, 73, 86, 90, 95, 97, 100, 107, 113, 126, 130, 183, 188, 190, 192, 194, 219, 229, 231, 236, 239, 240, 246, 247, 250, 252, 288, 296, 356, 357, 365, 367, 368, 370, 375, 387 classification, 339 classroom, xiii, xxxi, xxxii, xxxix, xl, 31, 49, 61, 64, 65, 68, 73, 90, 104, 106, 110, 125, 128, 132, 148, 150, 179, 186, 187, 194, 207, 237, 241, 248, 250, 261, 274, 293, 294, 296, 306, 307, 328, 356, 368, 370, 372, 375, 376, 392, 396 classroom environment, 73, 125, 294 cleaning, 227 cleanup, 223 clients, xxxvii, 366, 389 coaches, 87, 249 Codes and Standards, 392 coffee, 61 cogeneration, 96 cognition, 63, 83 cognitive style, 6, 7, 8, 31, 34, 35 collaboration, xxxiv, xlii, xliv, 44, 50, 53, 54, 238, 285, 297, 389 collaborative learning, xii, 38, 63, 147, 172, 285, 312 colleges, xxvii, xxviii, 182

420

Index

color, 151 combustion, xxiv, 95, 227 commercial, 113, 337, 357 communication, x, xv, xxviii, xxxii, xxxiv, xxxv, xlviii, 31, 33, 40, 46, 47, 48, 53, 54, 57, 62, 63, 65, 67, 73, 76, 127, 146, 149, 158, 182, 193, 196, 214, 220, 222, 226, 228, 232, 238, 292, 367, 368, 376, 389, 399 communication skills, xxviii, 48, 196, 226, 367, 389 communication systems, 149 communities, xxxvi community, xiv, xxviii, xxxii, xxxv, xxxix, 6, 32, 39, 53, 60, 62, 104, 181, 191, 199, 236, 237, 276, 284, 309, 378 community policing, 6, 32 compatibility, 316 compensation, 374 competency-based, xxxvi, 71, 87, 88 competency-based learning, 71 competition, 113, 114, 125, 126, 127, 129, 139, 140, 156, 157, 158, 159, 180, 182, 186, 190, 226, 229, 232 competitiveness, xxx compilation, xix, 116, 246, 315, 318 complement, 40, 239, 251, 288 complexity, xvi, 150, 178, 190, 212, 218, 221, 222, 224, 227, 228, 230, 316, 317, 332, 369, 389 compliance, 364 composites, 288, 303 composition, 107, 128, 285, 389 comprehension, 65, 293 compressibility, 258 computational modeling, 300 computer, xi, xvii, xviii, xxi, xxv, xxxii, xlv, 2, 60, 65, 126, 149, 150, 152, 190, 210, 215, 226, 256, 257, 275, 277, 289, 295, 325, 326, 327, 331, 357, 363, 382, 388, 391 computer simulations, 391

computer software, 327 computer systems, 149 computing, xlvii, 63 conception, 369 concepts proposal, 247 conceptualization, 286 conclusion, 30, 61, 86, 100, 139, 171, 194, 231, 240, 248, 251, 275, 306, 322, 396 concussion, 243 condensation, 262 conditioning, 241, 368 conduction, 368 conductivity, 284, 290 conference, xxv, xlvii, 377 conflict, 222 conflict resolution, 222 connectivity, 40 conscious perception, 318 consciousness, 322 consensus, 6, 152, 237 construction, xxxi, 63, 155, 157, 198, 199, 241, 261, 263, 333, 377, 379, 380 consulting, 183 consumption, 116 containers, 305 contractual obligations, 369 contradiction, 296 control group, 270, 271, 273 controlled trials, 66 convention, 361 convergers, 3 conversations, xlvi, 243 cooling, 283 cooperation, xliii Cooperative Learning, 141, 142, 143, 173, 310, 312, 313 co-ops, 374 copper, xxiv corporate policy, 369 correct range, 361 correlation, 113 correlations, 373

Index corrosion, 284 cost, xvi, xxxix, 52, 96, 121, 122, 130, 150, 159, 161, 171, 178, 181, 203, 204, 229, 240, 243, 248, 328, 364, 370, 392, 395 cost effectiveness, 161 course content, x, xxix, xlviii, 43, 45, 46, 52, 54, 58, 59, 252, 371, 385, 388 course work, 392 covering, xxiv, xl, 183, 298 creative thinking, 163, 165, 167, 238 creativity, xxxii, xxxiii, xxxix, 106, 113, 114, 124, 141, 143, 182, 185, 194, 196, 240, 249, 344, 393 critical analysis, 60 critical thinking, xxxviii, 182, 185, 189, 296, 298, 357, 358, 359, 360 criticism, 85 crystal structure, 299 crystals, 300 culture, xliii, 39, 63, 87, 354, 384 curiosity, xv, xx, 106, 143, 146, 238, 315, 329 curricula, xix, xxxii, xxxiii, xxxv, xxxvii, xxxix, xlix, 73, 187, 216, 281, 324, 383, 386, 393 curriculum, ix, x, xiv, xv, xvii, xxv, xxvi, xxvii, xxviii, xxxi, xxxiii, xxxiv, xxxv, xxxvii, xxxviii, xxxix, xl, xli, xlvii, xlviii, xlix, l, li, lii, 72, 75, 87, 88, 97, 105, 106, 142, 146, 147, 148, 173, 179, 183, 190, 191, 194, 207, 210, 216, 217, 218, 219, 226, 231, 235, 236, 237, 239, 252, 274, 278, 283, 288, 289, 290, 298, 300, 306, 343, 356, 363, 365, 366, 379, 383, 384, 385, 386, 392, 393, 396, 397 customers, xxxiv, 184, 205, 357 Cycle for Learning, 286 cycles, xviii, 211, 255, 258, 332

421 D

danger, 395 data analysis, 245, 289 data collection, 245, 247 data set, 93 database, 17, 32 deadlines, 219, 366, 375 decision matrix, 247 deduction, 82, 324, 327 deficiencies, 136, 219 deficiency, 138 Delta, 104, 379 demographic, xii, 3, 30 demonstrations, 61 Department of Defense, 104 deployments, 166 depth, 129, 133, 136, 137, 138, 140, 144, 251, 374 derivatives, 79 Design Competition, 182, 195, 228, 232 Design Project, vi, 115, 116, 119, 120, 121, 123, 124, 125, 127, 133, 136, 137, 138, 141, 143, 145, 154, 174, 195, 203 Design Project I – Mission Analysis, 116 Design Project II – Parametric Cycle Analysis, 119 Design Project III – Engine Performance Analysis, 121 design thinking, xxxvii, 184, 191, 205, 209, 210, 211, 232, 233 designers, xvii, 45, 50, 210, 211, 213, 214, 215, 216, 218, 223, 232, 299 despair, 324 detection, 66, 153 deviation, 268 dichotomy, 384 direct observation, 319, 334, 335 disability, 85 discipline-based education research (DBER), 387

422

Index

disgust, 317 dissatisfaction, 171, 214, 293 distance learning, xix, 281, 282, 303, 307, 308 distress, 66 distribution, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 68, 76, 98, 154, 161, 164, 238, 305 diversity, xxi, xxx, xxxi, xxxv, xxxviii, xliv, 100, 382, 389 doctors, 249 documentation, xxxi, 111, 203, 204, 328, 365 draft, 99, 100 drawing, 43, 195, 199, 323 dream, 144 drug testing, 328 durability, 226 dynamic graphics, 394 dynamics, 73, 74, 75, 76, 78, 79, 87, 124, 136, 186, 287, 290, 343, 372, 390, 394

E E. coli, 331, 339 economic cycle, 305 economics, 96, 97, 305 ecosystem, xii, 38, 205, 229 education, ix, x, xi, xii, xiii, xiv, xv, xix, xx, xxi, xxiii, xxiv, xxv, xxvi, xxvii, xxviii, xxix, xxx, xxxi, xxxii, xxxiii, xxxiv, xxxv, xxxvi, xxxvii, xxxviii, xxxix, xl, xlii, xliii, xliv, xlv, xlvi, xlvii, xlviii, xlix, 1, 2, 4, 6, 7, 31, 32, 33, 37, 38, 39, 40, 62, 63, 65, 67, 81, 88, 89, 103, 104, 108, 123, 124, 132, 145, 147, 172, 173, 184, 216, 219, 234, 236, 252, 253, 257, 282, 283, 289, 291, 295, 298, 299, 300, 301, 304, 306, 307, 323, 324, 328, 329, 334, 335, 354, 358, 377, 378, 381, 382, 383,

384, 386,387, 388, 389, 391, 392, 394, 396, 398 educational experience, xxx, xxxi, 286, 359 educational objective, 219, 220, 222, 299, 307, 330 educational practices, xxxi, 282 educational research, 283 educators, xix, xx, xxxvii, xxxix, 62, 256, 260, 301, 325, 329, 354, 359, 384 Egypt, xl, 333 electives, xix, 281, 383, 385 electricity, xxiv, 96, 181 electroencephalography, 329 electronic portfolios, 43 elementary school, xxxvii emotional intelligence, xxxviii empirical studies, 40, 61 employability, xxxix employees, xiii, 42, 103, 365, 366, 368 employers, xxxvi, 366, 384, 392 employment, xx, 191, 354, 363, 369 empowerment, xxxix enamel, 227 endurance, 115, 134 energy, xxv, xxxv, 61, 79, 263, 300, 305, 329 engagement, xxxv, xl, 42, 46, 52, 54, 60, 65, 66, 136, 174, 184, 191, 237, 261, 308, 374 Engagers, 5, 6, 17, 31 engineering education, ix, x, xi, xii, xiii, xx, xxiii, xxv, xxvi, xxvii, xxviii, xxix, xxx, xxxi, xxxii, xxxiii, xxxiv, xxxv, xxxvi, xxxvii, xxxviii, xxxix, xl, xlii, xliii, xliv, xlv, xlvi, xlvii, xlviii, 2, 4, 7, 31, 32, 33, 37, 63, 81, 88, 89, 216, 219, 253, 257, 289, 291,295, 299, 306, 324, 328, 329, 354, 358, 377, 378, 381, 382, 384, 387, 388, 389, 391, 392, 393, 394, 396, 401 engineering education research, xxvii, xxxi, xxxiii, xxxv, xlvi, 382, 391, 396

Index engineering experimentation, x, xvii, xlviii, 235, 239, 240, 242, 250, 251 engineering thinking, xxxv, 209, 210, 211 England, 321, 335 enrollment, 42, 63, 127, 171, 188, 189, 302 entrepreneurial mindset, ix, x, xv, xvi, xlviii, 106, 107, 146, 147, 159, 160, 165, 166, 167, 168, 171, 196, 236, 237, 238, 239, 251, 252, 253 entrepreneurial-minded learning (EML), xv, xvii, 106, 107, 146, 147, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 171, 235, 236, 237, 238, 239, 242, 244, 246, 248, 249, 250, 251, 252 entrepreneurship, x, xv, xix, xlviii, 145, 147, 172, 173, 174, 236, 252, 253, 281, 282, 303, 304, 306, 307, 312, 390, 392, 393 entropy, 258, 263, 267 environment, xxxv, 53, 57, 60, 94, 105, 109, 138, 139, 140, 189, 193, 204, 260, 299, 305, 326, 354, 367, 369, 379 environmental effects, 96 environmental impact, 305 environments, 6, 39, 72, 107, 339 epigenetics, 338 epistemology, 337 equilibrium, 85, 86 equipment, 92, 181, 183, 220, 224, 225, 229, 230, 231, 241, 248, 288, 289, 290, 291, 340, 364, 371, 372, 384, 392, 395 equity, xliv error, 78, 224, 241, 272, 289, 360, 361, 364 estimates, 366 ethical standards, 330 ethics, xxx, xxxv, xxxvi, 337, 342 ethnic diversity, 100 ethnicity, xii, 3, 9, 13, 14, 20, 21, 27, 28, 30, 108 etiquette, 58

423

evidence, xii, xix, xliv, 38, 40, 41, 44, 100, 238, 244, 259, 298, 317, 319, 323, 336, 344, 385 evil, 324 evolution, 251, 270, 271, 337, 338, 339, 340 exaggeration, 393 examinations, 45, 46, 341 exams, ix, xlvii, 44, 49, 53, 54, 55, 57, 59, 73, 75, 78, 80, 81, 82, 85, 104, 123, 127, 130, 138, 139, 355, 363, 365, 366, 375, 390 Excel, 278, 357 exercise, xv, 95, 97, 99, 100, 111, 113, 114, 124, 129, 137, 140, 147, 191, 194, 200, 203, 206, 247, 257, 282, 289, 331, 364 expenditures, 225 experimental design, 247 experimentation, vi, x, xvii, xlviii, 94, 189, 235, 239, 240, 242, 245, 246, 247, 248, 250, 252, 289, 303, 395 expertise, v, xxxiii, xliii, 71, 105, 133, 150, 173, 224, 282, 295, 311, 359, 371, 375, 378, 384, 387, 394, 401 experts, xxxiii, 132, 133, 134, 137, 139, 283, 359 exposure, 97, 98, 150, 370, 371, 386, 393 extracurricular work activities, 370

F fabrication, 181, 188, 221, 222, 223, 227, 228, 229, 231, 289 facilitators, 87, 107 factories, 144 faculty, xiv, xx, xxvi, xxvii, xxx, xxxi, xxxii, xxxiii, xxxiv, xxxvi, xli, xliv, xlv, xlvi, xlvii, li, lii, 41, 44, 47, 48, 49, 50, 60, 86, 87, 88, 101, 105, 106, 107, 141, 146, 173, 179, 181, 182, 183, 190, 193, 204, 205, 231, 236, 243, 252, 260, 284,

424

Index

306, 329,354, 369, 371, 372, 374, 376, 378, 379, 384, 386, 387, 392, 394, 396 fairness, 390 faith, 318, 324, 341 false belief, 369 fear, 324 federal government, xliii federal regulations, 369 feedback, xix, 39, 45, 46, 52, 54, 55, 56, 58, 83, 106, 107, 109, 123, 124, 125, 135, 136, 149, 150, 192, 206, 215, 230, 247, 259, 261, 268, 270, 273, 274, 279, 284, 292, 294, 298, 301, 310, 331, 341, 342, 343, 345, 355, 366, 367, 370, 373, 375, 390, 392, 396 fiber, 289 filters, 153 financial, 181, 225, 230 financial support, 230 fixation, 212, 234 flexibility, xii, 5, 38, 44, 60, 197, 316, 385, 386 flexible stylists, 7 flight, 116 Flipped Classroom, 141, 172, 308 flowers, 324 fluid, ix, xxv, xl, xlvii, 73, 90, 91, 92, 149, 218, 258, 259, 270, 275, 277, 362 food, 324 football, 243, 246, 247, 249 force, xxvii, xxxiii, xli, 17, 142, 143, 203, 204, 229, 322, 383 formal education, 67 formation, 100, 135, 222, 304, 317 formula, 48, 49, 50, 156 foundations, xxxvi fracture toughness, 284 France, 67, 68, 345 free choice, 99 freedom, 79, 240, 249 frequency distribution, 9, 10, 14 fuel cell, 328

fuel consumption, 116, 117, 119 functional architecture, 332 funding, xxix, 248 funds, 97

G gender, xii, 3, 9, 10, 11, 12, 13, 14, 18, 19, 25, 26, 30, 100, 108 general education, xli, 243 general knowledge, 328 General Motors, 353 geometry, 301 Georgia, 277, 348 Germany, xxv, 15, 349 gifted, 194 global economy, xxx, 393 globalization, xl God, 318, 349 goods and services, 304 google, 57, 59 Google, 48, 49, 53, 57, 272 government, iv, xxix, xliii, xliv, 354, 373, 389 governor, 335 GPA, 33, 73, 100, 108 GPS, 257 grades, xii, 71, 76, 79, 81, 156, 158, 187, 191, 355, 366, 370, 375 grading, xii, xiii, 71, 72, 73, 74, 75, 76, 78, 81, 82, 83, 84, 87, 88, 97, 98, 188, 192, 220, 244, 250, 252, 366, 367, 396 grading curves, 366 graduate education, xxxi, xxxiii, 289 graduate students, xx, 183, 218, 354, 372, 373 gravitational field, 316 gravity, 204 grotesque, 324 group activities, 6, 107, 287 group work, 53, 234, 295

Index

425

growth, xxv, 194, 300, 343, 395 guidance, xliv, 52, 114, 125, 179, 225, 231, 276, 297, 329 Guided Inquiry, 298, 308 guidelines, 198 Gulf of Mexico, 205

hydrogen, 305 hygiene, 334 hypothesis, 85, 270, 271, 286, 289, 320, 344 hypothesis test, 289

H

ideal, 192, 257, 258, 260, 262, 264, 265, 266, 274, 305, 323 identification, xv, 146, 339 identity, 191, 321 ill-defined problems, 356, 357 ill-structured problems, 356 image, 65 imagery, 6 images, xxiv, 6 imagination, xxviii, 393 improvements, xxxviii, 105, 156, 160, 163, 164, 382 incidence, 242 incubator, xxxv independence, 250 Index of Learning Styles or ILS, 7 India, xliv, 15 individual differences, 3 individual students, 185 individuals, 107, 114, 188, 249 Indonesia, 15 industrial experience, 374 Industrial Revolution, xxiv, 393 industries, xiii, 103, 367, 385, 386 industry, v, vi, ix, x, xiii, xiv, xx, xxviii, xxix, xliii, xlviii, 97, 103, 104, 105, 109, 126, 127, 131, 132, 139, 140, 179, 180, 181, 190, 191, 193, 203, 225, 230, 231, 233, 237, 301, 304, 331, 353, 354, 355, 356, 357, 359, 364, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 384, 387, 389, 392, 395, 396, 397 industry standards, 369

hands-on design, x, xvii, xlviii, 195, 209, 210, 213, 216, 217, 218, 219, 221, 222, 224, 225, 226, 231 hardness, 290 Hawaii, 276, 277, 348 hazards, 181 heat capacity, 283 heat transfer, 218, 284, 361, 368, 401 high school, xiv, xxxvii, 104, 373 higher education, xii, xiv, xlvi, 38, 40, 42, 65, 66, 104, 236 High-Impact Practices, 310 hiring, 389 history, xxiv, xxvi, xl, 129, 316, 317, 329, 330, 331, 337, 342 homework, 44, 45, 46, 49, 52, 54, 55, 56, 59, 75, 82, 83, 104, 111, 114, 123, 124, 127, 128, 130, 134, 136, 185, 187, 245, 268, 365, 366, 375, 388, 396 honesty, 58 host, 52, 372 House, 307, 350, 377 human, xxiv, xxx, 39, 214, 259, 276, 316, 324, 325, 328, 334, 336, 337, 338, 344, 349, 358 human body, 316, 328, 336, 337, 338 human brain, 325, 329 human cognition, 276 human condition, 338 husband, 319, 321 hybrid, xl, 44, 54, 59, 302, 306 Hybrid Learning, 302

I

426

Index

inertia, 260, 384 inferences, 344 information technology, xxxv, 105 infrastructure, 181 injuries, 334 innovation, x, xi, xxi, xxiv, xxvi, xxx, xxxi, xxxii, xxxiii, xxxix, xlii, xliv, xlviii, 2, 38, 41, 66, 67, 73, 106, 113, 114, 141, 143, 196, 302, 304, 382, 383, 390, 392, 393, 396 institutions, xiv, xxvi, xxviii, xxix, xxxiv, xxxvi, xxxviii, xxxix, xli, 101, 104, 107, 236, 251, 343 instructional activities, 55 instructional design, xi, 2, 4, 31, 42, 45, 59, 261 instructional materials, 30 instructional methods, xxxvi, xlvii, 22, 61 instructional practice, xxxiv, 31 instructors, xi, xii, xiii, xvi, xvii, xviii, xx, xxxvi, 2, 5, 22, 30, 37, 40, 42, 50, 53, 61, 62, 77, 89, 94, 107, 108, 148, 178, 179, 180, 183, 184, 185, 187, 188, 189, 191, 192, 193, 194, 207, 225, 231, 236, 241, 246, 247, 251, 252, 255, 256, 259, 260, 262, 267, 269, 275, 282, 291, 292, 294, 298, 356, 359, 360, 361, 362, 366, 367, 372, 377, 384, 388, 389 integrated circuits, 153 integration, 40, 41, 61, 65, 67, 107, 149, 152, 251, 260, 261, 289, 318, 379 integrity, 354 intellectual capital, xxx intellectual property, 231, 238, 304 intelligence, xxxv, 113, 194, 338 intentionality, 338 interdependence, 258, 259, 268, 269, 285, 295 interface, xxxii, 241, 289 international students, 243, 387 internationalization, xxxviii internships, 283, 372, 374, 376

interpersonal communication, 222 intervention, 67, 270, 272, 286 intrinsic motivation, 183, 191 introduction, v, xxiii, xxxvi, 1, 31, 37, 50, 64, 67, 71, 81, 89, 103, 104, 107, 109, 123, 124, 127, 131, 136, 139, 140, 145, 151, 152, 172, 177, 186, 198, 199, 204, 209, 235, 245, 255, 260, 261, 269, 275, 277, 281, 308, 315, 325, 329, 342, 345, 347, 349, 353, 381 Introduction to Aeronautics, 104, 107, 109, 127, 131, 140 inventions, xxiv, xxxiv investment, 225, 248, 252, 288, 294, 369 Iowa, xxv IRC, xviii, 256, 277 isolation, 216, 239, 245 isotherms, 264 issues, xxviii, xxxii, xxxv, xl, 66, 129, 225, 237, 284, 289, 292, 301, 305, 330, 331, 333, 341, 343, 389 iteration, 269

J Japan, xliv Jigsaw, 107, 130, 132, 133, 134, 135, 137, 138, 140, 142, 288, 294, 295, 312 Jigsaw 1 – Lightweight Utility Fighter Design Features, 132 Jigsaw 2 – Lightweight Fighter Performance, 134 jigsaw technique, 132, 134, 295 job creation, 303 job skills, 191 Jordan, 147, 173, 295, 310, 380, 398 journals, xxiii, xlii, xliv, xlv, xlvi judgment, 360, 370 jumping, 223, 298

Index K Kenya, 15 Kern Entrepreneurial Engineering Network (KEEN), ix, xiv, xv, xvii, xlviii, 106, 142, 143, 145, 146, 147, 159, 160, 171, 173, 174, 236, 237, 239, 240, 249, 251, 252, 253 kinetics, 79 knowledge acquisition, xxxi knowledge economy, xxix, 384 Korea, xliv

L laminar, 362 languages, 210 laptop, 241 Latin America, xliv law enforcement, 6 laws, 79, 317, 318, 340 lawyers, 249 lead, xvii, xxxviii, 61, 85, 91, 92, 93, 94, 95, 116, 179, 184, 218, 226, 227, 245, 322, 332, 333, 355, 365 leadership, xxx, xliii, 94, 135, 137, 193, 354, 367, 373 leadership positions, 373 leakage, 305 learner progress, 55 learners, xi, xii, xxi, xxvii, xliv, 2, 3, 5, 7, 22, 34, 37, 39, 41, 42, 44, 54, 55, 56, 58, 59, 62, 73, 284, 297, 382 Learning Cycle, 35 learning environment, xii, xxxviii, 3, 34, 38, 74, 138, 174, 188, 303 learning outcomes, 40, 73, 76, 223, 250, 251, 257, 259, 261, 268, 274, 302, 303, 330, 341 learning preferences, v, ix, x, xlvii, 1, 8, 34, 35, 383, 387

427

learning process, 44, 59, 111, 138, 282 learning strategy preferences, 1, 5, 6, 8, 17, 18, 20, 30, 31 Learning Style Inventory, 3, 33 learning styles, 2, 3, 5, 7, 8, 22, 32, 33, 34, 284, 382 learning task, 5, 7 LED, 228 legs, 117, 121 lens, 39 life cycle, 369 lifelong learning, xlv, 33, 330, 367 lifetime, 106 light, xxviii, 104, 228 Lightweight Utility Fighter Design Project, 130 Likert scale, 46, 123, 289, 341, 342 literacy, xxxvii, 42 literature review, xxiii, 246 local community, xiv, 104 loneliness, 66 Louisiana, 143, 276 love, 324 LSI, 3

M magnet, 227 magnitude, 201, 360, 362 majority, xvii, 3, 7, 15, 16, 123, 209, 259, 302, 373 management, xiv, xxi, xxx, xxxii, 42, 47, 59, 104, 141, 191, 193, 197, 207, 252, 311, 357, 366, 367, 368, 370, 374, 377, 379, 380, 381, 389 manipulation, 395 manufacturing, xxv, 182, 215, 222, 226, 232, 295, 297, 300, 301, 305, 326, 384 marginalization, 287 marine environment, 204 marketability, 378

428

Index

marketplace, xxxiii, 113, 383 mass, xix, 79, 263, 281, 361, 364, 385 mastery-based learning, v, 71, 72 materials, x, xix, xxiv, xxv, xxxvii, xlviii, 1, 43, 44, 46, 47, 51, 52, 55, 57, 59, 129, 197, 198, 206, 214, 215, 216, 219, 220, 225, 229, 281, 283, 285, 288, 289, 290, 292, 293, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 308, 312, 317, 326, 327, 356, 383, 385, 388 materials science, xix, 281, 282, 285, 286, 288, 289, 290, 292, 293, 295, 296, 298, 299, 300, 301, 302, 306, 307, 308, 310, 312, 313 mathematical knowledge, xxiv mathematics, xxiii, xxvi, xxxiv, xxxvii, xli, 64, 66, 75, 88, 284, 309, 317, 318, 358 Matlab, 357 matrix, 97, 247, 303 matter, xxix, 75, 114, 220, 249, 250, 263, 319, 337, 394 Mauritius, 32 measurement, 360, 363, 364, 372, 373 measurement uncertainty, 363 measurements, 92, 93, 241, 289, 363, 364, 373 mechanical engineering education research, xxi, 381, 383 mechanical engineering students, xi, xix, xxxvi, xlvi, xlvii, 1, 2, 8, 17, 30, 282, 289, 383, 387, 389, 391 mechatronics, ix, xlviii, 145, 148, 149, 150, 172, 174 medical, xl, 147, 328, 334, 335 medicine, xxx, 40, 335 membership, 251 memorization, 356 memory, 335 mental model, x, xlviii, 212, 257, 258, 259, 263, 270, 271, 274, 275 mentors, 370, 372, 390 messages, xxix, 319, 320

meta-analysis, 284, 297, 308 metallurgy, 312 metals, xxiv meter, 241 methodology, 8, 93, 94, 95, 190, 205, 218, 220, 316, 319, 340 Mexico, 15, 67, 277 Middle East, 13 military, 143 minorities, xiv, 104, 307 misconceptions, 300, 327 mission, xlii, xliii, xliv, xlvi, 106, 111, 112, 115, 116, 117, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 130, 136, 137, 140 missions, 354 Missouri, 349 mixed processors, 7 mobile device, 39, 40, 41, 42, 43, 45, 46, 47, 48, 49, 57, 59, 61, 63, 64, 68 mobile phone, 63 modeling, xxxi, xxxviii, liii, 211, 212, 216, 259, 299, 300, 307, 325, 348 models, xvii, 4, 75, 83, 208, 210, 211, 212, 213, 214, 216, 221, 223, 231, 237, 241, 257, 259, 270, 274, 275, 300, 328, 331, 373, 391 modifications, xvi, 116, 147, 156, 158, 160, 258 modules, 51, 107, 139, 239, 294, 299, 328, 329, 339 modulus, 283, 290 molecules, 262 momentum, 79, 192, 237, 251 motif, 339 motivation, 51, 106, 130, 138, 181, 229, 230, 238, 282, 294, 296, 393 motor control, 229 Muddiest Points, 291, 308 multimedia, x, xi, xxxii, xliv, xlviii, lii, 2, 7, 33, 34, 45, 46, 388, 391, 394, 398, 399 Multimedia Cone of Abstraction, 391, 398

Index multiple acceptable solutions, 357 murder, 324

N Namibia, 15 nanotechnology, xlv, 289 National Academy of Engineering, xi, xx, xxiii, xxvii, xliii, li, 2, 34, 217, 234, 325, 348, 349, 381, 387, 393, 395, 399 National Academy of Sciences, 88, 346, 350 National Center for Education Statistics, 311 National Research Council, xxxvi National Science Board, xxviii, li, 393, 399 National Science Foundation, xiv, xxvi, xxviii, xxxii, xxxv, li, 4, 146, 393, 399 native country, xii, 3, 8, 9, 15, 20, 22, 27, 28 native language, xii, 3, 9, 16, 21, 23, 29 natural gas, 96 natural selection, 332 navigators, 5, 17, 31 NCES, 302, 311 negative experiences, 92 negative influences, 287 neglect, 365 Nepal, 88 neuroscience, 282, 329 neutral, 292 Nigeria, 15 nodes, 338 novices, 359 numerical analysis, 221 nursing, 296 nutrients, 343 nutrition, 282

O observational learning, 68 Oklahoma, xxiii, 1, 307, 353, 381, 403

429

onboarding process, 369 one-to-one tutoring, 87 online courses, xxxviii, 53, 302, 384 online labs, 395 online learning, xl, 47 open-ended assignments, 357 open-ended design, 169, 230, 231 open-ended problems, x, xvi, xvii, xlviii, 107, 177, 178, 179, 180, 182, 183, 184, 185, 187, 188, 189, 190, 194, 239 operations, 153 opportunities, xii, xiv, xv, xviii, xxxii, xxxvi, xliii, 38, 40, 42, 44, 53, 60, 72, 84, 85, 94, 104, 106, 146, 182, 183, 194, 226, 236, 238, 243, 251, 252, 303, 367, 374, 395 optimism, 288 optimization, 117, 119, 130, 136, 150, 301 oral presentations, 95 order of magnitude, 360, 362 organizations, xxiii, xli, xliii, 113, 193, 373, 401 organize, 200, 359 originality, 113 outreach, xiv, 104 overlap, 169, 273 oversight, 213, 287, 298, 299 ownership, 86, 87, 91, 181, 230, 231 ox, 336 oxygen, 335

P pain, 324 Pakistan, 15 parallel, 84, 134, 150, 215, 239, 248, 339, 368 parents, 39, 249 participants, xlvii, 7, 9, 10, 13, 14, 16, 21, 24, 25, 27, 29, 30, 45, 46, 47, 105, 107, 195, 196, 197, 341

430

Index

pathologist, 331 pathway, 343 pathways, xxxiii, xxxv, 331, 343 PCA, 119 peace, xliii pedagogy, ix, xii, xxxiv, xl, xlvii, 38, 39, 41, 61, 64, 65, 250, 260 peer assessment, 139 peer review, 98, 186 peer tutoring, 55, 60 penalties, 366, 375 perceived self-efficacy, 296 personal choice, 243 personal development, 39 personal stories, 249 personality, xxxv personality traits, xxxv physical laws, 299 Physical Model, 234 physical phenomena, 334 physics, 63, 229, 296, 317 physiology, 334 pipeline, xiv, 104 pitch, 304, 323 platform, 42, 44, 59, 60, 130, 169, 278, 303 Plato, xxxix, li playing, 153, 155, 157 polar, 116 police, 6, 17, 32, 321 policy, xliv, xlv, 58, 237, 369 politics, 372 pollution, 304, 305 polymer, 289 population, ix, x, xi, xxx, xlvii, 1, 2, 4, 6, 17, 18, 21, 30, 72, 205, 302, 383, 387 portfolio, 46, 62, 63 positive feedback, 341 power generation, xviii, 255 practical knowledge, 169 practice, ix, x, xv, xix, xx, xxiv, xxix, xxxi, xxxiii, xl, xliv, xlv, xlvii, xlviii, xlix, l, lii, 30, 31, 32, 39, 41, 48, 64, 66, 68, 74,

77, 83, 85, 106, 126, 147, 148, 150, 169, 174, 191, 207, 246, 248, 256, 258, 259, 262, 263, 267, 269, 273, 284, 308, 310, 315, 322, 335, 348, 355, 356, 358, 365, 368, 372, 374, 376, 379, 384, 386, 389, 391, 396, 398 pregnancy, 328 preparation, xlvii, 111, 138, 187, 290, 293, 386 pre-planning, 221 president, xxix, xxx, 201, 374 prestige, xxix, xxx primary function, xliii principles, xxiii, xxxii, xxxvii, xlvi, 59, 61, 64, 79, 80, 129, 195, 301, 304, 328, 329, 330, 331, 342, 343, 358, 373, 388 prior knowledge, 246, 296 probability, 360 problem solvers, 30, 358, 359, 387 problem solving, ix, x, xx, xxiii, xxxi, xxxii, xxxv, xxxvi, xxxviii, xlvii, xlviii, 1, 3, 30, 42, 45, 46, 52, 82, 106, 148, 184, 196, 205, 293, 302, 318, 322, 333, 356, 358, 359, 360, 379, 383 Problem-Based Learning, 145, 147, 173, 174, 297, 307, 312 problem-solving, xxxii, 49, 56, 60, 67, 210, 293, 355, 359, 370 problem-solving skills, 67, 355, 359 procurement, 114 product design, 326 productivity growth, xxx professional careers, 269, 283 professional development, xvii, xliii, xlv, 210, 236, 238, 330 professional practice, x, xx, xxxi, xxxiii, xlviii, 354, 372, 375, 382, 387 professionalism, xxx professionals, xliii, 358, 360, 372, 383 programming, 152, 170, 226, 357 progress reports, 154 project based learning, 106, 177

Index project sponsors, 301 proposition, 318 prosperity, xxviii protein synthesis, 317 prototrial, 233 prototype, xxxi, 181, 203, 206, 212, 214, 215, 216, 218, 220, 221, 223, 224, 227, 229, 230, 231 prototyping, 184, 188, 190, 206, 211, 212, 213, 214, 215, 216, 222, 227, 232, 233, 234, 325, 328 psychological type, 3 psychology, 193 public health, xxvi, 358 public policy, xxx, xlii public schools, 359 publishing, 336 Puerto Rico, 35, 276

Q quality improvement, xxxv questionnaire, 7, 8, 199 quizzes, 44, 49, 52, 53, 54, 55, 56, 57, 59, 82, 245, 366

R race, xxvi, 30 radar, 133 radiation, 362, 368 radio, 326 ramp, 197 reactions, 200, 201, 319, 337 readership, xlv reading, 6, 61, 68, 81, 193, 285, 369 reading comprehension, 81 reading skills, 68 reality, x, xxxi, xxxviii, xl, xlviii, 62, 66, 242, 263, 299, 318, 359, 362, 364, 367, 369, 373, 391, 393, 394

431

reasoning, 4, 226, 322, 323, 327, 334, 335, 337, 344 recall, 64, 282 recall information, 282 recognition, xxiii recommendations, xi, xviii, xx, xxvii, xxviii, xxix, xxx, xxxiii, 2, 4, 47, 48, 236, 282, 356 reconciliation, 330 recovery, 327, 331 recruiting, 183 reengineering, 325 reform, xxx, xxxi, xxxiii, xxxiv, 383 regulations, 369 rehabilitation, 67 relaxation, 282 relevance, xviii, xxxii, 256 reliability, 326 religion, 324 repair, 289, 303 requirements, xvi, xvii, xx, xxix, xxxi, xxxv, 54, 99, 119, 120, 122, 130, 147, 159, 178, 180, 182, 187, 191, 192, 194, 195, 196, 209, 216, 219, 220, 221, 224, 230, 237, 240, 300, 302, 339, 342, 354, 358, 369, 370, 392 research institutions, xxvi researchers, xlvi, 283, 287, 292, 295, 325, 327, 329, 331, 332, 337, 339, 343 resistance, xxxi, 250, 260, 275 resources, xxxiv, xliii, 45, 52, 57, 58, 62, 144, 170, 183, 184, 186, 188, 193, 205, 207, 208, 218, 219, 220, 224, 225, 228, 245, 252, 259, 261, 262, 268, 269, 273, 274, 275, 291, 294, 306, 369 response, 53, 57, 94, 125, 165, 166, 167, 168, 171, 269, 274, 296, 343, 369 response time, 57 restitution, 74 restructuring, 73 reusability, xxxi

432

Index

reverse engineering, 218, 226, 227, 325, 328, 331, 337 risk, 163, 165, 238, 304 risk-taking, 304 robotics, xxxvii, 220, 227 rods, 298 roots, xxv roses, 324 rounding, 363 Royal Academy of Engineering, xxviii, xxxiii, xliii, l, lii, 393, 398, 399 rubrics, xxxi, 55, 250 rules, 151, 153

S sabbaticals, 375 safety, xxvi, xl, 96, 98, 221, 225, 226, 358, 366, 369 saltwater, 205 saturation, 274 Saudi Arabia, 15 schedule, 50, 51, 59, 95, 108, 151, 152, 193, 221, 243, 245, 365, 369 schema, 78, 82, 171, 257, 275 school, xii, xiv, xv, xx, xxiv, xxxi, xxxiii, 3, 9, 10, 11, 12, 13, 14, 15, 16, 104, 145, 146, 147, 181, 182, 192, 193, 275, 312, 334, 354, 355, 356, 357, 359, 367, 368, 370, 371, 372, 376, 384, 385, 396, 397 science, xvi, xxiii, xxvi, xxxiv, xxxvi, xxxvii, xxxix, xli, xliii, xlv, 64, 75, 78, 80, 88, 178, 190, 282, 284, 286, 291, 295, 296, 300, 301, 304, 306, 307, 309, 324, 325, 328, 334, 335, 337, 338, 341, 358, 385, 387, 390, 391 scientific theory, 222 scientific understanding, 334 scope, xlv, 180, 181, 182, 183, 184, 218, 224, 227, 229, 230, 244, 249, 251, 368, 369, 389

second language, 44, 59 secondary education, 64, 67 security, 344 self-assessment, xxxi, xxxviii, 56 self-efficacy, 53, 65, 287 self-esteem, 148, 326 self-evaluations, 108 self-study, xxxi senses, 323 sensing, 156 sensitivity, 121 sensors, 148, 149, 152, 153, 161, 162, 241, 245, 248, 250 services, xxx, xxxii, xlii, 57, 82, 113, 181, 304, 393 shape, xxvi, 227, 230, 244, 301 shock, 331 shoot, 186 shortage, xxix showing, 59, 116, 160, 200, 201, 245, 302, 356, 365, 394 sibling, 324 sign convention, 361 signals, 332 significant digits, 277, 360, 363, 364, 375 silicon, 298 silk, 317 simulation, 202, 299, 308, 309, 312, 394 simulations, 78, 223, 239, 300 Singapore, liii skill assessments, 78, 86 social change, xxvi social development, 40 social justice, xxxv social sciences, 385 social skills, 285 socialization, 39 society, xii, xxv, xxix, xxx, xxxii, xxxvi, xlii, xliv, 38, 39, 127, 238, 305, 323, 354, 382, 393 socioeconomic status, xxxiv

Index software, xi, xxi, xxxii, xxxv, 2, 7, 107, 108, 109, 111, 119, 123, 135, 139, 142, 150, 152, 153, 160, 161, 162, 202, 275, 299, 357, 382, 385, 394 solution, xvi, xxiii, xxxiv, 49, 95, 97, 98, 99, 113, 150, 178, 180, 182, 183, 190, 194, 211, 213, 237, 244, 248, 268, 297, 319, 323, 325, 338, 343, 357, 360, 361, 363, 366, 375 spatial ability, 391 specialization, xxv, xxxiii specific knowledge, 359 specifications, xxvi, 33, 88, 91, 115, 244, 368, 369 spending, 82, 83, 185, 223, 246 spin, 94 spirituality, 330, 341, 342 spreadsheets, 139 Spring, 154, 398 Sprint, 207 stability, 136 staffing, 288 stakeholders, xxxii, xxxvi, 205, 249, 343 standard deviation, 272, 273, 284 standardization, 188 standardized testing, 82 standards-based, 73, 87, 88 state, xviii, xliii, 54, 65, 87, 149, 152, 153, 183, 255, 257, 258, 260, 262, 263, 271, 274, 317, 323, 332, 337, 338, 363, 396 statics, 73, 75, 78, 87, 186, 202, 203, 207, 208, 221, 229, 290, 390, 394 statistics, 240, 291 steel, 229, 289 stock, 229 storage, 155 stress, 169, 230, 283, 289, 290, 299, 300, 301, 388 stroke, 67, 196 strong interaction, 338 structure, xviii, 5, 51, 57, 72, 76, 83, 84, 86, 125, 130, 133, 150, 151, 179, 182, 185,

433

188, 189, 228, 229, 236, 237, 246, 249, 251, 284, 288, 293, 298, 299, 301, 302, 306, 317, 333, 395 structuring, 234, 239 student motivation, 140, 180, 190, 325 student outcomes, ix, xiii, xxviii, xlvii, 89, 90, 95, 97, 98, 99, 100, 148, 217, 293 styles, xxvii, 3, 5, 7, 8, 31, 33, 34, 73, 104 Styles, 7, 32, 33, 34, 35 suicide, 321 supervision, 272 supervisors, 355, 356, 358, 366, 370, 373, 375 supply chain, 238 surplus, 227 sustainability, xxxiii, xxxvi, 295, 304, 305, 306, 309, 329 sustainable development, xxxii Sustainable Materials, 304 Swahili, 16 Switzerland, xxxvi, l synthesis, xvii, 56, 64, 178, 191 systemic change, xxxiii

T Taiwan, xliv Tanzania, 15 target, 100, 214, 243, 289, 291, 366 taxonomy, 51, 307 teachers, xxxvii, xxxix, xlvi, 6, 22, 199, 306, 357, 366, 367, 370, 375 teaching strategies, xlvi teaching/learning activities, 55 team members, 92, 99, 108, 133, 134, 137, 138, 139, 206, 219, 222, 287, 368, 370 team-based activities, 103 teams, xiii, xvi, 38, 54, 89, 90, 91, 92, 93, 94, 95, 99, 100, 107, 108, 110, 111, 112, 113, 114, 115, 123, 124, 126, 127, 128, 129, 130, 132, 133, 134, 135, 136, 139,

434

Index

140, 147, 148, 150, 151, 154, 156, 157, 161, 182, 183, 185, 186, 188, 190, 191, 192, 198, 200, 202, 203, 207, 212, 222, 226, 228, 230, 238, 241, 247, 248, 288, 297, 367, 369, 370, 371, 383, 390 teamwork, v, xiii, 89, 90, 94, 97, 98, 101, 137, 191, 193, 220, 222 technician, 219, 220, 225, 227, 368 techniques, x, xix, xxxvi, xlvi, xlviii, 5, 22, 30, 45, 46, 76, 81, 106, 107, 147, 151, 189, 215, 221, 229, 281, 284, 306, 330, 331, 332, 340, 342, 369, 396 technological advancement, 332 technological developments, 384 technologies, ix, xxxii, xxxiv, xxxix, xlvii, 39, 40, 41, 42, 60, 62, 114, 125, 130, 133, 183, 222, 303, 304, 333, 371, 384, 388 technology, v, ix, xii, xxv, xxvi, xxix, xxx, xxxi, xxxii, xxxiii, xxxiv, xxxv, xxxvi, xxxvii, xl, xli, xliii, xlvii, l, lii, 31, 35, 37, 38, 39, 40, 41, 42, 43, 44, 52, 57, 59, 61, 62, 63, 64, 65, 66, 67, 68, 73, 88, 105, 119, 126, 129, 133, 177, 208, 234, 284, 304, 307, 316, 338, 350, 351, 364, 367, 377, 378, 383, 384, 391, 395, 396, 398, 399, 404 temperature, xviii, 61, 121, 255, 257, 258, 260, 262, 264, 267, 362, 364 testing, xxvi, 4, 43, 84, 85, 153, 180, 211, 214, 216, 217, 221, 222, 223, 224, 229, 231, 244, 245, 246, 247, 248, 289, 290, 300, 328, 360 textbook, xviii, 78, 92, 119, 134, 192, 256, 261, 263, 267, 268, 343, 355, 356, 360, 396 textiles, xxiv The First and Second Day – Quick Think, 128 The First Day, 111 The Request for Proposal, 113 theatre, xxxviii

theoretical support, 284 theory, x, xvii, xx, xlviii, li, 32, 35, 38, 39, 65, 66, 67, 81, 169, 206, 207, 209, 213, 222, 293, 301, 310, 335, 336, 343, 348, 349, 356, 374, 378, 384, 386, 398 thermodynamic properties, xviii, 255, 256, 257, 258, 260, 267, 268, 269, 271 thermodynamics, ix, xviii, xix, xlvii, 43, 45, 50, 51, 53, 54, 55, 56, 59, 60, 63, 64, 67, 68, 73, 74, 88, 95, 96, 97, 99, 100, 109, 141, 172, 186, 210, 231, 255, 257, 259, 261, 262, 270, 271, 275, 277, 278, 281, 290, 300, 361, 388 think critically, 360 think-pair-share, 110, 111, 128, 129, 141, 144, 152, 172, 267, 288, 296 thoughts, 161, 316, 335, 336 threats, xxxiii time commitment, 93, 97, 191, 288 time constraints, 214 tinkerers, 338 toddlers, 316 topology, 343 torsion, 301 Toyota, xxxvi toys, 227 tracks, 335 trade, 161 trade-off, 161 training, xi, xiv, xxvi, xxvii, xxxii, xlii, xliv, xlvi, 2, 95, 127, 139, 146, 193, 220, 221, 224, 225, 228, 229, 230, 274, 303, 316, 359, 384, 385, 386, 396 training programs, 193, 396 traits, 237 trajectory, 173 transactions, 277 transcription, 339 transformation, xxx, xxxi, 393 transport, 154 transportation, xxv

Index treatment, 258, 260, 263, 270, 271, 272, 273, 275, 300, 334 troubleshooting, 169

U uncertainty, 91, 210, 211, 241, 245, 268, 363, 364 undergraduate education, xxx, xliv, 64, 105, 283, 284, 299, 391 undergraduate engineering programs, 384 uniform, 157 unique features, 193 United Kingdom, xxxix United Nations, xliii, lii, 399 United States, xiii, xiv, xxxi, l, 103, 111, 145 units, 365, 368, 375 universe, 317, 318, 324, 325, 338 universities, xxvii, xliv, 8, 42, 62, 106, 107, 194, 325, 326, 372, 378, 384, 395 updating, 105, 384 urban, 6, 32

V validation, 214 validity, 355, 359, 360 vapor, xviii, 255, 362 variables, xii, 3, 30, 189, 362 variations, 84, 189 vector, 79 vehicles, xvi, 178, 207 velocity, 121, 229 verbalizers, 6, 7 verbalizer-visualizer, 6, 8, 34 Verbal-Visual Learning Style Rating (VVLSR), 8 verbal-visual preferences, x, xi, 1, 2, 3, 9, 23, 24, 25, 26, 29, 30, 383 vessels, xxiv

435

vibration, 75 vibration analysis, 75 videos, 44, 45, 46, 47, 49, 52, 54, 55, 58, 59, 60, 81, 84, 139, 197, 223, 261, 264, 266, 267, 269, 274, 292, 394 violence, 324 virtual lab, 289, 395 Virtual Reality, 303, 313, 391, 398 viscosity, 362 vision, xxxiii, xxxvi, xlvii Vision 2030, xlix, 376, 385, 386, 387, 391, 392, 393, 397 visual processing, 6 visual-auditory, 7 visualization, 232, 259, 273, 276, 277, 299 visualizers, 6, 7 vulnerability, xxxix VVQ (verbal and visual questions), 7

W waking, 365 walking, 316 war, xxiv Washington, xlix, l, li, lii, 7, 33, 34, 172, 234, 278, 310, 312, 345, 349, 351, 377, 378, 379, 398, 399 waste, 304, 375 water, xviii, xix, 181, 197, 205, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 274, 275, 279, 305 weakness, 392 wealth, xv, xxx, 147, 340 weapons, 129, 133 web, xviii, 108, 249, 256, 311, 395 welding, 229 welfare, xxvi, 358 well-being, xliii White Paper, 174 wind turbines, 227

436

Index

Wisconsin, 348 withdrawal, 187 wood, 203, 228, 242, 295 work activities, 370 work environment, x, xx, xlviii, 31, 356, 367 work permits, 370 workers, xiv, 104, 210, 211, 212, 214, 302, 333, 372 workflow, 49 workforce, xiv, xxviii, xxix, 31, 104, 356, 359, 376, 384 work-life balance, 355 workload, xiii, 89, 108, 125, 138 workplace, xvi, xx, xxiii, 31, 98, 106, 178, 354, 358, 360, 365, 366, 370, 372

workshops, xxiii, xxviii, xxxiv, xxxvi, xlvi, xlvii, 142, 236, 304 World War I, xiv, 145 worldview, 330, 341, 342 worldwide, xxxi, xli, xliii worry, 84 written communication skills, 196, 389

Y yeast, 339 yield, 283

Z Zimbabwe, 15