Frontiers in Education : Computer Science and Computer Engineering [1 ed.] 9781683921912

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PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON FRONTIERS IN EDUCATION: COMPUTER SCIENCE & COMPUTER ENGINEERING

Editors Hamid R. Arabnia Leonidas Deligiannidis, Fernando G. Tinetti Associate Editor Ashu M. G. Solo

CSCE’17 July 17-20, 2017 Las Vegas Nevada, USA americancse.org ©

CSREA Press

This volume contains papers presented at The 2017 International Conference on Frontiers in Education: Computer Science & Computer Engineering (FECS'17). Their inclusion in this publication does not necessarily constitute endorsements by editors or by the publisher.

Copyright and Reprint Permission Copying without a fee is permitted provided that the copies are not made or distributed for direct commercial advantage, and credit to source is given. Abstracting is permitted with credit to the source. Please contact the publisher for other copying, reprint, or republication permission.

© Copyright 2017 CSREA Press ISBN: 1-60132-457-X Printed in the United States of America

Foreword It gives us great pleasure to introduce this collection of papers to be presented at the 2017 International Conference on Frontiers in Education: Computer Science and Computer Engineering (FECS’17), July 1720, 2017, at Monte Carlo Resort, Las Vegas, USA. An important mission of the World Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE (a federated congress to which this conference is affiliated with) includes "Providing a unique platform for a diverse community of constituents composed of scholars, researchers, developers, educators, and practitioners. The Congress makes concerted effort to reach out to participants affiliated with diverse entities (such as: universities, institutions, corporations, government agencies, and research centers/labs) from all over the world. The congress also attempts to connect participants from institutions that have teaching as their main mission with those who are affiliated with institutions that have research as their main mission. The congress uses a quota system to achieve its institution and geography diversity objectives." By any definition of diversity, this congress is among the most diverse scientific meeting in USA. We are proud to report that this federated congress has authors and participants from 64 different nations representing variety of personal and scientific experiences that arise from differences in culture and values. As can be seen (see below), the program committee of this conference as well as the program committee of all other tracks of the federated congress are as diverse as its authors and participants. The program committee would like to thank all those who submitted papers for consideration. About 65% of the submissions were from outside the United States. Each submitted paper was peer-reviewed by two experts in the field for originality, significance, clarity, impact, and soundness. In cases of contradictory recommendations, a member of the conference program committee was charged to make the final decision; often, this involved seeking help from additional referees. In addition, papers whose authors included a member of the conference program committee were evaluated using the double-blinded review process. One exception to the above evaluation process was for papers that were submitted directly to chairs/organizers of pre-approved sessions/workshops; in these cases, the chairs/organizers were responsible for the evaluation of such submissions. The overall paper acceptance rate for regular papers was 26%; 14% of the remaining papers were accepted as poster papers (at the time of this writing, we had not yet received the acceptance rate for a couple of individual tracks.) We are very grateful to the many colleagues who offered their services in organizing the conference. In particular, we would like to thank the members of Program Committee of FECS’17, members of the congress Steering Committee, and members of the committees of federated congress tracks that have topics within the scope of FECS. Many individuals listed below, will be requested after the conference to provide their expertise and services for selecting papers for publication (extended versions) in journal special issues as well as for publication in a set of research books (to be prepared for publishers including: Springer, Elsevier, BMC journals, and others). • • • • • • •

Prof. Afrand Agah; Department of Computer Science, West Chester University of Pennsylvania, West Chester, PA, USA Prof. Abbas M. Al-Bakry (Congress Steering Committee); University President, University of IT and Communications, Baghdad, Iraq Prof. Nizar Al-Holou (Congress Steering Committee); Professor and Chair, Electrical and Computer Engineering Department; Vice Chair, IEEE/SEM-Computer Chapter; University of Detroit Mercy, Detroit, Michigan, USA Prof. Hamid R. Arabnia (Congress Steering Committee); Graduate Program Director (PhD, MS, MAMS); The University of Georgia, USA; Editor-in-Chief, Journal of Supercomputing (Springer); Fellow, Center of Excellence in Terrorism, Resilience, Intelligence & Organized Crime Research (CENTRIC). Prof. Mehran Asadi; Department of Business and Entrepreneurial Studies, The Lincoln University, Pennsylvania, USA Prof. Dr. Juan-Vicente Capella-Hernandez; Universitat Politecnica de Valencia (UPV), Department of Computer Engineering (DISCA), Valencia, Spain Prof. Juan Jose Martinez Castillo; Director, The Acantelys Alan Turing Nikola Tesla Research Group and GIPEB, Universidad Nacional Abierta, Venezuela

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Prof. Kevin Daimi (Congress Steering Committee); Director, Computer Science and Software Engineering Programs, Department of Mathematics, Computer Science and Software Engineering, University of Detroit Mercy, Detroit, Michigan, USA Prof. Zhangisina Gulnur Davletzhanovna; Vice-rector of the Science, Central-Asian University, Kazakhstan, Almaty, Republic of Kazakhstan; Vice President of International Academy of Informatization, Kazskhstan, Almaty, Republic of Kazakhstan Prof. Leonidas Deligiannidis (Congress Steering Committee); Department of Computer Information Systems, Wentworth Institute of Technology, Boston, Massachusetts, USA; Visiting Professor, MIT, USA Dr. Lamia Atma Djoudi (Chair, Doctoral Colloquium & Demos Sessions); Synchrone Technologies, France Prof. Mary Mehrnoosh Eshaghian-Wilner (Congress Steering Committee); Professor of Engineering Practice, University of Southern California, California, USA; Adjunct Professor, Electrical Engineering, University of California Los Angeles, Los Angeles (UCLA), California, USA Prof. George A. Gravvanis (Congress Steering Committee); Director, Physics Laboratory & Head of Advanced Scientific Computing, Applied Math & Applications Research Group; Professor of Applied Mathematics and Numerical Computing and Department of ECE, School of Engineering, Democritus University of Thrace, Xanthi, Greece. Prof. Houcine Hassan; Department of Computer Engineering (Systems Data Processing and Computers), Universitat Politecnica de Valencia, Spain Prof. George Jandieri (Congress Steering Committee); Georgian Technical University, Tbilisi, Georgia; Chief Scientist, The Institute of Cybernetics, Georgian Academy of Science, Georgia; Ed. Member, International Journal of Microwaves and Optical Technology, The Open Atmospheric Science Journal, American Journal of Remote Sensing, Georgia Prof. Byung-Gyu Kim (Congress Steering Committee); Multimedia Processing Communications Lab.(MPCL), Department of Computer Science and Engineering, College of Engineering, SunMoon University, South Korea Prof. Tai-hoon Kim; School of Information and Computing Science, University of Tasmania, Australia Prof. Louie Lolong Lacatan; Chairperson, Computer Engineerig Department, College of Engineering, Adamson University, Manila, Philippines; Senior Member, International Association of Computer Science and Information Technology (IACSIT), Singapore; Member, International Association of Online Engineering (IAOE), Austria Prof. Dr. Guoming Lai; Computer Science and Technology, Sun Yat-Sen University, Guangzhou, P. R. China Dr. Andrew Marsh (Congress Steering Committee); CEO, HoIP Telecom Ltd (Healthcare over Internet Protocol), UK; Secretary General of World Academy of BioMedical Sciences and Technologies (WABT) a UNESCO NGO, The United Nations Prof. Dr., Eng. Robert Ehimen Okonigene (Congress Steering Committee); Department of Electrical & Electronics Engineering, Faculty of Engineering and Technology, Ambrose Alli University, Nigeria Prof. James J. (Jong Hyuk) Park (Congress Steering Committee); Department of Computer Science and Engineering (DCSE), SeoulTech, Korea; President, FTRA, EiC, HCIS Springer, JoC, IJITCC; Head of DCSE, SeoulTech, Korea Dr. Akash Singh (Congress Steering Committee); IBM Corporation, Sacramento, California, USA; Chartered Scientist, Science Council, UK; Fellow, British Computer Society; Member, Senior IEEE, AACR, AAAS, and AAAI; IBM Corporation, USA Chiranjibi Sitaula; Head, Department of Computer Science and IT, Ambition College, Kathmandu, Nepal Ashu M. G. Solo (Publicity), Fellow of British Computer Society, Principal/R&D Engineer, Maverick Technologies America Inc. Prof. Dr. Ir. Sim Kok Swee; Fellow, IEM; Senior Member, IEEE; Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia Prof. Fernando G. Tinetti (Congress Steering Committee); School of CS, Universidad Nacional de La Plata, La Plata, Argentina; Co-editor, Journal of Computer Science and Technology (JCS&T). Prof. Hahanov Vladimir (Congress Steering Committee); Vice Rector, and Dean of the Computer Engineering Faculty, Kharkov National University of Radio Electronics, Ukraine and Professor of Design Automation Department, Computer Engineering Faculty, Kharkov; IEEE Computer Society Golden Core Member; National University of Radio Electronics, Ukraine Prof. Shiuh-Jeng Wang (Congress Steering Committee); Director of Information Cryptology and Construction Laboratory (ICCL) and Director of Chinese Cryptology and Information Security Association (CCISA); Department of Information Management, Central Police University, Taoyuan, Taiwan; Guest Ed., IEEE Journal on Selected Areas in Communications. Prof. Layne T. Watson (Congress Steering Committee); Fellow of IEEE; Fellow of The National Institute of Aerospace; Professor of Computer Science, Mathematics, and Aerospace and Ocean Engineering, Virginia Polytechnic Institute & State University, Blacksburg, Virginia, USA

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Prof. Jane You (Congress Steering Committee); Associate Head, Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

We would like to extend our appreciation to the referees, the members of the program committees of individual sessions, tracks, and workshops; their names do not appear in this document; they are listed on the web sites of individual tracks. As Sponsors-at-large, partners, and/or organizers each of the followings (separated by semicolons) provided help for at least one track of the Congress: Computer Science Research, Education, and Applications Press (CSREA); US Chapter of World Academy of Science; American Council on Science & Education & Federated Research Council (http://www.americancse.org/); HoIP, Health Without Boundaries, Healthcare over Internet Protocol, UK (http://www.hoip.eu); HoIP Telecom, UK (http://www.hoip-telecom.co.uk); and WABT, Human Health Medicine, UNESCO NGOs, Paris, France (http://www.thewabt.com/ ). In addition, a number of university faculty members and their staff (names appear on the cover of the set of proceedings), several publishers of computer science and computer engineering books and journals, chapters and/or task forces of computer science associations/organizations from 3 regions, and developers of high-performance machines and systems provided significant help in organizing the conference as well as providing some resources. We are grateful to them all. We express our gratitude to keynote, invited, and individual conference/tracks and tutorial speakers - the list of speakers appears on the conference web site. We would also like to thank the followings: UCMSS (Universal Conference Management Systems & Support, California, USA) for managing all aspects of the conference; Dr. Tim Field of APC for coordinating and managing the printing of the proceedings; and the staff of Monte Carlo Resort (Convention department) at Las Vegas for the professional service they provided. Last but not least, we would like to thank the Co-Editors of FECS’17: Prof. Hamid R. Arabnia, Prof. Leonidas Deligiannidis, and Prof. Fernando G. Tinetti. We present the proceedings of FECS’17.

Steering Committee, 2017 http://americancse.org/

Contents SESSION: SCIENCE, TECHNOLOGY, ENGINEERING AND MATHEMATICS STEM EDUCATION AND COMPUTATIONAL SCIENCE Increasing Awareness and Participation in Computer Science Education David Hicks, Jeong Yang

3

An Analysis of Student Success in Math, Physics, and Introductory Civil Engineering Courses Jaime Raigoza

8

Effective Techniques for Student Engagement in Introductory Computing Courses Sajid Hussain, Steven Morgan, Ziaul Haque

15

Co-Curricular Industry Experiences to Promote Student Success Meline Kevorkian, Greg Simco

20

Development of 21st Century Skills and Engineering Confidence Mahmoud Abdulwahed

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SESSION: PROGRAMMING AND SOFTWARE ENGINEERING COURSES Creative Coding for All Students Mehdi R. Zargham, Chandra Kishore Danduri

31

Visualization of the Progress of the Exercises of Students in C Programming Class Hiroshi Dozono, Tomonori Enda, Hiroki Yoshioka, Masanori Nakakuni, Gen Niina

34

SESSION: ACTIVE LEARNING, LEARNING STRATEGIES, COLLABORATIVE LEARNING, TOOLS, ACCREDITATION AND RELATED ISSUES Web-based Projects in Service Learning Donald R. Schwartz

43

An Automated Tool for Individualized Guidance in a Large-Scale Course Quoc-Viet Dang

50

Towards Interest-based Adaptive Learning and Community Knowledge Sharing Karen Aguar, Hamid R. Arabnia, Juan B. Gutierrez, Walter D. Potter, Thiab R. Taha

58

Interest-Based Learning for Teaching a Human-Computer Interaction Course Media and Cognition Course Yi Yang, Shengjin Wang, Jiasong Sun, Xian Zhong

62

Project-based Learning in Computer Science Education with Social Network Application Programming Interfaces Weidong Liao

68

Student Perspectives on Learning in a Course on Engineering Applications for Nanoscience 73 and Nanotechnology Deborah Worley, Naima Kaabouch, Matt Cavalli, Kanishka Marasinghe, Nuri Oncel, David Pierce, Brian Tande, Julia Zhao ABET-CAC Accreditation at University of Petra - Assessment Plan for Continuous Improvement Shakir Hussain, Gassan Issa, Nuha El-Khalili, Muhammad Abu Arqoub, Nesreen A. Otoum

77

SESSION: TEACHING METHODOLOGIES AND STRATEGIES, ASSESSMENT METHODS, AND RELATED STUDIES Aptitude and Previous Experience in CS1 Classes Lisa Lacher, Albert Jiang, Yu Zhang, Mark Lewis

87

Class Behavior on Quizzes that include a Prisoner's Dilemma Bonus Question Peter Jamieson

96

Integrating Modern Software Tools into Online Database Course Hong Wang

100

Integration of Agile Education Solutions for Teaching Complex Subject Matter: AI system design case study John N. Carbone, James A. Crowder

104

Teaching Parallel Programming Using CUDA: A Case Study Timothy O'Neil, Yingcai Xiao

110

Two Alternative Modules for Teaching Scheduling Algorithms in Computer Science Using Web Games and Kinesthetic Learning Activities Fatimah Al-Khazl, Norah Al-Otaibi, Young G

116

Experiments for a Database Course: Access Path Selection by the Oracle Query Optimizer Jamal Alsabbagh

122

Setting MCQs That Adhere To Given Course Learning Outcomes For An Algorithms Class Charlie Obimbo

128

SESSION: NOVEL EDUCATIONAL DEGREE PROGRAMS, COURSES AND STRATEGIES On the Development of Networking and Information Security Degree Programs in a Four Year University Weidong Liao, Osman Guzide

137

A Project-Driven Operating Systems Class: A Case Study Timothy O'Neil

142

SESSION: RESEARCH PROJECTS, CAPSTONE DESIGN PROJECTS, AND NOVEL ALGORITHMS HomeUnit - An Internet of Things Air Quality Monitor Marian Grocholski, Nathan Moschler, Roslyn Debacker, Winston Grocholski, Ken Ferens, Marcia Friesen

151

A Senior Research - An Integrated and Automated Submission System Kerules Fareg, Ching-yu Huang

161

Botnets: An Unsolvable Problem? Rory F. McDowell, Andrew M. Colarik

165

Heuristic Function in an Algorithm of First-Best Search for the Problem of Tower of Hanoi: Optimal Route for n Disks Erick Berssain Garcia V., Norma Elva Chavez R.

172

An Independent Study on Geographical Information and Online Map System Jacob Quick , Ching-yu Huang

179

Design of a Laboratory Power Supply - Capstone Research Project Bassam Shaer, Andreas Fuchs, Nathaniel Morales

182

An Intelligent and Automated PDF Format Checker Tsaihsuan Yang, Yu Sun

189

Evaluating Storage Performance: Activities to Simulate Storage Configurations Adam H. Villa

196

Vectorization and Parallelization of Loops in C/C++ Code Xuejun Liang, Ali a. Humos, Tzusheng Pei

203

Vegetables Gardening Web Application - Capstone Experience Project Suhair Amer

207

SESSION: LATE PAPERS - FRONTIERS IN EDUCATION: STEM AND LEARNING TOOLS AND METHODS A Comparative Study of Early Instruction Set Architectures and their Effect on Current Computer Architecture Coverage Hassan Farhat

215

A Study of Kinesthetic Learning Activities in Teaching Computer Algorithms in Saudi Arabia 221 Ashwag Gashgari, Gilbert Young

Proposing an Improved Content Web Page Ranking Algorithm Elham Tavakol, Marjan Abdeyazdan

228

A Study of Learning Effectiveness on the Dijkstra's Algorithm Modeled in an Interactive KLA Approach Peter Chen, Paul Chiou, Gilbert Young

234

Capstone Projects: A Feedback Mechanism for Research and Design, Development and Delivery of Curricula and Courses Ramesh G. Kini, Timur F. Umarov, Fuad A. Hajiev

239

Weaving STEM into the Design, Development and Delivery of our Curricula and Courses and 245 our Research Ramesh G. Kini, Timur F. Umarov, Fuad A. Hajiev The Project Participation Tier Model: Bridging Perceptions of Computer Science 252 Competency Marlon Mejias, Ketly Jean-Pierre, Dwight Thomas, Gloria Washigton, Peter Keiller, Legand Burge

Int'l Conf. Frontiers in Education: CS and CE | FECS'17 |

SESSION SCIENCE, TECHNOLOGY, ENGINEERING AND MATHEMATICS - STEM EDUCATION AND COMPUTATIONAL SCIENCE Chair(s) TBA

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Increasing Awareness and Participation in Computer Science Education David Hicks Electrical Engineering and Computer Science Frank H. Dotterweich College of Engineering Texas A&M University-Kingsville [email protected] Abstract - This paper reports on an ongoing project that focuses on increasing awareness of and interest in computer science among students at a high school in a South Texas community. An important activity of the project has been teaching a computer science course to the high school students. The information learned during the course helps students in preparing for a popular interscholastic computer science competition, and has served as an effective student incentive during the project. The scores received by students in the competition are analyzed to compare their performance before and after having taken the computer science course. The results of a small survey are also reported that describe the current participation rate in the computer science competition of high schools within the surrounding region. Current activities of the project that might help increase participation and promote awareness of computer science are also discussed. Keywords: University Interscholastic League, UIL, Computer Science, STEM Education

1

Introduction

In many areas of South Texas, like other regions of the U.S., the resources available for public education are very limited. This can be especially true in rural regions. Often this results in important topics, such as computer science and related subjects not being included in the curriculum. This paper reports on an ongoing project that is focused on increasing awareness, interest, and participation in computer science and related programs among high school students. It provides an update on the status of the project and the results that have been obtained since it was first described in [1]. The students taking part in the project are juniors and seniors attending high school in a small city in South Texas. Currently the high school does not offer any state sponsored classes to teach these topics. South Texas is transitioning from a region with limited opportunities for historically under-represented students to one in which an expanded educational and economic infrastructure will better support the needs of its predominantly young and poor population [2]. The Texas Comptroller of Public Accounts has projected that South Texas will grow in terms of economics and its educational work force along with increases in enrollment and the number of degrees awarded at institutions of higher education [4]. However, at the same

Jeong Yang Computing and Cyber Security College of Business Texas A&M University-San Antonio [email protected] time, public school districts in the region are still ranked below the state average, and it has also been projected that this region will continue to experience shortages of qualified STEM teachers within its high schools [3]. An important goal of the project is to teach computer science topics to high school students, further increasing their interest and awareness of the topic. The popular state organized UIL (University Interscholastic League) computer science competition is employed as an effective motivational tool in the project. The details of the computer science competition are briefly described in section 2 of the paper. As reported in [1], during the first phase of the project, a computer science course was taught to high school students, and student scores were observed to improve in both the individual and the team categories of the competition. A natural extension of the project since then has been to attempt to teach the computer science course to students at a different high school and see if the results were similar. This has been done as the project has continued, and the results are reported in section 3 of the paper. Another important area that is examined in this paper is the participation rate among high schools in the UIL computer science competition. The participation rate is first considered in terms of how many schools within a region participate at all in the UIL competition. Next, for those schools that do participate, the specific parts of the competition they take part in are examined. These results are described along with a discussion of possible contributing factors in section 4 of the paper. Also included in the discussion is a description of current project activities that are anticipated to help improve participation rates. Section 5 concludes the paper and provides a brief look at future research.

2

The University Interscholastic League Computer Science Competition

The state of Texas University Interscholastic League organizes a variety of academic related competitions among state high schools. Computer Science is one of the areas included in the Science Technology Engineering Math (STEM) category of competition. The competition is arranged such that students from schools of similar size meet to compete at the individual level in a written test covering various computer science and related topics, and compete as a

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team on both a written test and a programming contest. Competitions take place first at a district level, with winners advancing to a regional competition. The winners of regional competitions then move on to the state level contest. The written part of the competition consists of an exam with 40 multiple choice questions covering a variety of computer science and related topics. Students are allowed 45 minutes to complete the exam. The scoring system for the exams awards 6 points for each correct answer given, 0 points for questions left unanswered, and 2 points are deducted for each incorrect answer. The team part of the competition consists of both a written component and 12 programming problems that students work on together to solve. The programming language used in the competition is Java. Once a team has completed a trial programming problem solution, it is submitted for evaluation. A correct solution is awarded a maximum of 60 points. Incorrect solutions are returned back to the team along with feedback concerning parts of the solution that are incorrect. If time permits student teams can update and resubmit a returned (incorrect) solution. The point value awarded for a correct solution is reduced by 5 points for each time it has been resubmitted. Student teams are given 2 hours to complete correct solutions for as many of the programming problems as possible.

3

Teaching the Computer Science Course

As mentioned previously, a computer science course was taught to a group of high school students in a previous phase of this project. The details of the course were reported along with an evaluation of student performance in [1]. In the current phase of the project, a similar course was taught to a group of students at a different high school. The details of that course are described in this section, along with a discussion of student performance.

3.1

Methodology

The contents covered by the course consisted of a variety of introductory computer science and programming related topics. The material was intentionally representative of topics likely to be encountered during the UIL Computer Science competition (based on previous competitions). The students did not receive formal credit for the course – their primary motivation was to improve their performance in the UIL competition. The course was taught using the Java programming language. As Java is also the language used in the UIL Computer Science competition, this choice further encouraged students to follow lessons carefully in order to perform better in both the written and programming parts of the competition. The course met once per week, after the high school students’ regular class day, usually on Tuesday afternoons, for approximately 1.75 hours. During class meetings a small number (usually 3 or 4) of topic areas were introduced and explained in depth along with several examples to help clarify

the material. Students were encouraged to ask questions, interacting with the teacher, teaching assistants, and also each other during classroom sessions. Homework exercises were distributed to the students at the end of each class to be submitted on Friday of the same week. Homework exercises consisted of 6 to 8 questions for each of the major topic areas covered during the corresponding class. At the start of each class, graded homework exercises from the previous week were returned to the students, solutions for the exercises were covered and any student questions were answered. The course started in mid- February of 2017 and met for a total of 8 weeks.

3.2

Evaluation and Discussion

A District level UIL Computer Science competition was scheduled to take place just after the conclusion of the computer science course. The top 3 students that participated in the computer science course also took part in the District level meet. The results of those students in the 2017 District competition are summarized in Table 1, along with the results of the same competition from the previous two years. As shown in the top part of Table 1, the scores for the top 3 finishing students for the high school in 2017 were very similar to those in 2016. Specifically, the top 2 scores in 2017 were slightly lower than those in 2016, by 4 points and 16 points, while the third place score for 2017 was 20 points higher than the previous year. In addition, a noticeable improvement was clearly observed for a particular student who participated in the District level competition in both 2016 and 2017. The student scored 40 points and placed second in 2016, and scored 90 points (a 125% increase) and placed first in 2017. (St2 in 2016 and St1 in 2017 in Table 1). Overall these results do not represent the broader and clearer improvement in scores for the individual part of the competition that was observed when the course was taught previously as reported in [1]. However, the basis for comparison, the top 3 scores for the individual part of the competition in 2016, was notably higher this time than the basis of comparison for the previous time the class was taught. This could indicate that whenever the course is taught, adjustments might be needed to tailor its contents. For example, the specific topics to be covered, the level at which they are covered, or some other aspect of the course might need to be adjusted to better meet the background of the anticipated students. The results of the team part of the 2017 District competition are shown in the bottom part of Table 1, along with results from the previous two years. The score received by the high school team in 2017 was much higher than that of previous recent years. For the 2016 team competition score, much of that difference was caused by the high school team having decided not to participate in the programming part of the team competition that year. However, the high school did participate in both parts of the team competition in 2015, and the 2017 team score was still much higher. Though no clear conclusions can be drawn, participation in the computer

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Table 1. Results from District level of UIL CS competition. Year 2015

Year 2016

Year 2017

Student

Score

Place

Student

Score

Place

Student

Score

Place

St1

50

1

St1

94

1

St1

90

1

Individual Results

St2

6

3

St2

40

2

St2

24

3

St3

0

5

St3

4

5

St3

24

4

Team Results

Written

56

Written

138

Written

138

Program ming

60

Program ming

0

Programm ing

257

Total

116

1

science course could help students become more comfortable with coding, encouraging them to attempt more problems than they otherwise might in the team programming part of the competition. This is an encouraging result, in particular, considering the fact that the participation rate for the programming competition is generally much lower than the one for the written test competition as shown in Table 3.

Score

To further study potential benefits of the computer science course at the individual student level, their performance on a test that was taken just before going through the computer science course was compared to their performance on a comparable test taken after the course. In this case, just before attending the computer science course the high school students involved participated in an Invitational UIL Computer Science competition. Invitational meets provide a practice opportunity for students planning to participate in the District level competition. Figure 1 shows for each of the top 3 scoring students, their performance at the Invitational competition (before the computer science course) compared to their performance at the 2017 District competition (after the computer science course). As can be seen in Figure 1, the individual scores at the District meet

Figure 1. Individual student performance at Invitational Competition and District Level Competition (2017).

138

1

395

1

were higher for all three students. Specifically, the first student’s score at the District meet rose by 12 points from the Invitational competition, the second student’s score rose by 18 points, and the third student’s score rose by 30 points.

4

Further Discussion of Participation

For purposes of UIL competitions, the state of Texas is organized into 4 different Regions with 32 Districts defined within them. The competition is organized so that high schools of similar size (number of students) compete with one another. School sizes range from 1A (the smallest) to 6A (the largest). The high school that was involved with this project was a size 3A school (220-464 students) located in District 30 of Region 4 in 2016, and in District 31 in 2017. 4.1

Participation Rate in STEM Subject Areas

There are four top level categories of UIL Academic competitions: STEM, Theatre and Film, Journalism, and Speech and Debate. Computer Science is one of the STEM subject areas along with: Calculator Applications, Mathematics, Number Sense, and Science. Table 2 summarizes the results of the participation of all of the size 3A high schools (11 in total) located in District 30 and District 31 for each of the STEM related subject areas for which UIL competitions are offered that took place during the 2015 and 2016 school years. As indicated in the second column of Table 2, Computer Science had the lowest level of participation (63.64%) of any of the STEM topic areas with only 7 of the 11 high schools in District 30 and District 31 choosing to compete. The third column of Table 2 indicates for each of the STEM related subject areas the highest score that was received by a competitor in the individual category of competition. Computer Science had the lowest individual high score at a level of 94. The highest score received in the team category of competition is also shown in Table 2 for all participating high schools. Again computer science had the lowest high score at a level of 138 where the high score levels for the other STEM topic areas were considerably higher. Having both the lowest “Highest Individual Score” and lowest

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Int'l Conf. Frontiers in Education: CS and CE | FECS'17 | Table 2. Participation in UIL STEM subject for 3A High Schools in Districts 30 and 31. STEM Subject

Participation Rate

Calculator Applications Computer Science Mathematics Number Sense Science

Highest Individual Score (out of 240)

81.81% (9/11) 63.63% (7/11) 81.81% (9/11) 72.72% (8/11) 72.72% (8/11)

197 94 192 116 124

“Highest Team Score” is likely an indicator of a lack of support for teaching computer science topics in the District 30 and District 31 size 3A high schools. Without the opportunity to learn about these subjects, students likely do not feel adequately prepared. Not feeling comfortable with the material, students are less likely to choose to compete in the computer science competition, reducing the overall participation rate.

4.2

Participation Rate in both Written and Programming in CS Competition

The second type of participation that was examined focused on the number of high schools that participated only in the written competition in comparison to the number that participate in the programming parts of the computer science competition. Table 3 summarizes the results of a comparison of both competitions for all of the size 3A high schools located in District 30 and District 31 for the 2015 and 2016 school years. In 2015, 57% of high schools participated in the individual category of the written competition while only 14% took part in the programming competition. In 2016, 71% participated in the individual part of the competition and none participated in the team category. The two year average of schools participating in the written category of the competition was 63% while the average for participation in the programming test was 9%. As reflected in these results, students from these schools are likely not receiving the opportunity to learn about fundamental programming topics, and are not being given the chance to practice even basic programming skills. Students who don’t feel comfortable with their level of programming knowledge are less likely to participate in that part of the computer science competition.

4.3

Enhancing Participation Increasing student awareness of and participation in

Table 3. Participation in UIL CS written and programming for 3A High Schools in Districts 30 and 31. Written Test

Programming Test

Year

2015

2016

2015

2016

Participation Rate

57.14

71.42%

14.29%

0%

63.63% (7/11)

9.09% (1/11)

Highest Team Score (Sum of three best individual scores: out of 720) 478 138 414 273 274

computer science is an important part of the ongoing project. In support of those efforts, a prototype online Java practice programming environment has recently been developed (http://www.uilcs.org/). It is especially targeted at students preparing to participate in the programming part of the UIL computer science competition. The system is specifically designed to provide an easier and more convenient environment for students to practice programming on their own schedule with the devices that they use most often. It is anticipated this will help improve student performance, increase interest and lead to broader participation in the computer science discipline, especially among underrepresented groups.

5

Conclusion and Future Work

The focus of the ongoing project reported in this paper is to increase awareness, interest, and participation in computer science and related programs among high school students, especially in public schools where resources are limited. A computer science course designed to help students prepare for a popular programming competition was taught to high school students. The effect on student performance was analyzed and compared to that observed when the course has been taught previously. The results seem to indicate the course had a beneficial effect on the students’ team programming performance. Tailoring the course to match the background of anticipated students is expected to increase the beneficial effect for individual student performance. A survey of the participation rate of regional high schools in the UIL Computer Science competition reveals that few schools participate in the team programming category. An online Java practice programming environment has recently been developed and will be deployed in an attempt to improve student programming performance and participation in the programming part of the competition.

References [1] David Hicks and Jeong Yang, “Leveraging Interscholastic Competition in Computer Science Education,” The 12th International Conference on Frontiers in Education: Computer Science and Computer Engineering (FECS'16), 2016. [2] Jeong Yang, Young Lee, Sung Park, Monica Wong-Ratcliff, Reza Ahanger, and Marie-Anne Mundy, “Discovering the Needs Assessment of Certified STEM Teachers for the HighNeed Schools in South Texas,” Journal of STEM Education: Innovations and Research, Volume 16, Issue 4, pp. 40-45, 2015.

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[3] Sid W. Richardson Foundation Forum (2012). 51 math and science teachers in Texas: Motivating, preparing, supporting, and retaining Math and Science Teachers in Texas high schools. Retrieved from http:// www.sidrichardson.org/pdf/forumreports/51Math-Science-Teachers-In-Texas.pdf. [4] Texas Comptroller of Public Accounts (2008). Texas in Focus: South Texas. Retrieved from http://www. window.state.tx.us/specialrpt/tif/southtexas/index.html.

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An Analysis of Student Success in Math, Physics, and Introductory Civil Engineering Courses Jaime Raigoza Computer Science Department California State University, Chico Chico, USA 95929

Abstract— Much research exists on the need of remedial education and how it affects a student’s first two years of college education. Studies have shown a problem exists on retention rates from the inability to succeed in math and physics courses required for many engineering majors. Colleges need to address how to best design their course curriculum considering students needing remedial courses, especially in math. The problem is difficult to understand due to the existence of many possible factors and metrics. Factors include the student’s grades, the grade distribution of courses, how often students are repeating failed courses, graduation success rates, the time duration to graduate, and many others. The differences between majors with respect to their different course curriculums further complicates the problem to understand a solution. For example, the pathway to success for Civil Engineering students is not only through math and physics coursework but, students are also required to take introductory courses in statics, strength of materials, and fluid mechanics. The goal of this paper is to extend the understanding on how Civil Engineering students make progress through their first twoyears by analyzing 10 years of course enrollment data. The success in math, physics, and Civil Engineering courses are analyzed. The correlation between non-transfer and transfer students is considered as well as the frequency that students repeat courses. Analysis is done by following the students until graduation. An evaluation of student outcomes such as grades earned is also performed to determine the best predictor of graduation success. With a better understanding of where students struggle, college instructors and administrators can make informed decisions that improve student success. Keywords- Civil Engineering; remedial math; student success; retention rates

I. INTRODUCTION The cost of remedial education in U.S. colleges is a serious problem and a topic of much discussion. Estimates indicate that the yearly U.S. cost is $1.3 billion and at the state-level, California incurs the highest at $205 million per year (Jimenez, 2016). Colleges are incurring the cost burden for the students to catch-up on course material that should have been completed at high schools. Enrollment rates in remedial math is estimated at 26% at two-year colleges, 18% at four-year institutions, and 4% for higher level research institutions (Jimenez, 2016).

Unfortunately, studies further show that remedial courses are the best predictor of a student not graduating (Terry, 2007). For example, from a cohort study of over 125,000 students by the Texas Higher Education Coordinating Board in 2010 on two-year college students, 76% of the students enrolled in remedial math. Of these students, only 33% passed the remedial courses, and 18% went on to complete their first college-level course. The problem is further impacted by the time needed to graduate. A study by Armstrong, J. & Zaback, K. (2014) shows that only 28% of the full-time students who were placed in remedial courses completed their four-year degree within 6 years compared to 47% of the non-remedial students given the same degree, also within 6 years. The goal of this paper is to perform a data analysis on course enrollment data taken by Civil Engineering majors within their first two years at California State University, Chico. The data spans a period of ten years between the Fall 2006 semester through the Spring 2016 semester. The work is unique such that the course data includes subjects from three Civil Engineering courses, remedial math, calculus, and physics. In addition to studying the success, the flow of students’ progress is measured, and whether the students graduated. Other aspects analyzed include the grade distribution and an understanding on how students transition between majors. College instructors and administrators have ongoing demands to make decisions to improve student success. By gaining a better understanding on how the students make progress through their course work towards graduation, they are better able to make informed decisions on areas that may or may not require changes. For example, if the student population is required to take remedial math courses such as Trigonometry, and the students eventually graduate, then, it may be possible to make curriculum changes to ensure these students graduate in a timely manner. II.

RELATED WORK

The first two years of a college education are critical to the success of a student being able to graduate. Related work has attempted to better understand this challenge. The readySTEMgo project is a collaboration of six universities who study the fundamental skills required of engineering students for success (Pinxten, 2016). Their work showed that prior math grades, gender, and the previous school were

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related to better test scores during a student’s first college year. Pinxten (2016) found a statistical significance of female engineering students outperforming male students in engineering courses. The analysis by Froyd et al. (2015) on over 14,000 undergraduate students at Texas A&M showed that female students progressed through the four-course math series at about an 8% higher rate than their male counterparts. The study by Pinxten (2016) also found that other factors such as time management skills, motivation and test taking strategies did have a moderate relationship towards predicting success in the first year. The analysis by Froyd et al. (2015) over the four semester math course series that engineering majors take indicate that the grades students receive in their first Calculus I course has a definite influence on their progress. Students who earned an A grade were 80% likely to complete the math series. The success rate dropped to 60%-70% for students earning a B grade in Calculus I and 40%-50% for students earning a C grade. The work by Froyd et al. (2015) suggests that the Calculus I course in a math series is a key predictor of retention and graduation rates in engineering majors. Students are strongly advised to enter Calculus I well prepared by mastering the necessary remedial math courses to increase their success. Similar results were concluded by Pembridge and Verleger (2013) who analyzed success in Calculus I given different student load scenarios and conclude that students need to be advised to avoid a high course load when taking Calculus I. Students should be placed in the appropriate math course based on his/her skill level. This is often difficult because students are compelled to overload their course work due to scholarship conditions and to reduce the tuition costs of an extended graduation date. Given linear algebra and calculus course data, Dekker, Pechenizkiy, & Vleeshouwers, (2009) built a decision tree classifier model to predict the dropout rate of freshmen with accuracies between 75% to 80%. With an accurate model of prediction, students were better advised to pursue a field of study. Additionally, the department was better able to allocate resources to aid students. The work by Dekker et al. (2009) also found that the grades for linear algebra and calculus classes were strong predictors of success in the electrical engineering curriculum. Work by Pembridge et al. (2013), McKenna et al. (200), and by Budny et al. (1997) conclude that there exists a correlation between undergraduate courses in math and physics to academic success in engineering majors. Budny et al. (1997) further state to treat these courses as high risk, rather than the students, and to provide additional help to the academically disadvantaged students so they acquire the critical skills needed to continue in engineering.

III.

METHODOLOGY

The approach taken in this study was to follow the success of students rather than simply counting the percentage of pass and fails. Students were assigned unique pseudo IDs. The

9

actual student IDs were not used for privacy protection. Hence, the school database administrator ran all the actual student IDs through a hashing function to produce a pseudo student ID number. The pseudo student ID number was essential to follow each student through the Civil Engineering curriculum. To follow the progress of each student through the curriculum, Python scripts were created. A reason the analysis was easier done via code was that the analysis required maintaining statistics on the distribution of courses being repeated with respect to each student, and to calculate the graduation percentages. The contents in Table 1 shows the courses from which student enrollment data was analyzed. Three early math or remedial courses were included in the study, as well as the required math and physics courses. Key introductory courses taken by students in the Civil Engineering curriculum in addition to the required math and physics courses are listed.

Table 1. Courses from which enrollment data was analyzed

Course ID

Course Description

MATH 51 (M51)

Intermediate Algebra

MATH 118 (M18)

Trigonometry

MATH 119 (M19)

Pre-Calculus Mathematics

MATH 120 (M20)

Analytic Geometry and Calculus I

MATH 121 (M21)

Analytic Geometry and Calculus II

PHYS 4A (P4A)

Mechanics

PHYS 4B (P4B)

Electricity and Magnetism

CIVL 211

Statics

CIVL 311

Strength of Materials

CIVL 321

Fluid Mechanics

IV. RESULTS The server type used to execute the experiments consisted of a Windows 10 Home 64-bit with an Intel Core i7 [email protected] GHz and 16GB Dual-Channel DDR3@798MHz. The experiments were executed on VMWare version 12 Pro platform with the Ubuntu operating system version 16.04LTS. The version of Python was 3.4. The virtual server was configured with 2GB of memory. Civil Engineering introductory courses The data below is based on the time between Fall 2006 through Spring 2016. A total of 20 15-week semesters. Because CIVL 211 is a pre-requisite of both, CIVL 311 and

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CIVL 321, the input data was staggered. In other words, the Spring 2006 data was only applied to CIVL 211. Then, for the following Spring 2007 semester until Fall 2015, all course data was analyzed. And for the last semester, Spring 2016, CIVL 211 was not included. Table 2 includes a high-level distribution summary of the student population analyzed. The likely reason more students enroll in CIVL 311 compared to CIVL 211 is due to transfer students entering the curriculum. During the same 10-year period, 606 students graduated in Civil Engineering. Thus, assuming the same graduation rate, less than 55% (606 / 1104) of the students who enroll in CIVL 211 eventually graduate. The reason the percentage is less is due to transfer students entering later into the Civil Engineering curriculum who are potentially part of the 606 graduates. Future work is expected to fully consider the impact of transfer students. Table 2. Student distribution of Civil Engineering courses Course ID

# Students Enrolled

# Students Passed

Fail

CIVL 211

1104

1028

76

CIVL 311

1266

1193

73

CIVL 321

1060

892

168

Students (Passed CIVL 211)

Transfer Students (non-CIVL 211)

Enrolled in CIVL 311 and CIVL 321

749

218

Passed both CIVL 311 and CIVL 321

632

182

84%

83%

How often do students repeat failed courses?

Table 3. Progress from CIVL 211 to CIVL 311/321 Course ID

Table 4. Success comparison between non-transfer and transfer students

Success

Next, of the students who passed the CIVL 211 course and continued to either CIVL 311 and/or CIVL 321 were analyzed as shown in Table 3. Both of these courses could be taken concurrently. The “Do not Enroll” values represent the number of students who passed CIVL 211 and dropped from the Civil Engineering curriculum. Given a passing grade in CIVL 211, 80 students do not continue to CIVL 311 compared to 251 students not continuing on to CIVL 321. One possible argument could be that more students enroll and fail in CIVL 311 after taking CIVL 211. This is partially true because CIVL 311 has a higher enrollment. However, since only 47 students fail CIVL 311, a greater number of students are still not enrolling in CIVL 321. The table also shows the performance of the transfer students, which are the students who took an equivalent CIVL 211 course most likely from a two-year college. Transfer Students Enrolled (non – CIVL 211)

Students Enrolled (Passed CIVL 211)

Now, let’s compare the success performance between the students who passed CIVL 211 and the transfer students. The results shown in Table 4 indicate almost no difference between the two student classifications.

Total Enroll

Pass

Fail

Do not Enroll

Total Enroll

Pass

Fail

CIVL 311

948

901

47

87

325

289

36

CIVL 321

777

654

123

251

287

237

50

The frequency of the students repeating failed courses was measured for all three courses, CIVL 211, CIVL 311, and CIVL 321. Based on department needs, it was also necessary to determine the graduation rates of the students who tended to repeat the courses. The values for the “# of students” in Table 5 are non-cumulative such that the sum of students for each course equals the “# Students Enrolled” values from Table 2. Thus, the table can be read as “80 students or 86% of the students who took the CIVL 211 course, were able to pass on their second attempt. Of these 80 students, 66% eventually graduated but not necessarily with a Civil Engineering degree”. This implies that all though the 80 students failed CIVL 211 on their first attempt, they are not part of the first attempt failure metric. The results show that putting a cap of allowing courses to be repeated twice would have an insignificant impact. It also shows that far more students leave the Civil Engineering curriculum after initially failing CIVL 321 compared to CIVL 311, 143 students vs. 53 students, respectively. It is of interest to note that during the study subsequent analysis tended to generate more questions. For example, given the repetition analysis, are the same students repeating the courses? Future work is expected to address this question. The results also indicate that the failure rate for CIVL 321 is higher. The right-most column indicates the likelihood of students graduating from Chico State, regardless of their major.

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Table 5. Course repetition analysis Course ID

# Attempts 1 2

CIVL 211

3 4 5

1 2 CIVL 311

3 4 5

1 2 CIVL 321

3 4 5

Outcome

11

data in chronological order, it is possible to determine any changes to students declared major based on the courses taken. # of Students

% Grad

Pass

933 (94%)

79%

Fail

59 (6%)

37%

Pass

80 (86%)

66%

Fail

13 (14%)

23%

Pass

12 (75%)

67%

Fail

4 (25%)

25%

Pass

1 (100%)

100%

Fail

0 (0%)

NA

Pass

2 (100%)

100%

Fail

0 (0%)

NA

Pass

1089 (95%)

83%

Fail

53 (5%)

17%

Pass

92 (84%)

80%

Fail

18 (16%)

33%

Pass

11 (92%)

100%

Fail

1 (8%)

0%

Pass

1 (50%

100%

Fail

1 (50%)

0%

Pass

0 (0%)

NA

Fail

0 (0%)

NA

Pass

844 (86%)

81%

Fail

143 (14%)

62%

Pass

43 (72%)

84%

Fail

17 (28%)

47%

Pass

5 (45%)

60%

Fail

6 (55%)

50%

Pass

0 (0%)

NA

Fail

1 (100%)

0%

Pass

0 (0%)

NA

Fail

1 (100%)

0%

Are there any patterns from students changing majors into and out of Civil Engineering? At CSU, Chico, it is fairly easy for students to change their declared major. It was of interest to determine the patterns of these changes and when were these changes being made. Since the course enrollment data contained the declared major of the student at the time the course was taken, it was possible to study patterns in major changes. For example, by sorting the

The first set of experiments consisted of first determining the Civil Engineering graduates within the Fall 2011 through Spring 2016 semesters. There was a total of 315. Based on these graduates, the next step was to determine what their declared major was upon taking a given course. The course data was taken from the full 10-year period between the Fall 2006 and Spring 2016 semesters. This way, if a student graduated in the Fall 2011 semester, 5 years of prior data for this student was available. The results are shown in Table 6. The next set of experiments was to study possible patterns of students transferring out of Civil Engineering. This was accomplished by first determining all the students who took a given course as a declared Civil Engineering major. Then from these students, it was determined if the student graduated as a Civil Engineering major, or possibly if the student did not graduate. Table 6 contains the results.

Table 6. Transition between majors

Enrollment

Student Course Enrollment M51

M18

M19

Total Grads

12

16

49

# Grads declared as a CIVL major

2

5

30

# Grads declared as a nonCIVL major

10

11

19

# Students declared as a CIVL major

23

38

97

# Students who graduated as a CIVL major

1

6

29

# Students who never graduated

11

20

36

# Students who graduated in a non-CIVL major

11

12

32

Table 7 and Table 8 contain more detailed results pertaining to the majors that Civil Engineering students transitioned into and out from. To better focus on the more common transition patterns, the entries having a value of 1 for the “# of Students” were removed. The values under the “Total” column matches the values from Table 6.

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Table 6. (Continued) Student Course Enrollment

Enrollment

M20

M21

P4A

P4B

Total Grads

167

181

203

216

# Grads declared as a CIVL major

135

166

185

198

# Grads declared as a non-CIVL major

32

15

18

18

# Students declared as a CIVL major

282

267

261

256

# Students who graduated as a CIVL major

135

177

178

219

# Students who never graduated

74

37

35

17

# Students who graduated in a non-CIVL major

73

53

48

20

The results show that most of the incoming students are Undeclared. Also, the most common time students transfer in is after taking Math 120 or Calculus I.

Most of the students who transition out of the Civil Engineering major change to Construction Management. This is a trend the department suspected. Business administration is the next most popular major students transition to. Table 8. Majors that Civil Engineering majors transition to Course ID

Total

MATH 51

11

MATH 118

12

MATH 119

32

MATH 120

73

MATH 121

53

PHYS 4A

48

PHYS 4B

20

Table 7. Majors prior to becoming a Civil Engineering major Course ID

Total

# of Students

Major

MATH 51

10

Business Admin.

2

MATH 118

11

Undeclared

3

MATH 119

19

Business Admin. Undeclared Construction Mgmt. Elect. / Electronic Eng. Mechanical Eng. Business Admin. Undeclared Business Admin. Undeclared Elect. / Electronic Eng. Business Admin. Undeclared GNED Sustainability Spanish Undeclared

2 6 4 3 4 2 10 2 4 3 2 4 4 2 2

MATH 120

32

MATH 121

15

PHYS 4A

18

PHYS 4B

18

Major

# of Students

Construction Mgmt.

4

Business Admin. Construction Mgmt. Business Admin. Construction Mgmt. Business Admin. Concrete Ind. Mgmt. Construction Mgmt. Elect. / Electronic Eng. Envrn. Sci: Hydrology Exercise Physiology Mechanical Eng. Sustainable Mfg. Business Admin. Concrete Ind. Mgmt. Construction Mgmt. Envrn. Sci: Hydrology Exercise Physiology Math Mechanical Eng. Mechatronic Eng. Physics Business Admin. Concrete Ind. Mgmt. Construction Mgmt. Exercise Physiology Mechanical Eng. Physics

2 5 3 10 8 3 13 2 2 3 6 3 7 2 6 2 3 2 5 2 2 5 2 8 3 3 2

Mechanical Eng.

2

Time to graduate The following table shows the average number of semesters that Civil Engineering students spent at CSU, Chico based on his/her first semester at Chico State up until his/her graduation semester. The first step was to determine the list of students who graduated between the Fall 2011 and Spring 2016 semesters. Then, from these students, the number of students who took the given math and/or physics course were counted as well as the average number of semesters attended by the students. The results are shown in Table 9. The Summer term was considered equivalent to the prior Spring semester when calculating the number of semesters.

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The results confirm that a pathway to earning a Civil Engineering degree exists through taking early math course(s) in algebra, trigonometry, and pre-calculus. The time to graduate on average is between 5-6 years from the time they start their coursework at Chico State. It is important to note that the time to graduate is most likely longer for the non-early math courses because the data in Table 9 includes transfer students. For example, transfer students may only need MATH 121 (Calculus II course) and complete her degree in 3 years. Table 9. Time to graduate for Civil Engineering students based on courses taken Course ID

# Students

Avg. # of Semesters

13

Table 10. Grade distribution of Civil Engineering students Student Course Enrollment Grade

M51

M18

M19

M20

M21

P4A

P4B

A

3

5

12

12

10

24

9

A-

0

1

1

16

8

8

8

B+

0

2

6

12

14

10

16

B

2

1

7

26

25

46

20

B-

0

2

3

15

19

11

21

C+

0

1

4

21

18

17

22

C

2

3

7

35

41

53

43

C-

0

1

3

14

30

19

33

D+

0

0

1

1

3

1

18

D

0

0

2

3

2

2

10

MATH 51

5

11.60

MATH 118

12

10.67

MATH 119

46

10.87

F

0

0

1

0

1

1

2

MATH 120

159

10.21

CR

2

0

0

0

0

0

0

MATH 121

178

9.97

W

0

0

0

1

0

0

0

PHYS 204A

200

9.92

WU

0

0

0

1

0

0

1

PHYS 204B

213

9.71 TOTAL

9

16

47

157

171

192

203

3.14

3.04

2.81

2.66

2.47

2.64

2.28

F11-S16 Grads

GPA

315

Since the measured amounts include non-transfer and transfer students, it is not be fair to compare the time to graduate between courses. For example, a student who took MATH 118 may have started as a Freshman at Chico State and a student who started at MATH 121 may have transferred in most of his math. Hence, the original objective goal was to measure the time to graduate as a Chico State student thus, non-transfer and transfer students are treated similarly. Further work will be done to split the analyzed data between the nontransfer and transfer students. Grade Distribution Analysis The next set of experiments were done to evaluate the distribution of grades across different courses. Again, the students who graduated during the 5-year period between the Fall 2011 and Spring 2016 semesters were evaluated. The total amount was 315 students. Then, given these students, the courses these students took during the 10-year period between Fall 2006 through Spring 2016 was determined as shown in Table 10. The results were summarized using a weighted GPA formula. From Table 10, it is possible to infer the number of transfers entering the Civil Engineering curriculum. For example, given that MATH 120 is a required course and only 157 students took this course, it is possible to deduce that 158 (315 -157) or 50% of the students transferred in. Table 11 shows the grade distribution for all university-wide students between the Fall 2006 through Spring 2016 semesters.

Table 11. Grade distribution from all university-wide students Student Course Enrollment Grade

M51

M18

M19

M20

M21

P4A

P4B

A

786

222

230

319

222

247

134

A-

0

121

149

188

124

97

81

B+

0

128

187

207

160

113

118

B

1355

193

322

409

286

325

187

B-

0

145

228

251

254

141

163

C+

0

98

206

258

240

210

226

C

1801

245

399

559

418

445

337

C-

0

111

220

339

300

190

189

D+

0

26

56

48

28

27

108

D

0

124

167

232

126

96

128

F

0

195

249

326

259

155

68

CR

71

19

5

1

2

2

1

W

66

55

57

88

56

48

20

WU

0

65

110

159

88

52

35

TOTAL

4079

1747

2585

3384

2563

2148

1795

GPA

2.74

2.36

2.30

2.28

2.27

2.40

2.31

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Now that we evaluated difference between courses, let’s compare between the Civil Engineering majors and all majors, independent on whether the student graduated. As shown in Table 12, Civil Engineering majors outperform other majors the strongest in Calculus I compared to Calculus II and the Physics courses. Table 12. Grade distribution comparison between Civil Engineering and University-wide students Student Course Enrollment Major

M51

M18

M19

M20

M21

P4A

P4B

CIVL

3.14

3.04

2.81

2.66

2.47

2.64

2.28

ALL

2.74

2.36

2.30

2.28

2.27

2.40

2.31

DIFF

0.40

0.69

0.51

0.38

0.20

0.24

-0.02

V.

CONCLUSION AND FUTURE WORK

A deeper understanding on the students’ progress through the math and physics courses as well as the introductory courses in Civil Engineering was gained, such as determining the success performance metrics between courses and that the success between non-transfer and transfer students were similar. The changes in the student’s major into and out of Civil Engineering was understood better. And, based on the grade distribution analysis, it was found that roughly half of the students who graduated in Civil Engineering transferred in the required Calculus I course most likely from a two-year college.

REFERENCES Armstrong, J. & Zaback, K. (2014). College Completion Rates and Remedial Education Outcomes for Institutions in Appalachian States. State Higher Education Executive Officers Association. Washington: Appalachian Regional Commission. Budny, D., Bjedov, G., & EeBold, W. (1997). Assessment of the Impact of the Freshman Enineering Courses. 1997 Frontiers in Education Conference. Dekker, G., Pechenizkiy, M., & Vleeshouwers, J. (2009). Predicting Students Drop Out: A Case Study. Proceedings of the 2nd International Conference on Educational Data Mining - EDM 2009, Cordoba, Spain, pp. 41-50. Froyd, J. E., Shryock, K. J., Tripathy, M., Srinivasa, A. R., and Simon, R. C. (2015). Patterns of Students’ Success: How Engineering Students Progress Through a Course Sequence. Presented at Persistence and Retention. 122nd American Society for Engineering Education (ASEE) Annual Conference & Exposition. Seattle, WA, June 14-17, 2015. Jimenez, L., Sargrad, S., Morales, J., & Thompson, M. (2016). Remdial Eduction: The Cost of Catching Up. Washington DC. Center for American Progress. McKenna, A, McMartin, F. & Agogino, A. (2000). What students say about learning physics, math, and engineering. 30th Annual Frontiers in Education Conference, 2000. FIE 2000. Pembridge, J. J. & Verleger, M. A. (2013). First-Year Math and Physics Courses and their Role in Predicting Academic Success in Subsequent Courses. 120nd American Society for Engineering Education (ASEE) Annual Conference & Exposition, Atlanta, GA, June 23, 2013. Pinxten, M. & Hockicko, P. (2016). Predicting study success of first-year Science and Engineering students at the University of Zilina. IEEE ELEKTRO, Strbske Pleso, 2016, pp. 18-23. Terry, Brooke D. (2007). The cost of remedial education. Policy Perspective. Austin TX: Center for Education Policy. Texas Public Policy Foundation.

The time to graduate reporting can be extended by considering the grade students earned. The intention is to determine a correlation between success in a major and the time needed to graduate. Also, in order to more clearly analyze the time to graduate, the non-transfer and transfer students need to be analyzed separately. More data will be requested and analyzed to determine what other engineering majors, and their distribution, are enrolling in the early math courses. A comparison with students from Underrepresented Minority (URM) groups will also be studied. An URM group refers to students classified as women, Hispanic/Mexican-American, African-American, and Native-American. VI.

ACKNOWLEDGEMENTS

The author would like to convey his sincere gratitude to both Dr. Melody Stapleton and Dr. Steffen Mehl for their full support, motivation, and guidance. Their encouragement and feedback helped focus the data analysis work to better formulate the results. It was a pleasant experience working with their patience on this work.

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Effective Techniques for Student Engagement in Introductory Computing Courses S. Hussain1, S. Morgan2, and Z. Haque3 1 Computer Science, Fisk University, Nashville, TN, USA 2 Physics, Fisk University, Nashville, TN, USA 3 Mathematics, Fisk University, Nashville, TN, USA

Abstract – We propose a four-objective plan to innovate the curriculum and the pedagogies for teaching and learning of computer science (CS) in order to create awareness, interest and success in CS as a discipline. The four objectives are as follows: a) Computational Thinking in CS courses for nonmajors; b) Appealing Tools in Introductory Courses; c) Computing in Cognate Courses (Mathematics and Physics); and d) Utilize peer mentoring/coaching to foster deeper learning in related courses and to serve as ‘coaches’ in course-assigned projects. Due to these changes, the enrollment in CS increased by around 250% and students obtained competitive internships and job opportunities (e.g., Google, Microsoft, Apple, and Amazon). Students participated in hackathons and research conferences. Keywords: Computational thinking, student engagement, pedagogy, peer mentoring.

1

Introduction In this work, which is partially sponsored by NSF Grant# 1332432, we address the national problem of insufficient number of students enrolled in computing related disciplines. The U.S. Department of Labor predicts that there will be more than 1.4 million computing jobs available by 2020 [1]. Despite the increasing number of computing jobs, interest in these majors and careers has steadily declined over the past decade. Fewer students are enrolling in computer science and graduating with computer science degrees. If current trends continue, only 30% of these 1.4 million computing jobs could be filled by U.S. computing gradates by 2020 [1]. Further, the decline situation in women is even more significant. In 2010, women earned 57% of all bachelor’s degrees, yet they only earned 18% of computer and information science bachelor’s degrees, which is down from 37% in 1985 [1]. Moreover, there is 79% decline in the number of first-year undergraduate women interested in majoring in Computer Science between 2000 and 2011 [1]. Finally, it should be noted that the low percentage of women in IT industry has a

significant impact on innovation and productivity. Perhaps the lack of women and minorities in CS is due to ‘stereotype threat’ [6], where students of color or women underperform academically because they harbor low expectations projected upon them by society and their previous instructors, particularly apparent in the area of Science Technology Engineering and Mathematics (STEM). Students who believe they will not excel in STEM develop behaviors that facilitate this outcome. Perhaps this explains why more than 50% of all African American professionals are graduates of HBCUs, where a nurturing and ‘safe’ cultural environment occurs as the backdrop for learning. Though HBCUs represent just 4% of US Universities, HBCUSs confer nearly 35% of all Bachelor’s degrees to African-Americans in CS [3]. Cognitive researcher DiSessa postulates that our youth must have computational literacy to be active participants in the contemporary workforce [5]. Although computer literacy is more microscopic in concept, computational literacy is much more infrastructural. Computational literacy could be more likened to the ability to interact and develop systems, be them physical or procedural, to enhance one’s life in the workplace, classroom, or life’s challenges. DiSessa would suggest that computer technology can be a technical foundation of a new and dramatically enhanced type of literacy that will be as indepth as and have as much influence as the emergence of text based literacy [5]. Others suggest that computational abilities are essential to being full participants in citizenship [7]. Many students who fall behind academically in mathematics and related curricula become counted out of society even before they leave school. Thus, it becomes essential for universities to equip its graduates with positive learning experiences that will enhance their computational abilities. The national retention rate for computer science majors is only 20.6%, which is very low as compared to other majors: 56.7% Life Sciences, 57.9% Physical Sciences, 54.6% Mathematics, 44.6% Engineering, and 50.7% Social Sciences [2]. According to PCAST [2], “Merely increasing retention from 40% to 50% would translate to an additional 72,500 STEM degrees per year,

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comprising almost three quarters of the 1 million additional STEM graduates needed over the next decade.” Most of the bright and potential students leave STEM programs after introductory courses because the courses are not very exciting or challenging [2]. Similarly, if students can successfully complete year one, their chances of degree completion are much higher [3]. Our four proposed strategies are based on the following two PCAST (2012, Executive Report, page x) recommendations: x “Recommendation 1: Catalyze widespread adoption of empirically validated teaching practices.” x “Recommendation 2: Advocate and provide support for replacing standard laboratory courses with discovery-based research courses.”

2

Methodology

Objective 1 - Computational Thinking in CS courses for nonmajors. Currently, non-major students have only one option (CS 100, 3 credit hours) to enhance their computation skills. However, there are several students, who are although proficient in general computing (e.g. word processing, presentations, computer organization, etc), would be interested in developing mobile apps and web applications. Due to free online tools and web services (e.g. Google App engine), it is possible for non-major students to develop web-based database applications. In addition to providing training for collaborative tools and cloud-based resources, the students are trained for computational thinking by developing mobile apps (AppInventor of MIT Media Lab) and writing scripts (Google’s App Script) in order to develop real life applications. Further, the concepts of word processing, data management, and decision support systems are provided for both PC-based (Windows and Mac) and web-based development environments (e.g., Google Drive). We introduce two new short courses (CS 106 – Mobile Apps and CS 108 – Web Apps, 1 credit hour each) that would be beneficial to all the students. In current practice, several students take electives of 1-2 credit hours from Music, Arts, and other disciplines in order to obtain well-rounded liberal arts education. Consequently, the students obtain the opportunity to receive computation thinking training and being equipped with general and exciting CS tools while having enough credit hours for their respective majors. CS 106 – Introduction to Mobile Apps (1 credit hour) is a new introductory course (1 credit hour) in mobile apps and it is designed for non-majors and there is no-prerequisite.

Students use graphical tools to develop their mobile apps. This course attracts computer proficient students of other majors (e.g. Biology and Chemistry), who are even though proficient in traditional computing tools (word processing, presentation, web tools, etc.), they can benefit in creating their own mobile apps. CS 108 – Introduction to Web Apps for (1 credit hour) is new introductory course (1 credit hour) for developing web apps. As web is the common platform for computers, smart phones, and various smart devices, it is beneficial to train students in developing web apps. The latest cloud-based services and collaborative tools are used in developing projects related to real life applications. Objective 2 – Introducing Appealing Tools in Introductory CS Courses. Overall, our goal is to take some of the computational tools and programming platforms that students will find ‘relevant’ and easily learned and focus on those in early courses in the CS curriculum pathway. Introducing visual programming and scripting languages in introductory CS courses on the pathway to the major is intended to effectively engage student interest in modern software development tools. In a pilot effort using this approach, freshmen students developed word games such as hangman, where they applied the principles of lists (arrays), lookup (linear search and binary search), sorting (selection sort, merge sort), shuffling, and animations (hangman costumes). Similarly, students used recursion to design graphical representation of fractals (trees and snowflakes), which would be very difficult to implement in traditional structures languages, such as C++. A significant literature affirms that integrating research into the curriculum stimulates greater student engagement, learning of context, and active integration of classroom knowledge into its confident use [8]. Thus, we provide a structure and funding to integrate research into students’ academic pathway in Computer Science. The goal of CS110, CS 120 and CS 241 is to foster critical thinking and an understanding of what various tools can and can’t do, while making the exercises stimulating, well-suited to group problem solving, and targeting a wide range of learning styles [4]. To test and improve this critical thinking, all of these courses have embedded projects in them, as part of the assignments that count to the final grade. Importantly, these changes in the way the courses are taught, and courseembedded projects in which students engage, allow participating students to behave as computer scientists, key to fostering their interest in this discipline for their career [9]. The expected outcome of these changes increase student continuation in each of the next courses in the curricular pathway. Summer internships are created to engage students in research problems whose results are presented in local, regional, and national competitive conferences for data dissemination and recognition of students’ research. Lopatto and others have provided data indicating that involving students in authentic research projects has a significant impact

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on underrepresented minorities, with a disproportionately greater positive impact on those currently underrepresented in the sciences, particularly African American males [10, 11]. Objective 3 – Computing in Cognate Courses At Fisk University, the term ‘cognates’ refers to courses in other disciplines that are required in an academic pathway for graduation as a major; for example, in Computer Science, the cognate courses include mathematics (through Calculus II) and College Physics, a calculus-based course. One manifestation that students have successfully achieved a level of critical thinking efficacy is their ability to take concepts learned in one course and apply them in another. Consequently, we assure that this application of fundamental knowledge in another setting is expected and mentored by enhancing the cognate courses identified above with computer programming assignments and laboratories to foster computational thinking. As mathematics and physics courses are required for CS-majors, we infuse computational thinking and programming in Math 101 (College Algebra) 110 (preCalculus, including trigonometry), Math 120 (Calculus I) and in Physics 130 and 140. This integration of CS into these math and physics courses fosters the application of CS in new contexts for CS majors as well as encourage the introduction and use of computational tools and thinking for non-CS majors. Objective 4 - Peer Mentors A large literature affirms the positive impact of peer mentors in student learning, and have the reciprocal positive impact of training advanced and well-prepared students for effective communication and leadership skills, which are extremely beneficial for a successful CS academic and professional career. Peer mentoring fosters a scholarly environment for deeper learning and meaningful real-life applications across the four-year continuum of a student’s undergraduate experience [12, 13, 14]. We offer a professional development and leadership workshop before each semester for these students who have been selected to serve as peer mentors. The purpose of the training is to assure that the students are confident in their explanations of computer programs and computational tools, and also to help them learn how to foster discussions where questions are not directly answered, but are answered with questions that help the students come to their own understanding. Peer mentors commit 10 hours a week in this activity, including attending the didactic sessions with students to be better prepared to serve as cognate project mentors. This strategy is vastly different from that in individual student tutoring, which is more answer-oriented. Furthermore, the student peer leader engages all students in the discussion, so that the source of answer can percolate up from anyone in the discussion.

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Results & Discussion Due to these strategies, there was significant increase in enrollment and interest in CS courses. 333% increase in CSCI 110 enrollment (fifty students) compared to average of 5 years before the intervention. Similarly, 233% increase in CSCI 120 enrollment (twenty students) and 250% increase in CSCI 241 enrollment (fourteen students). Similarly, students obtained competitive internships at Google (16), Apple (2), Microsoft (5), Deloitte (8), Cisco (1), IBM (2), Amazon (3), and other national and regional competitive internships; moreover, students obtained full-time job offers from Google, Microsoft, Apple, Amazon, Cisco, Deloitte, etc. It should be noted that prior 2012, no student obtained internships at Google, Microsoft, Apple, Amazon, etc, and no student obtained full-time job offers at these competitive companies. However, in the last five (5) years, students job placement in competitive computing careers significantly increased. In addition to prestigious professional careers, students presented their research work at NCUR, Tapia, BKX/NIS, and other regional and national conferences as well as at Annual Fisk Research Symposium. During grant period, additional computational physics exercises were included in the Physics 140 (University Physics II) curriculum. In 2015-16 academic year, the class had 14 students in the Fall semester, 40 in the Spring semester, most of whom were biology majors with a few physics, chemistry and CS majors. These exercises utilized MATLAB or GNU Octave. MATLAB is available in several Fisk computer labs, and links to GNU Octave downloads were provided for any students who wished to use their personal computers. A link for a web-based version of Octave (online http://octave-online.net/) was also provided. The first exercise was designed to introduce the students to the software and familiarize them with the MATLAB/Octave environment. Specific objectives of this assignment were entering commands into the command window and learning basic syntax, accessing 'help' and finding additional documentation, and creating simple scripts (.m files). Table 1 shows computing exercises developed for PHY 140. Work is continuing on the Marble Game exercise because of the number of computational techniques it incorporates, its relevance to physics, and its relevance to the life sciences [15]. The marble game is a simple game that has been used to provide a conceptual framework for understanding a wide variety of physiological processes. Normally the game is used in a classroom setting with marbles and dice. The marble game is a simple Monte Carlo simulation of molecular partitioning between two compartments. The kinetics of this simple system can be used to model transport and

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equilibrium for a wide variety of systems of physical and physiological interest including: membrane transport; diffusion; drug elimination; electrical conduction; osmosis; and ion channel gating. [http://circle4.com/biophysics].

consequently students success in academics and professional careers.

Exercise Exercise

Physics concepts

Computational concepts/techniques

Basic plotting Create plots of Create plots of several techniques several functions, label the axes and functions, annotate the graphs label the axes and annotate the graphs Kirchoff's laws Graphing fitting data

circuit analysis Concept of arrays (matrices), additional matrix operations. and Analysis of experimental data

Plotting through points

Math Concepts lines two

BMI calculation

Evaluating Piecewise function

Evaluating Trig functions

Use of Pythagorean formula of right triangle to find values of all trig functions Dot product of two vectors with any finite number of components Transformation of functions (Shifts, rotation, and reflection) Matrix Algebra (inverse & multiplication) Using difference quotient to find 1st and 2nd derivatives of a discrete function Newton’s recursive formula for root finding uses 1st derivative

Arrays, visual display of data, data fitting

Equipotentials and Electrostatics, Electric Field field and potential

Creating surface plots

Simple Differential Equations

Charging and discharging capacitors

Solving simple differential equations

Marble Game

Diffusion, electrical conduction

Loops, Intro to Monte Carlo techniques

Fermat's Principle

Refraction, Snell's Law

Data types, errors

Inventory management Moving objects Solving system of linear equations Derivatives of discrete function

Table 1. Computing Examples in Physics Courses The math assignments related to computational thinking were assigned to students of Dr. Haque in his Calculus I (Math 120) and Pre-Calculus (Math 110) sections. There were 22 students in Calculus I (Fall 2015), 42 in Pre-Calculus (Fall 2015), and 20 in Pre-Calculus (Spring 2016). Most of these students are Biology majors with few Physics, Chemistry, Math and CS majors. Developed assignments are aligned to the objectives of these particular courses but reinforce students to think computationally, especially use of indexing, arrays, repetitions, and conditional statements in managing and manipulating data. Students were encouraged to use any programming language however emphasis was given to MATLAB. Table 2 shows a few sample computing exercises in Mathematics courses.

4

Conclusions In summary, the proposed techniques show that effective curriculum modifications, course-embedded research projects, computing-based summer internships, computing in related courses (Mathematics & Physics), and peer mentoring support can provide significant increase in enrollment and student engagement for minority students in computing courses and

Slope and point slope form of straight line

Finding root of a differentiable function

Computational Thinking/techniques Creation of linear space (array, x) and use of vector operation in a scalar manner to find array y. Then plotting y vs. x Introduce conditional statement (if-else) and printf statement Use of variables (input and output) to evaluate expressions and print output Storing data in arrays and retrieving data from array to compute element wise product in a for loop Introduction of 2D array in matrix multiplication Interchanging two elements of a 2D array in computing inverse of a 2x2 matrix Creating arrays, storing values in an arrays and using these values in loop to compute derivatives at each point Unknown number of iterations require while loop and an appropriate stopping condition upon reaching a desired tolerance

Table 2. Computing Examples in Mathematics Courses

5

References

[1] NCWIT. (2012). By the Numbers," National Center for Women & Information Technology (NCWIT). [2] PCAST (2012). "President’s Council of Advisors on Science and Technology (PCAST) -- Engage to excel: producing one million additional college graduates with degrees in science, technology, engineering, and mathematics, Report to the President". [3] NAP (2011). "Members of the Committee on Underrepresented Groups and the Expansion of the Science and Engineering Workforce Pipeline. Expanding Underrepresented Minority Participation: America's Science and Technology Talent at the Crossroads.," The National Academies Press.

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[4] Breitzman, C. & Ashcraft, A. (2007). "Who Invents IT? An Analysis of Women’s Participation in Information Technology Patenting," National Center for Women & Information Technology, Boulder, CO. [5] DiSessa, A. (2001). Changing Minds: Computers, Learning, and Literacy. Boston: Massachusetts Institute of Technology [6] Steele, C. M. (1997). A Threat in the Air: How Stereotypes Shape Intellectual Identity and Performance. American Psychologist, 52(6), 613-629 [7] Stinson, D. W. (2004). Mathematics as “Gate-Keeper”: Three Theoretical Perspectives that Aim Toward Empowering All Children With a Key to the Gate. The Mathematics Educator, 14(1), 8–18. [8] Osborn, J. M. & K. K. Karukstis (2009). The benefits of undergraduate research, scholarship, and creative activity. In: M. Boyd and J. Wesemann (Eds.), Pages 41-53, Broadening Participation in Undergraduate Research: Fostering Excellence and Enhancing the Impact. Council on Undergraduate Research, Washington, DC. [9] Handelsman, J., Ebert-May, D., Beichner, R., Bruns, P., Chang, A., DeHaan, R., Gentile, J., Lauffer, S., Stewart, J., Tilghman, S., & Wood, W. (2004). Scientific teaching. Science, 304 (5670), 521-522. [10] Lopatto, D. (2010). Science in solution: The impact of undergraduate research on student learning. Tuscon, AZ: The Research Corporation for Science Advancement. [11] Boyd, M. K., & Wesemann, J. L. (2009). Broadening participation in undergraduate research: Fostering excellence and enhancing the impact. Washington, DC: Council on Undergraduate Research. [12] Chinn, D.; Martin, K.; & Spencer, C. (2007). Treisman workshops and student performance in CS. ACM SIGCSE Bulletin, 39(1):203–207. [13] Rodger, S.H.; & Huss-Lederman, S. (2007). PLTL in CS website, www.pltlcs.org. [14] Horwitz, S. & Rodger, S. (2009), Using Peer-Led Team Learning to Increase Participation and Success of Underrepresented Groups in Introductory Computer Science. Fortieth ACM Technical Symposium on Computer Science Education (SIGCSE 2009) [15] P. H. Nelson (2011). Teaching Physiology with the Marble Game FASEB J. 25, 481.5

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Co-Curricular Industry Experiences to Promote Student Success Meline Kevorkian #1 Greg Simco#2 #1

#2

College of Engineering and, Nova Southeastern University 3301 College Avenue, Fort Lauderdale, FL 33314, USA [email protected] College of Engineering and, Nova Southeastern University 3301 College Avenue, Fort Lauderdale, FL 33314, USA [email protected]

The 2017 World Congress in Computer Science, Computer Engineering, and Applied Computing Abstract—Attracting and retaining students, especially in the areas of science, technology, engineering and math (STEM) can be challenging, as the major prerequisite courses are often rigorous. Early exposure to industry may provide the motivation and assist in helping students relate coursework to the future employment goals. Complementing curricular activities with real world experiences can improve the balance between technical knowledge and soft skills. This work incorporated the use of co-curricular industry and career activities for computing majors at Nova Southeastern University as an effective means for student success. By engaging with a diverse set of industry partners, students are exposed to the direct correlation between their curricular and co-curricular activities and their real world application that fosters a pathway to success through retention, internships, and graduation.

expected to exhibit key attributes to ensure their success and the success of the engineering profession, according to the National Academy of Sciences in their groundbreaking 2004 report entitled The Engineer of 2020: Visions of Engineering in the New Century. These attributes are identified as strong analytical skills, practical ingenuity, creativity, communication, business and management, leadership, high ethical standards, professionalism; dynamism, agility, resiliency, and flexibility, and becoming lifelong learners. According to a recent study by the Computing Research Association (2017), the number of CS majors has increased since its low in 2006 with continued growth expected. Additionally, these academic units have experienced stresses in available space, resources, and faculty workload. These conditions may be alleviated by Industry partnerships that may help to support the instruction in the classroom.

Keywords: Computer Science, Industry, and Student Success

1

Introduction

Industry partners are a productive way to bring real life situations into a student’s academic life and promote student success and retention. Hooley, Hutchinson & Moore (2012) discuss how college graduate employability is also becoming an essential selection criteria for college choice and discipline by potential students. In addition, according to the 2015-2016 Criteria for Accrediting Computing Programs, criterion 5, curriculum, the curriculum must combine technical and professional requirements with general education requirements and electives to prepare students for a professional career and further study in the computing discipline associated with the program, and for functioning in modern society. To meet the technology needs of our increasingly complex global society, engineers in the 21st century are now

Undergraduate students are often lacking the soft skills needed for success in the workplace. Work Integrated Learning (WIL) enables the embedding of relevant real world learning into the curriculum resulting in students being better prepared to enter the workforce. There is clear evidence that students who undertake WIL as a part of their degree consistently achieve better employment outcomes (Edwards et al., 2015; Smith, Ferns & Russell, 2014). Radermacher, A., & Walia, G. (2013) found that the ability to communicate with customers and possessing good listening skills were cited as specific examples of skills lacking in recent graduates. Many employers share that new employees often require remediation. According to the survey, Are They Really Ready to Work? Employers’ Perspectives on the Basic Knowledge and Applied Skills of New Entrants to the 21st Century U.S.

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Workforce, employers found significant deficiencies in soft skills in new hires. Research supports that the incorporation of Industry partners in supporting curricular and co-curricular activities may help reinforce soft skill development for computing students. (Lanzerotti, 2014). In this work, co-curricular workshops for computing students led by industry experts found self-reported benefits. These included the perceived ability to effectively present career opportunities, connect class learning to work readiness, and outline experience and technical skills highly sought after for employment. These workshops demonstrated the connection between hard and soft skills related to classroom learning and industry preparedness. Prior to an industry partner’s presentation, each session was planned to highlight the specific hard and soft skills required by that target company. The concept was to build a relationship of the curricular elements to the real world application in specific industry settings. This included core computing and engineering skills as well as the key communication and teamwork aspects of supporting industry products and services. Examples of the industry partners who provided workshops are below in table 1.

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Careers in Government Panel: FBI: Through protecting and defending the country from terrorist threats, the mission of the FBI is to uphold and enforce the criminal laws set forth by the United States. CIA: The Central Intelligence Agency’s primary mission is to collect, evaluate, and disseminate foreign intelligence information to assist the US Government with decision-making regarding the nation’s security. Peace Corps: The Peace Corps, a free of charge international volunteer program, offers a two-year service opportunity to motivated individuals that are willing to positively impact a community abroad. FDOT: Primary function of the Florida Department of Transportation is to coordinate the planning and development of a balanced state transportation system, serving all regions of the state to assure the compatibility of all components, including multimodal facilities.

Table 1 CIA: The Central Intelligence Agency’s primary mission is to collect, evaluate, and disseminate foreign intelligence information to assist the US Government with decision-making regarding the nation’s security. FBI: Through protecting and defending the country from terrorist threats, the mission of the FBI is to uphold and enforce the criminal laws set forth by the United States. INROADS: The largest non-profit agency in the nation, providing professional training, corporate internships, and one-on-one professional mentorship to 26,000 alumni and 1,500 interns. Kairos: Specializing in facial analysis technology through artificial intelligence, Kairos is transforming and enriching the experience between humans and machines. Microsoft: The multinational technology company manufactures, supports and sells computer software/electronics and services. City of Hallandale Beach: Conveniently located between Miami and Fort Lauderdale, the City of Hallandale Beach offers year-round activities to its residents and visitors. CITRIX: Leader in business mobility, CITRIX empowers businesses and people to work efficiently through their SaaS Solutions, providing instant access to apps, desktops, data and communications on any device, over any network and cloud.

DOJ: The primary responsibility of the Department of Justice is to ensure the public safety against domestic and foreign threats and to seek punishment to those guilty of unlawful behavior by ensuring fair and impartial administration of justice for all Americans. Ultimate Software: Industry leader in providing cloud based people management solutions, focusing on delivering flexible, comprehensive solutions for companies to strategically manage all needs relating to Human Resources and Talent Management.

2
Co-curricular Industry Workshop Results The self-reported impact of co-curricular industry workshops was measured on three main themes: presenting career opportunities, connecting class learning to work readiness, and outlining experience and technical skills highly sought after for employment. 484 students were surveyed over 2 years and 98% of responders Agree/Strongly Agree the presentation was effective in presenting career opportunities, connected class learning to work readiness, and outlined experience and technical skills highly sought after for employment. (See Table 2.)

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Table 2

of computer science majors, leading to a 23% increase in first year retention. Industry Workshop Survey Results

Statement

Agree

Disagree

The presentation presented career opportunities.

98%

2%

The presentation outlined experience and technical skills that are highly sought after for employment.

98%

2%

The presentation outlined experience and technical skills that are highly sought after for employment.

98%

2%

The overall results of industry workshops led to a selfreported improvement in connecting class learning to work readiness, and outlining experience and technical skills highly sought after for employment. To this end, faculty should be discussing ways to add co-curricular activities to their computer science curriculum to better address the needs of the workforce and foster student success. Teijeiro, M., Rungo, P., Freire, M. J. (2013) support that faculty must ensure the curriculum fosters the competencies necessary for successful employment. Beach (2013) emphasizes that employers will pay a high price for the educational deficit found in many graduates.

3
Implications of the Findings In computing disciplines, industry involvement gives students a direct example of how the hard skills they learn in coursework and projects correlates with the soft skills necessary for real world projects. Students are exposed to the diverse STEM applications and situations that require critical thinking beyond individual projects. This exposure creates an awareness of the scope of real world career opportunities that inspire students to engage in the application of curricular and co-curricular activities including participation in internship opportunities. Thus the early and continuous exposure to industry partner workshops has led to increases in academic internships by 33%. This includes students applying for internships prior to their senior year and has led to job offers prior to their last term. Research supports that employers prefer job candidates that have exposure to real world experience such as participation in an internship, a senior project, or collaborative research projects (Hart, 2015). Additionally, these workshops contribute to the retention

There is a lack of literature that provides a conceptual framework for increasing student success for computer science and related STEM fields. Research on industry involvement in educating computing students has been limited and not been given enough attention. This study indicates there is value in engaging students with industry partners early and continuously throughout their degree program. By engaging with a diverse set of industry partners, students are exposed to the direct correlation between their curricular and co-curricular activities and their real world application that fosters a pathway to success through retention, internships, and graduation.

4

References 1.


Hooley, Y. Hutchinson, J. and Moore, N. (2012). Supporting STEM students into STEM careers: A practical introduction for academics. Derby: International Centre for Guidance Studies (iCeGS), University of Derby. 2.
ABET. (2015, October 15). Criteria for Accrediting Computer Programs. Retrieved April 4, 2016, from http://www.abet.org/wp-content/uploads/2015/10/C001-1617-CAC-Criteria-10-20-15.pdf 3.
Engineer of 2020: Visions of Engineering in the New Century. Online: http://www.nae.edu/Programs/Education/Activities10374/En gineerof2020.aspx 4.
Computing Research Association (2017). Generation CS: Computer Science Undergraduate Enrollments Surge Since 2006. http://cra.org/data/Generation-CS/ 5.
Edwards, D., Perkins, K., Pearce, J., & Hong, J. (2015). Work integrated learning in STEM in Australian universities. Report for the Office of the Chief Scientist. Retrieved from http://www.chiefscientist.gov.au/wpcontent/uploads/ACER_WIL-in-STEM-in-AustralianUniversities_June-2015.pdf 6.
Smith, C., Ferns, S., & Russell, L. (2014). The impact of work integrated learning on student work-readiness (Final Report). Retrieved from http://www.olt.gov.au/resourceimpact-work-integrated- learning-student-work-readiness 7.
Radermacher, A., & Walia, G. (2013, March). Gaps between industry expectations and the abilities of graduates. In Proceeding of the 44th ACM technical symposium on Computer science education (pp. 525-530). ACM. 8.
Lanzerotti, M. Y. (2014). Transforming Undergraduate STEM Summer Internships in a Federal Government Institution for 21st Century Engineering Careers. age, 24, 1. 9.
Teijeiro, M., Rungo, P., Freire, M. J. (2013). Graduate competencies and employability: The impact of matching firms’ needs and personal attainments, Economics of Education Review, Volume 34:286- 295. 10.
The U.S. Technology Skills Gap: what every technology executive must know to save America's future Gary Beach John Wiley & Sons, Inc. – 2013 11.
Hart Research Associates. 2015. Falling Short? College Learning and Career Success. Washington, DC: Association of American Colleges and Universities.

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Development of 21st Century Skills and Engineering Confidence Mahmoud Abdulwahed, [email protected] College of Engineering, Qatar University, Doha, Qatar

Abstract – The attributes of 21st century engineers encompass a wide variety of technical and non-technical skills such as: core engineering knowledge, ICT, communication, teamwork, ethics, problem solving, design, management, life long learning, etc. In this paper we report on a four dimensional model of 21st century engineering skills that was developed, and later was used to in assessing engineering design impact among genders, also the paper report on the design impact on confidence. The four dimensional model was derived from a comprehensive set of studies (over 200 papers), and were categorized around one specialized dimension, and three generic dimensions. Keywords: 21st Century Engineering Skills, Design, Engineering Education, Attitudes, Confidence, Education

1

Dimension II – Cognition, Mental, and Thinking, which includes the following competencies: 1-

Lifelong learning (Mishra, 2010),

2-

Problem solving (Allan et al., 2009),

3-

Decision making (Jones et al., 2009),

4-

Analytical thinking,

5-

Systems thinking (Palmer et al., 2011),

6-

Critical thinking (Danielson, 2011),

7-

Creative & Innovation (Bowman, 2010),

Introduction the Four Dimensional 8- Design (Knight, 2012) Model of 21st century engineering skills Dimension III – Professional and

A comprehensive literature review, reported in details in Abdulwahed et al. (2013), revealed several national investigations of engineering skills needs conducted in countries across the world, such as USA, Australia, UK, Malaysia; etc. Synthesis of skills and competencies from over 200 studies was performedd, and led to categorize the skills founded into a set of 22 items of global skills under 4 main dimensions: Dimension I - Core Knowledge and Practice, which includes the following competencies: 123-

Science knowledge (Math, Physics & Science Fundamentals) (Rajala, 2012) Disciplinary fundamentals, Interdisciplinary fundamentals (Woods et al., 2000),

4-

Multidisciplinary knowledge (NAE, 2004),

5-

Practical experience (Markes, 2006)

6-

ICT skills (Curtis et al., 2001)

Interpersonal, which

includes the following competencies: 1-

Professionalism (NAE, 2004),

2-

Ethics and Responsibility (NAE, 2004),

3-

Communications (Jones, 2009),

4-

Teamwork (Mena, 2012),

Dimension IV – Business and Management, which includes the following competencies: 1-

Management (Markes, 2006),

2-

Leadership (Finegold, 2010),

3-

Entrepreneurship (Rajala, 2012).

The model was used to assess the impact of engineering design (using Shell Eco-Marathon SEM) on 21st century development by genders. Figure 1 shows a conceptual diagram of the four dimensional model of engineering skills.

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4

Demographics

The whole students sample number is N=273, in which European participating students (Sample no. N=131), Asia participating students (sample no. N=109) and Middle Eastern participating students (sample no. N=33) (Note Middle Eastern countries included are all Arabian countries in addition to Turkey). Students were of both genders; males and Females but most of them are Males N=217(80%) while Females N=54 (20%), taking into consideration that Middle Eastern students males N=28 (85%) while females N=5 (15%), Asian students males N=92 (85%) while Females N=17 (15%) and European Males N=97 (75%) and Females N=32 (25%), see Table 1; Most students are of average age of 22.

Figure 1. Conceptual diagram of the four dimensional model of 21st century engineering skills.

2 2.1

Methodology and Research Questions Methodology

The methodology of the assessment included quantitative methods where surveys were also administered each of 17 questions (including demographics, open-ended, and Likert-scale type questions). Surveys were delivered to Europe participating students (Sample no. N=131), Asia participating students (sample no. N=109) and Middle Eastern participating students (sample no. N=33). Student responses were analyzed and presentation of major findings is included with interpretation as appropriate.

3

Instrument Design and Reliability

Surveys were designed to investigate the impact of design experience project “Shell Eco Marathon (SEM)” on student’s confidence, soft skills enhancement as well as core engineering knowledge and practice. Reliability was demonstrated using Cronbach`s alpha in order to determine the internal consistencies of the used satisfaction scales, Cronbach alpha values above 0.9 indicate excellent reliability (Cronbach and Meehl, 1955). Reliability of the designed survey has been calculated indicated a value of 0.983 > 0.9 representing high internal consistency. Validity was also demonstrated through conclusion validity which is described as appropriateness of the conclusions reported based on statistical relationships (Calder et al. , 1982), thus within this analysis conclusion validity was reported through the use of inferential statistics relying on statistical significance results at the 5% threshold.

TABLE I. Gender

STATISTICS OF GENDER

Europe

Asia

Male

97 (75%)

92(85%)

Female

32 (25%)

Total

129 (47.6%)

17 (15%) 109 (40.2%)

Middle East 28 (85%) 5 (15%) 33 (12.2%)

Total 217 (80%) 54 (20%) 271 (100%)

When students were asked about their English proficiency N=105 (38.7%) students stated that they are good in English (MEA N=13 (39.4%), Europe N=50 (38.2%) and Asia N=42 (39.3%); See Table 2. TABLE II. Origin Good English

ENGLISH PROFECIENCY STATISTICS

Europe in

Asia

50 (38.2%)

42 (39.3%)

Middle East 13 (39.4%)

Total 105 (38.7%)

About half of the students are from the mechanical engineering major N= (152) (56%) (MEA N=19 (57.5%), Europe N=74 (56.9%) and Asia N=59 (54.1%)) followed by other majors N=45 (17%) in which highest stated are mechatronics for European N=8, automotive for Asians N=6 and control and automation or material engineering for Middle Eastern N=2 then finally followed by electrical engineering N= (28) (10%) (MEA N=3 (9%), Europe N=13 (10%) and Asia N=12 (11%)); See Table 3. TABLE III.

STATISTICS OF ENGINEERING MAJOR

Origin

Europe

Asia

Mechanical engineering major Other

74 (56.9%)

59 (54.1%)

Middle East 19 (57.5%)

Mechatronics

Automotive

Automation

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Total 152 (56%) 45 (17%)

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Origin

Europe

Asia

majors

N=8 (6.2%)

N=6 (5.5%)

Electrical engineering

13 (10%)

12 (11%)

Middle East or material engineering N=2 (6%) 3 (9%)

25

Total

Math, Physics, & Science Fundamentals

28 (10%)

Europe

Asia

Seniors

52 (41%)

Juniors

26 (20%)

Graduates

27 (20%)

73 (68%) 16 (15%) 3 (3%)

Others

22 (19%)

5

15 (14%)

Middle East 16 (50%) 6 (18%)

Total

2 (6%)

32(12%)

8 (26%)

41 (17%)

Gender Impact on 21st Engineering Skills of Design

Interdisciplinary Engineering Knowledge (Breadth)

Multidisciplinary Knowledge

Males Females Males Females

Sample Numbe r (N)

4.04

183

4.04

48

4.07

Century

182

4.35

49

Males

3.80

178

Females

3.83

48

Males

3.85

176

Females

3.94

47

0.503

0.970

0.761

TABLE AX.18 MALE AND FEMALE STUDENTS ENGINEERING

WHERE 5 IS THE HIGHEST.

Variable

Mean

Lifelong Learning

Males

175

Females

4.15

47

Males

4.21

177

Females

4.15

47

Males

4.10

176

Females

4.11

47

Males

4.13

178

Females

4.17

47

Males

4.07

178

Females

4.06

47

Males

4.06

178

Females

4.15

47

Males

4.20

178

Females

4.21

47

Problem Solving

Decision Making

Analytical Thinking

Systems Thinking

0.982

Sample Number (N)

3.99

P-value

182 49

4.37

0.294

DIMENSION. ALL RATINGS ARE ACHIEVED BASED ON A SCALE FROM 1 TO 5,

0.284 3.98

47

PERCEPTUAL SCORES OF SEM- POST PARTICIPATION ON 21 SKILLS 2ND

48 (18%)

MALE AND FEMALE STUDENTS ENGINEERING PERCEPTUAL SCORES OF SEM- POST PARTICIPATION ON 21 SKILLS 1ST DIMENSION. ALL RATINGS ARE ACHIEVED BASED ON A SCALE FROM 1 TO 5, WHERE 5 IS THE HIGHEST.

Disciplinarily Engineering Fundamentals (Depth)

ICT Experience

TABLE VI.

TABLE V.

Mean

3.57

Females

141 (53%)

Analysis of the surveys have shown significant impact of engineering design as a result of participation in engineering design competition. However, the results has shown that there are no differences in impact based on gender; see Table V to Table VIII.

Variable

181

Males

STATISTICS OF STUDENTS ACADEMIC LEVEL

Origin

4.03

Females

Practical Experience

Also most of the students were seniors N= (141) (53%) (MEA N=16 (50%), Europe N=52 (41%) and Asia N=73 (68%)) followed by other Juniors N=48 (18%) in which MEA N=6 (18%), European N=26 (20%) and Asians N=16 (15%) and finally graduate students N=32(12%) in which MEA N=2 (6%), European N=27 (20%) and Asians N=3 (3%). See Table 4. TABLE IV.

Males

Critical Thinking

Creative Thinking

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P-value

0.432

0.58

0.943

0.990

0.852

0.768

0.942

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Innovation

Males

Design

4.10

Leadership

178 0.936

Females

4.13

47

Males

4.06

178

4.02

TABLE AX.19 MALE AND FEMALE STUDENTS ENGINEERING PERCEPTUAL SCORES OF SEM- POST PARTICIPATION ON 21 SKILLS 3RD DIMENSION. ALL RATINGS ARE ACHIEVED BASED ON A SCALE FROM 1 TO 5, WHERE 5 IS THE HIGHEST.

Professionalism

Mean

Males Females

Ethics & Responsibility

Males Females

Adaptability

Males Females

Communication

Males Females

Teamwork

Males Females

TABLE VIII.

Entrepreneur ship

Sample Number (N)

4.28

0.855 4.06

47

3.94

177

3.96

47

4.12

176

0.912

0.421 4.24

46

4.09

176

4.06

47

4.33

175

4.40

47

6

0.41

TABLE AX.20 MALE AND FEMALE STUDENTS ENGINEERING

DIMENSION. ALL RATINGS ARE ACHIEVED BASED ON A SCALE FROM 1 TO 5,

Females

47

3.87

175

3.74

47

0.032

0.261

Design

1-

Pre- Participation in SEM

2-

Post- Participation in SEM

Impact

In the next subsections, details of comparative analysis (preand post- SEM) on the European, Asian, and Middle Eastern students is provided, as well as comparing males vs. females responses.

6.1

European Students Perception against Asians

Statistical analysis using Mann Whitney U test showed that most of the European students have relatively higher perceptual Pre-SEM scores on the majority of items, with seven of them of statistically significant difference as highlighted in yellow in Table 7. Comparing European and Asian perceptual scores Post-SEM participation revealed close means on most items, Mann Whitney U test showed no statistically significant difference between European or Asians students perception. TABLE IX.

EUROPEAN AND ASIAN STUDENTS ENGINEERING PERCEPTUAL

SCORES OF PRE-SEM PARTICIPATION ON CONFIDENCE ON A SCALE FROM 1 TO

5, WHERE 5 IS THE HIGHEST.

Sample Number (N)

3.94

P-value

175 0.064

3.70

46

Category

Confidence

Mean

Males

3.81

Students were asked about their opinion regarding these statements on a scale from 1 to 5, where: 1= “Strongly Disagree”, 2= “Disagree”, 3= “Neutral”, 4= “Agree”, and 5= “Strongly Agree” on two time pointes:

WHERE 5 IS THE HIGHEST.

Management

175

0.93

PERCEPTUAL SCORES OF SEM- POST PARTICIPATION ON 21 SKILLS 4TH

Variable

4.07

Pre and Post Confidence

P-value

177

Males Females

47

TABLE VII.

Variable

Females

0.717

Females

Males

Mean

Items

N

Pvalue E

I’m able to learn new engineering knowledge on my own I’m confident of my ability/competencies I’m confident of becoming a successful engineer

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EU

3.76

127

Asian

3.47

105

EU Asian EU Asian

3.44 3.42 3.43

126 99 127

3.43

103

Sig.

0.026

Yes

0.761

No

0.789

No

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TABLE X.

27

EAN AND ASIAN STUDENTS ENGINEERING PERCEPTUAL

engineering deisgn experience such as shell has impact on developing 21st century engineering skills, however there is no major difference in impact among different genders of males and females. The design was found to impact confidence as well, males seems to have more confidence than females.

SCORES OF POST-SEM PARTICIPATION ON CONFIDENCE ON A SCALE FROM 1 TO 5, WHERE 5 IS THE HIGHEST.

Confidence

Category

6.2

Mean

Items

N

Pvalue E

I’m able to learn new engineering knowledge on my own I’m confident of my abilities/competencie s I’m confident of becoming a successful engineer

EU Asian EU Asian EU Asian

Middle Eastern against European

4.21

126

4.07

98

4.01

123

3.96

97

3.90

126

4.04

100

Students

0.209

Sig. No

8 0.932

No

0.240

No

Statistical analysis showed that most of both the Middle Eastern and the European students have relatively close perceptual scores of Pre-SEM participation. Mann Whitney U test of pre-SEM participation showed no statistically significant. While comparing their satisfaction level Post SEM participation using Mann Whitney U test showed that there was also no statistically significant difference between European and Middle Eastern students perception but with higher means than pre-SEM participation close to 4 “Agree” or in some cases close to 5 “Strongly agree” which showed that the participation in such project had a great impact in students skills and knowledge; See Error! Reference source not found.in appendix.

6.3

Male Students Perception against Females in regards with Confidence

Genders analysis was carried to measure any potential differences between males and females. Descriptive analysis showed male students have slightly higher perceptual scores in majority of items, however inferential analysis using Mann Whitney U test for pre-SEM participation revealed no statistically significant difference in any item. In Post-SEM perceptions male students have also reported slightly higher perceptions than females in majority of items, three of these twelve were statistically significant: 1- “I’m confident of my abilities/competencies”, 2- “I’m confident of becoming a successful engineer”, 3-“Egineering is fun”.

7

Funding of this work by Dolphin and Qatar-Shell is greatly acknowledged.

9 Perception

Conclusions This paper reported on model of 21 st century engineering skills that has been used to asses impact of design on genders. The analysis have shown that complex

Acknowledgement

References

Abdulwahed, M., Balid, W., Hasna, M. O., & Pokharel, S., 2013. Skills of engineers in knowledge based economies: A comprehensive literature review, and model development. In Teaching, Assessment and Learning for Engineering (TALE), 2013 IEEE International Conference on (pp. 759765). IEEE. Allan, M.; Chisholm, C.U. The formation of the engineer for the 21st century—A global perspective. In Proceedings of the 20th Australasian Association for Engineering Education Conference, Adelaide, Australia, 6-9 December 2009; pp. 447–452. Bowman, K. Background Paper for the AQF Council on Generic Skills. Australian Qualification Framework Council, Canberra, Australia, 2010. Cronbach L.J., and Meehl P.E. 1955. “Construct validity in Psychological Tests” Psychological Bulletin 52:281-302 Curtis, David, and Phillip McKenzie. Employability skills for Australian industry: Literature review and framework development. Australian Council for Educational Research: Melbourne, Australia, 2001. Danielson, S. ASME vision 2030: Helping to inform mechanical engineering education. In Proceedings of the 41st ASEE/IEEE Frontiers in Education Conference, Rapid City, SD, USA, 12–15 October 2011; pp. 1–6. Davis. H, Evans.Y and Hickey.C, A knowledge based economy landscape: Implications for tertiary education and research training in Australia, Journal of Higher Education Policy and Management, 28:3, 231-244, 2006. Finegold, D.; Notabartolo, A. 21st-Century Competencies and Their Impact: An Interdisciplinary Literature Review. Research on 21st Century Competencies, National Research Council. 2010; pp. 1–50. Available Jones, J.; Meckl, P.; Harris, M.; Cox, M.; Cekic, O.; Okos, M.; Campanella, O.; Houze, N.; Litster, J.; Mosier, N.; Tao,

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B.; Delaurentis, D.; Radcliffe, D.; Howell, K.; Okutsu, M.; Brophy, S.; Penner, A.; Wilson, A.; Jamieson, L. Purdue's Engineer Of 2020: The Journey. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition, Austin, Texas, 14-17 June, 2009. Knight, D.B. In Search of the Engineers of 2020: An Outcome-Based Typology of Engineering Undergraduates, AC 2012–3337. In Proceedings of the 119th Annual Conference of the American Society for Engineering Education, San Antonio, TX, USA, 10–13 June 2012. Markes, I. A review of literature on employability skill needs in engineering. Eur. J. Eng. Educ. 2006, 31, 637–650. Mena, I.B.; Zappe, S.E.; Litzinger, T.A. Preparing the Engineer of 2020: Analysis of Alumni Data. In 2012 ASEE Annual Conference; American Society for Engineering Education: San Antonio, Texas USA, 10-13 June 2012. Mishra, S. Engineering curricula in the 21st century: The global scenario and challenges for India. J. Eng. Sci. Manag. Educ. 2010, 1, 29–33. NAE. The Engineer of 2020: Visions of Engineering in the New Century; The National Academies Press: Washington, DC, USA, 2004. Palmer, B.; Terenzini, P.T.; McKenna, A.F.; Harper, B.J.; Merson, D. Design in Context: Where Do the Engineers of 2020 Learn This Skill. In Proceedings of the 118th Annual Conference of the American Society for Engineering Education, Vancouver, BC, Canada, 26–29 June 2011; Volume 2020. Rajala, S.A. Beyond 2020: Preparing engineers for the future. Proc. IEEE 2012, 100, 1376–1383. Woods, D. R.; Felder, R. M.; Rugarcia, A.; Stice, J. E.. The future of engineering education III. Developing critical skills. Change. 2000, 4, 48-52.

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SESSION PROGRAMMING AND SOFTWARE ENGINEERING COURSES Chair(s) TBA

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Creative Coding for All Students M. R. Zargham and C. K. Danduri Department of Computer Science University of Dayton Dayton, Oh USA

Abstract - This paper represents progress of our ongoing effort on new way of teaching an introductory programming course. Students use a simple environment, called Innovative Coding (IC), to write a code for designing a game or a virtual world. As they code, they see the development of their world in a 3D environment step by step. Once they have completed their design, they can wear a VR headset to interact on their design in a virtual environment. Currently, IC supports five different types of landscapes (desert, fantasy island, mountain, farm, and city), 8 different princesses, 8 boys with different ethnicities, 10 different animals, 10 different monsters, one helicopter, and one car. Excluding landscapes, all other objects are animated being able to perform many tasks, such as walk, run, jump, eat, sleep, dance, etc. IC also supports many static objects that are not animated, such as: tracks to construct different roller-coasters, plants, flowers, cabins, decorating objects, and walls. Keywords: VR Headset; Roller-Coaster; Virtual Reality; Computer Science Education; Programming.

1.

Introduction

We have created an innovative coding (IC) environment in which students can write simple codes consisting of several statements to create a game or a virtual world. This paper will explain the present functionality of IC, that emerged from a project funded by AAC&U that was intended to broaden participation in computer science at all levels. At present there are several graphical coding platforms that many K-12 schools use; the most popular ones are: Alice 3D by Carnegie Mellon University, Scratch by MIT Media Lab, and Snap by Berkeley [1, 2, and 3]. Although these platforms provide visual programming environment and are popular among some students, they still have some drawbacks such as visualizing the entire programming process in a drag-and-drop manner, teacher training, and difficulty of data and control flow observation [4, 5, 6]. The students working with such systems may struggle to move to a traditional programming environment due to the

lack of experience to linking between the programming concepts acquired from those visual tools and the implementation details in an actual programming language. IC overcomes these drawbacks by integrating both the traditional text-based programming platform and immersive virtual environment. IC creates an environment in which students can write simple codes consisting of several statements to create a world of their choice in a 3D and Virtual Reality environment. VR goes beyond a simply visual stimulus, and allows students to become directly involved, experiencing the code they write in a very tangible, interactive, and expeditious scheme(strategy). This allows them to experience both the successes and failures of writing (creating, developing) correct code versus incorrect code. Students can appreciate both the scale and the scope of the results of their code immediately, which prompts them to become invested in the quality of their coding skills, and strive to continually improve their logic, programming and code writing abilities. Through this Immersive and Responsive Visual Stimulus Learning, students become more engaged in coding. In general, the idea is to teach the introductory programming class in such a way that follows project-driven learning process and encourages students to develop problem solving and teamwork skills while fostering creativity and logic. The goal is to not only provide students with some “programming maturity,” but to also engage them with existing projects related to their interest.

2. Current Functionality of IC Currently, students can design their projects in different landscapes (backgrounds) using a variety of objects. For example, they can use the environment to design a 3D modeling of a roller-coaster based on their choice of options. Presently, there are several statements users may select from and implement, including statements that allow students to declare variables, assign values to variables, arithmetic and logical expressions, array statements, object statements (such as designing a track/wall segment by defining its length, direction, and angles), assignment

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statements, conditional statements, repeat statements, animation statements, function statements, and I/O statements. Figure 1 represents a sample code (on the right side) for a roller-coaster and its design. In the editor mode, the small window on the right-top corner is utilized by the user to enter his/her code for building the roller-coaster. During this process, the user can see the progress of his/her code in 3D. When the user is satisfied with the design, he/she will click the 3D or VR button to start the animation. If a VR headset (HTC Vive) is connected to the computer, the users can actually be given the sense that they are riding on their own roller-coaster design; see Figure 2. Note that it is not required to have VR headset to utilize IC; the 3D mode also allows the user to be engaged and interact with his/her created environment. The code editor allows students to add or delete a statement in their partial code very easily. The effect of such modification on their partially built roller-coaster can be seen instantly by clicking on “wooden” or “steel.” The user can then use “for-loop” to build a large track by repeating a section of the track many times. The “while-loop” can be used to design a track that reaches a certain height. The “if” statement can be used to make a track dynamic by assigning it different types of objects. Figures3 and 4 represent different scenes and objects of the current version of IC. Currently, IC supports five different types of landscapes (desert, fantasy island, mountain, farm, and city), 8 different princesses, 8 boys with different ethnicities, 10 different animals, 10 different monsters, one helicopter, and one car. Excluding landscapes, all other objects are animated being able to perform many tasks, such as walk, run, jump, eat, sleep, dance, etc. IC also supports many static objects that are not animated, such as: tracks to construct different roller-coasters. plants, flowers, cabins, decorating objects, and walls. Different types of interaction are possible with the designed world, such as riding a roller-coaster, flying a helicopter, and driving a car (see figures 5 and 6, below).

Figure 2. A class of 22 5th grade students designing different roller coasters

Figure 3. Landscapes supported with current version of IC.

Figure 4. A sample of four different objects, animals, monsters, princess, and boys. Early in 2017, the current version of IC was used in two different situations: a field trip of a fifth grade class from Harman Elementary School, and the Dayton Techfest 2017. In both occasions the outcomes were outstanding. In a short period of time students were able to design complex roller-coater using for and while-loops. They were also able to explore IC to experience interacting with helicopter, car, humans, and animals. In case of fifth grade, students were so engaged in coding different scene on IC that they did not want to go back to school. They all expressed that their experience was AMAZING! Students were glad to receive a copy of software on a memory stick to do coding at home.

Figure 1. The editor window for designing a roller-coaster.

Figure 5. Different types of interaction with designed world: riding a roller-coaster, flying a helicopter, and driving a car.

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3. Conclusions

Figure 6. Different environments with programmed objects. At present we are emphasizing on one project (rollercoaster). In future we will be adding more projects such as: car racing, 3-D store, and games. Once students have learned the basics of programming through various design projects, they will be encouraged to design games in an environment such as Unity by learning more programming statements and techniques. To teach a programming course in this way, will attract students with diverse educational backgrounds to form teams to tackle various problems. Beside students in K-12, computer science, electrical/computer engineering and business, students from other disciplines such as mathematics, physics, chemistry, biology, communication, and arts are encouraged to enroll and participate. To demonstrate the simplicity of the IC, a possible code for a roller-coaster can be developed as below: track 40 up 60 0 0; track 40 down 120 0 0; track 30 up 60 0 0; track 30 forward 0 0 0; var i; generate j 1 400; i = j%4; if(i=0) { pumpkin 20; } else if(i=2) { goblin 20;} else { bear 20; } i = maximumheight; j=currentheight; display i j; track 10 up 60 0 0; while (j@ 6WXGHQWIDFXOW\UDWLRVDQGQXPEHURIFUHGLWKRXUVSHUIDFXOW\ PHPEHUKDYHLQFUHDVHGLQWKHODVWVL[\HDUVDORQHDPLGVW 8& EXGJHW FXWV >@ $ORQJ ZLWK DQ HYHULQFUHDVLQJGLYHUVLW\

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Towards Interest-based Adaptive Learning and Community Knowledge Sharing Karen Aguar, Hamid R. Arabnia, Juan B. Gutierrez, Walter D. Potter, and Thiab R. Taha Department of Computer Science, Department of Mathematics, Institute of Bioinformatics, University of Georgia, USA Email: [email protected] Abstract—We propose a System for Adaptive Interest-based Learning (SAIL) that utilizes community knowledge sharing (crowd-sourcing) strategies to empower adaptation of examples and practice problems based on students’ individual interests. Personalizing education based on interest can lead to increased intrinsic motivation and positive learning outcomes. While most studies have been conducted manually, adaptive learning technologies offer a new approach to widespread incorporation of adaptive interest-based materials. The difficulty in widespread implementation is the enormous effort required to create customized content. SAIL aims to provide a framework for educators to access and contribute adaptive materials via community knowledge sharing within an easy-to-use adaptive learning system.

.

1. Introduction Individualized instruction is the concept that instruction and/or materials should be customized to the unique needs of each student. Manual efforts to personalize education have been common practice - for example, if a student is struggling with addition, the teacher may assign extra homework problems for practice. Using technology to individualize instruction via an adaptive learning system (ALS) has been a widely studied research area over the last several decades. Adaptive learning strategies have been shown to improve student performance, with adaptation usually based on a student’s previous knowledge, pace, or learning style [1]. This research takes an alternative, less-explored approach by enabling the adaptation of content, practice problems, and examples based on a student’s interests. Incorporating personal interest into learning has been shown to increase intrinsic motivation and provide positive learning outcomes [2]. Most studies of personalized interest in education have been implemented manually, but initial studies incorporating personal interest into adaptive technologies have indicated tremendous potential [3]. While adapting educational content based on student interest can be advantageous, widespread incorporation is difficult due to the enormous amount of instructor effort required to develop custom content. We propose a System for Adaptive Interest-based Learning (SAIL)- a web-based adaptive learning framework that supports community knowledge sharing (crowd-sourcing) of adaptive materials to address these limitations and empower

adaptation based on interest. The vision is to provide a framework where educators from multiple domains can contribute to and access adaptive content available to the community to provide students with an improved and individualized learning experience.

2. Background 2.1. Adaptive Learning Adaptive learning is the notion of using computers as interactive teaching devices to adapt to the user’s individual needs. It combines the fields of Computer Science, Education, Psychology, etc. Adaptive Learning is a broad term with many varying implementations such as: Adaptive Educational Hypermedia, Intelligent Tutoring Systems, and Adaptive eLearning. It is primarily used in educational settings such as classrooms and business training. While Adaptive Learning has been widely researched with many reporting success, many challenges still exist. Creating a successful adaptive learning system is difficult, time-consuming, expensive, and usually geared towards one particular domain [1]. Recent research explores the use of authoring tools - designs to customize an adaptive system to work across multiple related domains [4]. Widespread integration of adaptive learning in education relies on the continued improvement of adaptive learning technologies and authoring tools [1].

2.2. Personalization Adaptive learning strategies typically adapt based on a students previous knowledge, pace, or learning style [1]. This research takes an alternative, less-explored approach by adapting content, practice problems, and examples based on a students interests while. Many studies have demonstrated that self-reference and context personalization have influenced student memory and learning [5]. The Self-Reference Effect [6] has been extensively studied and has shown that the customization of information to relate to the self or someone closely associated with the self can lead to learning improvement including better recall, transfer, and retention of information [7], [8], [9]. Recent studies evolving from the SRE have demonstrated that the the customization of content to include students’ familiarities and interests (personalized context)

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has shown significant benefits in student learning. Most of the studies to this date have been manually conducted without the use of any adaptive technologies. Initial studies using adaptive technology as a medium for personalized context have suggested positive learning outcomes [5]. Most notable is a 2013 study that personalized word problems in one unit of an Algebra course with an Intelligent Tutoring System (ITS) as the educational medium. Students who received word problems with personalized context had better and faster performance on the affected unit of instruction and demonstrated positive outcomes in abilities to transfer knowledge and retain information [3]. While results have been overwhelmingly positive for the incorporation of student interest in educational content creation, widespread incorporation is difficult. Most studies have been conducted in the field of mathematics where key words can be easily substituted into generic word-problems [5]. This approach limits the domains in which personalized context can be implemented and also the depth of which problems can relate to student interests. Many STEM fields such as computer science, engineering, and mathematics are highly applicable with other disciplines and the world around us. Utilizing the interconnectivity of these subjects with the world around us could be immensely beneficial in correcting the low enrollment, retention, and diversity issues that these STEM fields often suffer. Imagine a world where a student athlete could learn how to program based on sports related examples while another student in the same class could learn programming through science examples. To accomplish such a task, more effort would need to go into customized content creation - a task that would quickly bottleneck if created in isolation. We propose a framework that supports community knowledge sharing (crowd-sourcing) of adaptive materials to address these limitations. Creating and encouraging the use of online knowledge sharing communities for educational resources is an effort being explored by many top universities. In the Computer Science community, efforts such as Stanford’s Nifty Assignments project seek to collect ’interesting’ CS assignments for reuse to help improve CS education (nifty.stanford.edu). Similarly, UC Berkeley’s Ensemble project seeks to establish a digital library for computing education, with current research in participation encouragement through ranks/badges (computingportal.org). As we move towards the sharing of educational resources in online communities, more work is needed to encourage participation, organization, and optimize utilization of these materials.

3. SAIL We are currently developing SAIL - System for Adaptive Interest-based Learning. The goal of SAIL is to provide a system where educators from multiple domains can contribute to and access adaptive content available to the community to provide students with an improved and individual learning experience. SAIL is the evolution of

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ALICE (Adaptive Learning for Interdisciplinary Collaborative Environments), developed by UGA as a revolutionary adaptive learning system for interdisciplinary instruction, but is being significantly expanded to accomplish two important missions: 1)

2)

Provide an adaptive learning system based on community knowledge sharing (crowd-sourcing) of educational content to alleviate the enormous task of content creation from the instruction. Provide a framework for adaptation of practice problems and examples based on a student’s interests. Initial studies of the incorporation of students’ interests into adaptive learning systems have demonstrated many positive learning outcomes, but no widespread solution exists to address existing limitations by domain or instructor. Through community knowledge sharing, SAIL offers a novel solution to the problem of adaptive interest-based learning.

3.1. Community Knowledge Sharing ALICE was initially designed as a system to empower transdisciplinary knowledge acquisition by providing students with a personalized syllabus based on their previous knowledge to help students from varying background collaborate on interdisciplinary problems [10]. ALICE was piloted in the Spring 2017 semester and the numerous changes to instructional design and planning that must be considered when transitioning to an adaptive learning system were documented [10]. Though certain adaptations to instructional design should be anticipated when transitioning to a new teaching method, the enormous effort currently required by the instructor to develop adaptive content is an obstacle preventing widespread incorporation and success. Even with the most dedicated educators, keeping up with the demand needed to implement ALICE in the Systems Biology pilot study was a struggle. These observations identified a clear need for community knowledge sharing in adaptive learning systems. SAIL transforms the approach to adaptive learning by providing a medium for community knowledge sharing of educational content. The framework for the organization, storage, and retrieval of educational content is in early development. Once complete, educators will be able to search for content using keywords to find lectures, practice problems, and supporting material to utilize in their instruction. The usability of such a system is important for widespread success and is considered at every step of the design phase. An interactive drag and drop map is being developed to allow instructors to easily add content (via uploading or reuse of community resources) and create adaptive branches in course instruction. Students will also be presented with an interactive map where they can track their progress and see their unique adaptive path highlighted throughout the course.

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3.2. Interest-based Adaptation

A common issue in traditional instruction is a lack of meaningful connections between what is being taught and how these concepts apply to the real-world. For example, a student learning introductory programming may learn how to print out the numbers 1-10 with a for-loop but not understand how learning such a concept could be applicable. Even when instructors provide examples to show the interconnectivity with the real world, it is impossible for a single example to be of interest to all students in the class. An example problem that uses baseball to teach some fundamental skill would intrigue only a subset of the students involved in the course, as some students may be uninterested in sports or may be from different backgrounds and not understand the rules. Problems such as these exist in the traditional instructional setting where all content, practice problems, and assignments depend on the unique instructor and are universal for all students. SAIL addresses this problem by adapting the learning content to a student’s individual interests to ensure that each student is taught new ideas in a meaningful way. It has been shown that increasing student interest while learning leads to a better overall learning experience - specifically impacting attention, goals, and level of cognition [2]. However, developing adaptive content is time consuming and too large a task for a single instructor. SAIL provides a unique opportunity to allow this adaptation of practice problems and exercises based on students interests through the incorporation of a community knowledge sharing framework. In the evolution from ALICE to SAIL, there are some significant differences in both ideology and adaptivity. ALICE’s adaptivity was implemented through a network connecting multiple disciplines, while SAIL evolves to include intradisciplinary adaptivity - aiming to highlight the interdisciplinary nature of many STEM fields (Computer Science, Mathematics, Statistics, Engineering) through interestbased learning to show the impact and interconnectivity with other surrounding fields. With ALICE, students learn different content at different paces with multiple starting points and end goals. While this interdisciplinary design is still supported in SAIL, the adaptive framework is expanded to support linear traversal through lexias/lessons in a predetermined order while adapting practice problems and examples to a student’s individual interests. The design of this adaptive framework can be seen in Figure 1. For example, students in an introductory programming course would all have the same lesson on loops but will then follow a path of examples and practice problems based on their indicated interests. A student interested in Biology may take one path where they practice loops with Biology-related examples and problems, while other students may encounter problems in other domains such as PE, English, or Chemistry.

Figure 1. Knowledge Map for SAIL-CS. Students will move through a linear ordering of lexias: A, B, C, ... Practice problems and examples will branch off based on the student’s interests (D1, D2, D3, .. Dn) Students may follow the adaptive path (solid arrows) or adapt their own path (indicated by dotted arrows) through the examples.

4. Conclusion SAIL is currently in active development at the University of Georgia with an anticipated pilot study (SAIL-CS) in an Introductory Programming course expected in Fall 2017. Computer Science, like many other STEM fields suffers from low enrollment and diversity issues. As computer science touches nearly every part of our daily lives, adapting the way it is taught based on a students other interests could help attract and retain a larger and more diverse population of computer scientists. SAIL is designed to be a highly scalable system that could be used by many courses and evolve over time to include more content and interestbased problems through community contribution (crowdsourcing). Our hypothesis is that SAIL can positively impact STEM education at a very broad scale.

Acknowledgment This material is based upon work supported by the National Science Foundation under Grant No. 1645325.

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K. Aguar, H. R. Arabnia, J. B. Gutierrez, W. D. Potter, and T. R. Taha, “Making cs inclusive: An overview of efforts to expand and diversify cs education,” in Computational Science and Computational Intelligence (CSCI), 2016 International Conference on. IEEE, 2016, pp. 321–326.

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R. Moreno and R. E. Mayer, “Engaging students in active learning: The case for personalized multimedia messages.” Journal of Educational Psychology, vol. 92, no. 4, p. 724, 2000.

[10] K. Aguar, C. C. Sanchez, D. B. Beltran, S. Safaei, M. Asefi, J. Arnold, P. Portes, H. R. Arabnia, and J. B. Gutierrez, “Considerations on interdisciplinary instruction and design influenced by adaptive learning. sail-sb: A case study involving biology, computer science, mathematics, and statistics,” arXiv preprint arXiv:1703.06010, 2017.

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Student Perspectives on Learning in a Course on Engineering Applications for Nanoscience and Nanotechnology

Deborah Worley1, Naima Kaabouch2, Matt Cavalli3, Kanishka Marasinghe4, Nuri Oncel4, David Pierce5, Brian Tande6, Julia Zhao5 1 Educational Leadership, 2Electrical Engineering, 3Mechanical Engineering, 4Physics and Astrophysics, 5 Chemistry, 6Chemical Engineering, University of North Dakota, Grand Forks, North Dakota, USA Abstract - This paper offers preliminary results of formative and summative assessment of awareness, exposure, and motivation to study nanoscience and nanotechnology for students who enrolled in a course on engineering applications for nanoscience and nanotechnology at a large research university in the upper Midwest. The results of this data collection activity were used to further discourse surrounding nanoscience and nanotechnology curriculum development. The overall study is funded by a National Science Foundation (NSF) Nanotechnology Undergraduate Education (NUE) grant. Keywords: nanoscience, nanotechnology, application, awareness, exposure, motivation

1

engineering

Introduction

Nanoscience and nanotechnology is a maturing field of study that has grown rapidly beyond the formative stages of exploration and experimentation in interdisciplinarity. Institutions of higher education offer coursework and academic programs that are designed to introduce students to the fundamental concepts of the field [1-2] as well as to pedagogy in teaching nanoscience and nanotechnology [3], and to the social implications and ethics of responsibility in carrying out work in these fields [4]. Curriculum is now developing around application of the core concepts of nanoscience and nanotechnology, partially as a means to best prepare students for careers that require nanoscience and nanotechnology knowledge and skill acquisition but also as means to advance cutting-edge science and engineering initiatives. The purpose of this paper is to describe the preliminary results of formative and summative assessment efforts surrounding students’ self-reported levels of awareness, exposure, and motivation to study nanoscience and nanotechnology in a new course that was developed to teach students engineering applications for nanoscience and nanotechnology. We sought students’ perspectives in order to better understand how they experienced the course material. This assessment is part of a larger project that is dedicated to introducing the nanoscience and nanotechnology field into undergraduate engineering curricula at a public state research institution in the upper Midwest. With this overarching purpose in mind, the project

team members developed and implemented new courses. The overall study is funded by a National Science Foundation (NSF) Division of Engineering Education and Centers (EEC) Nanotechnology Undergraduate Education (NUE) grant.

2

Methodology

The population of interest for this portion of the project was undergraduate and graduate students who enrolled in a spring 2016 course, Engineering Applications of Nanoscience and Nanotechnology. This course covered mechanical, electrical, and chemical properties of nanomaterials and engineering applications of nanoscience and nanotechnology. It also covered ethical, social, and environmental impacts of nanomaterials. Students could take the course in a face-to-face, campus-based format or via an asynchronous, distance platform. The course was collaboratively taught by faculty in the College of Engineering and Mines and the College of Arts and Sciences. A faculty member from the College of Education and Human Development visited the class on two occasions (at the beginning and end of the fifteen week semester) to administer the Nanotechnology Reflection Survey. Students in the face-to-face, campus-based section of the course were given a hard copy of the survey. Students using the asynchronous, distance platform to take the course were given an electronic version of the instrument. Fifteen face-to-face, campus-based students completed the survey at the beginning of the course. Nine face-to-face, campus-based students completed the survey at the end of the course. No distance students elected to complete the survey. The Nanotechnology Reflection Survey [5] was created by Diefes-Dux and colleagues. The survey questions pertain to students’ awareness (eight questions), exposure (six questions), and motivation (17 questions) for studying nanoscience and nanotechnology [6]. The same instrument was used to gather information on student exposure to and experience with nanoscience and nanotechnology curricula in a previous semester. Related questions on the survey about students’ knowledge about nanotechnology as well as their overall learning experiences will be explored at a later date.

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3

Results

In this paper, we report basic descriptive statistics about students who enrolled in a course on Engineering Applications of Nanoscience and Nanotechnology. Specific items include levels of awareness of nanotechnology, prior exposure to nanotechnology, and motivations for taking a course in engineering applications for nanoscience and nanotechnology.

3.1

Awareness of Nanotechnology

By nature of enrollment in the course, it was assumed that students had some awareness of nanotechnology as a field of study. Given this assumption, we then focused on understanding their levels of awareness on eight specific items related to studying nanotechnology. All items used a five point scale, ranging from (1) strongly disagree to (5) strongly agree. The results for these items are included in Table 1. Table 1. Engineering Applications in Awareness of Nanotechnology Mean (start of I can: course) Name a nanoscale4.00 sized object. Describe one way nanotechnology 3.60 directly impacts my life. Name a field of study that currently conducts 3.93 nanotechnology research. Describe one way nanotechnology 4.07 may benefit society/humankind Name an 4.00 application of nanotechnology. Describe a process to manufacture 2.93 objects at the nanoscale. Name an instrument used to 3.40 make measurements at the nanoscale. Describe one way nanotechnology may directly 3.67 impact my life in the future.

Nanotechnology:

Students’

3.2

Exposure to Nanotechnology

Students’ self-reported levels of exposure to nanotechnology were measured using six items from the Nanotechnology Reflection Survey. All items used a five point scale, ranging from (1) not at all/never to (5) a great deal. The results for these items are included in Table 2. Table 2. Engineering Applications in Nanotechnology: Exposure to Nanotechnology Mean SD Mean (start of (start of (end of I have: course) course) course) Heard the term 4.07 0.88 4.33 nanotechnology.

Students’ SD (end of course) 0.71

Read [something] about nanotechnology.

3.13

1.46

4.11

0.93

SD (start of course)

Mean (end of course)

SD (end of course)

Watched a program about nanotechnology.

2.87

1.25

3.11

1.05

1.20

4.78

0.44

1.30

4.56

0.73

Had one [or more] instructors/ teachers talk about nanotechnology in class.

3.00

1.56

4.22

0.83

1.03

4.67

0.50

Participated in an activity involving nanotechnology [lab, project, research].

2.40

1.68

3.67

1.41

Taken a class about nanotechnology.

2.80

1.57

3.89

0.78

1.03

4.78

0.44

1.13

4.78

0.44

1.49

4.67

0.50

1.64

4.67

0.50

1.35

4.67

0.71

3.3

Motivations for Investigating Nanotechnology

Students’ self-reported levels of motivation for studying nanotechnology was measured across 17 items from the Nanotechnology Reflection Survey. All items used a five point scale, ranging from (1) strongly disagree to (5) strongly agree. The results for these items are included in Table 3.

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Table 3. Engineering Applications in Nanotechnology: Student Motivation Mean (start of I plan to: course) Read a fiction story about nanotechnology. 2.67

75

SD (start of course) 1.05

Mean (end of course) 2.78

SD (end of course) 0.97

Formally teach nanotechnology concepts (e.g. as a teaching assistant)

2.07

1.03

3.00

1.00

Investigate the implications of nanotechnology.

3.87

0.99

4.22

0.67

Informally/casually teach someone something about nanotechnology.

3.87

0.92

4.00

0.87

3.40

1.18

3.67

0.71

4.00

0.76

4.00

0.71

3.13

1.19

3.38

0.92

4.27

0.70

4.13

0.64

Read a research journal article about nanotechnology.

3.86

1.23

3.88

0.64

Enroll in a course about nanotechnology.

3.87

1.19

3.88

0.83

Attend a non-course related seminar about nanotechnology.

3.40

0.99

3.88

0.64

Visit an industry or business that specializes in nanotechnology.

3.87

1.13

4.00

0.93

Give a presentation related to nanotechnology to an audience I perceived as having less experience with nanotechnology than I.

3.33

1.23

3.63

0.74

Watch a program about nanotechnology.

3.60

1.12

3.63

1.06

Apply or interview for a nanotechnology related work or research experience.

3.40

0.99

3.88

0.64

Investigate fields of study in which I can learn more about nanotechnology.

3.60

1.12

3.88

0.64

Obtain a work experience or undergraduate research opportunity related to nanotechnology.

3.13

1.06

3.88

0.83

Seek information about internships or Co-op experiences with companies engaged in nanotechnology. Read a news story or popular magazine article about nanotechnology. Give a presentation related to nanotechnology to an audience I perceived as having more experience with nanotechnology than I. Explore nanotechnology as part of this course.

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4

Discussion

6

For students in this course on Engineering Applications of Nanoscience and Nanotechnology, individuals studying engineering applications of nanoscience and nanotechnology reported the greatest levels awareness of nanotechnology by being able to name a nanoscale-sized object, describing one way nanotechnology may benefit society or humankind, and naming an application of nanotechnology. The means for these items were highest at the beginning as well as at the end of the semester. The lowest reported mean related to awareness of nanotechnology at the beginning of the semester belonged to describing a process to manufacture objects at the nanoscale. However, the mean for this item was significantly higher at the end of the semester. Related to awareness is the concept of exposure to engineering applications of nanoscience and nanotechnology. At the beginning of the course, students reported the greatest amount of exposure by simply hearing the term nanotechnology. By the end of the course, students reported the greatest amount of exposure to concepts related to application of nanotechnology through reading something about nanotechnology and also by hearing instructors talk about nanotechnology application in class. These same students demonstrate the greatest amount of motivation to study nanotechnology through their plans to take a course in nanotechnology at the beginning as well at the end of the semester. Students also noted being motivated to explore the implications of nanotechnology by the end of the semester in which they studied engineering applications of nanoscience and nanotechnology.

5

Conclusion

References

[1] L. Jiao, N. Barakat, “Balanced depth and breadth in a new interdisciplinary nanotechnology course,” Journal of Educational Technology Systems, 40(1), 2011-2012, pp. 7587. [2] D. Worley, N. Kaabouch, M. Cavalli, K. Marasinghe, N. Oncel, D. Pierce, B. Tande, & J. Zhao, “In the beginning: Establishing a baseline of awareness, exposure, and motivation for students in a nanoscience and nanotechnology course,” Proceedings of the 2016 International Conference on Frontiers in Education: Computer Science & Computer Engineering, 2016, pp. 289-292. [3] R. Blonder, R. Mamlok-Naaman, “Learning about teaching the extracurricular topic of nanotechnology as a vehicle for achieving sustainable change in science education,” International Journal of Science and Mathematics Education, 14, 2016, pp. 345-372. [4] V. Fages, V. Albe, “Social issues in nanoscience and nanotechnology master’s degrees: The socio-political stakes of curricular choices,” Cultural Studies of Science Education, 10, 2015, pp. 419-435. [5] H.A. Diefes-Dux, "Nanotechnology Reflection Survey," 2014, Retrieved from https://nanohub.org/resources/20771 [6] H.A. Diefes-Dux, M. Dyehouse, D. Bennett, P.K. Imbrie, “Nanotechnology awareness of first-year food and agriculture students following a brief exposure,” Journal of Natural Resources & Life Sciences Education, 36(1), 2007, pp. 58–65.

At the beginning of the course on engineering applications of nanoscience and nanotechnology, students maintain a focus on in-class activities for the sources of information. However, they begin to look beyond the course by the end of the semester, as is expected, and their motivation to learn more about nanotechnology is enhanced in several categories. The preliminary findings of this portion of the study help us understand the student mindset when taking courses in this interdisciplinary format. Data analysis beyond the production of descriptive statistics will further our understanding of how to use student self-reported levels of awareness, exposure, and motivational variables in developing a nanoscience and nanotechnology curriculum.

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ABET-CAC Accreditation at University of Petra – Assessment Plan for Continuous Improvement Shakir M. Hussain*, Ghassan Issa*, Nuha El-Khalili*, Muhammad Abu Arqoub*, Nesreen A. Otoum* *Faculty of Information Technology, University of Petra, Jordan [email protected] [email protected] [email protected] [email protected] [email protected]

Abstract— Obtaining accreditation is a recognition of the quality of academic programs and an indication that the program meets the criteria and the standard, which is set by an accrediting agency. Academic institutions can demonstrate their commitments to maintain quality of their programs by an accreditation. This paper presents experience of successful ABET accreditation for the Computer Sciences (CS) and Computer Information Systems (CIS) programs at University of Petra, Jordan. These programs were reviewed for the first time by the ABET commission during fall 2016. An overview of the process of planning and designing to prepare for applying to the ABET accreditation, and then the implementation and assessment stages are described. The continuous improvement process designed and implemented in the faculty of IT is described in the paper. Finally, an example of program objectives rubric is presented. Keywords— ABET, accreditation, assessment plan, assessment quality, quality assurance, quality, Computing Accreditation Commission, CAC

1 Introduction Accreditation is a recognition of the quality of a program and it indicates that the program meets the standards and criteria set by an accrediting agency [1, 2]. Accreditation has a benefit for students, academic institute, and employers. Students are prepared adequately

for professional employment. Academic institutions demonstrate a commitment to quality education that inspires students to enroll to the program. Employers ensure that the graduates of accredited programs have attained the necessary knowledge and skills that are required to start their career [3]. The University of Petra (UOP) was established in 1991 as an all-girl higher educational institution with approximately 625 students. The university was granted the Jordanian Ministry of Higher Education (MoHE) license in the same year [4]. The Computer Science program was one of the first programs at the university to be established and licensed for operation in 1991 under the umbrella of the Faculty of Arts and Sciences. It was recognized and nationally accredited by the Jordanian Higher Education Commission in 1995. As the demand for studies in Information Technology grew in the mid to late nineties, other computer related programs were established, and a new Faculty of Information Technology (FIT) was approved in 2003. The FIT now runs four programs including Computer Science (CS), Computer Information Systems (CIS), Computer Networks (CN) and Software Engineering (SE). All programs offer Bachelor of Science degrees. FIT started in 2013 to obtain accreditation on their two undergraduate programs: CS and CIS. The Accreditation Board for Engineering and Technology (ABET), is an accreditation organization for college and university programs in applied science, computing,

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engineering, and technology. FIT put a main goal and priorities to obtain the accreditation certificate and begun to work hard on the study of all the requirements in order to apply and obtain the ABET for their CS and CIS programs. Obtaining accreditation is very important for both the academic institution and the students graduating from the program. Accreditation confirms that the institution or the program meets the minimum quality criteria. Some of the advantages of accreditation of the program or institutions are as follows [5, 6]: x Accreditation provides structured method to assess, evaluate, and improve the quality of the program x Accreditation helps students and families choose quality programs x Accreditation enables employers to recruit graduates that are well-prepared x Accreditation is used by registration, licensure, and certification boards to screen applicants x ABET provides a set of goals and objectives for accreditation and evaluates and certifies the engineering related education. After requesting an evaluation of its program, the faculty develops a methodology of assessing the performance with respect to outcomes that are defined in ABET criteria. Each program develops its objectives and performs internal evaluation to meet ABET criteria. In of this paper, we present the procedures that are used by FIT at University of Petra to accredit the CS and CIS undergraduate program.

2 Programs Outcomes The University of Petra has the Quality Assurance Certificate from the Higher Education Accreditation Commission (HEAC) in Jordan in 2015. It all started in 2011 when the UOP Quality Assurance (UOP-QA) office adopted and distributed new procedures and forms for documenting and linking the program outcomes with the courses offered in the program as an introduction to the process of measuring the effectiveness of the academic programs offered by the university. The UOP-QA standard

requires that any academic program outcomes be classified into four categories: Knowledge, Intellectual, Practical, and Transferable Skills. Different outcomes were aligned under the four categories in order to prepare students competitive in the IT marketplace. The outcomes alignment with the four categories as required by UOP-QA have been formed and applied in 2011 until FIT decided to apply for ABET accreditation for both CS and CIS programs. The dean formed special committee for getting ABET accreditation called FIT-ABET committee in order to study the ABET requirements and start working on it. FIT-ABET committee focused on the participation of all faculty members in the preparation and design process. The FIT-ABET committee started reviewing the curriculum of both CS and CIS programs to meet the ABET criteria. The recommendations were given to issue a new version (we call it ABET plan) for both CS and CIS plans and to make modifications on the description, outcomes, syllabus, textbooks, and assessment methods of 46 CS courses and 47 CIS courses in the plans. These modifications have been done by taking into consideration the satisfaction of both UOPQA and ABET requirements. The new curriculum approved by the faculty council was implemented in the first semester of 2014. Current students could officially transfer to the new plan according to the university rules in order to insure the satisfaction of ABET requirements of having graduate students of both programs. At the time of writing this paper, both the old and new, (ABET) CS and CIS curriculum plans are offered until old students graduate. Table I shows the new program outcomes for both CS and CIS programs. Program outcomes A to I are common for both CS and CIS programs. In addition, CS has J and K outcomes; while CIS has J outcome, which is different from the CS.J outcome. The required units for both old and new (ABET) CS and CIS programs are illustrated in Table II.

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Int'l Conf. Frontiers in Education: CS and CE | FECS'17 |

Table I: Program Outcomes of ABET CS and CIS Curriculum Program outcomes for CS & CIS A. An ability to apply knowledge of computing and mathematics appropriate to the program’s student outcomes and to the discipline. B. An ability to analyze a problem, and identify and define the computing requirements appropriate to its solution. C. An ability to design, implement, and evaluate a computer-based system, process, component, or program to meet desired needs. D. An ability to function effectively on teams to accomplish a common goal. E. An understanding of professional, ethical, legal, security and social issues and responsibilities. F. An ability to communicate effectively. G. An ability to analyze the local and global impact of computing on individuals, organizations and society. H. Recognition of the need for, and an ability to engage in, continuing professional development. I. An ability to use current techniques, skills, and tools necessary for computing practice. Program outcomes for CS Program outcomes for CIS J. An ability to apply J. An understanding of mathematical foundations, processes that support the delivery algorithmic principles, and and management of computer science theory in information systems the modeling and design of within a specific computer-based systems in a way that demonstrates application comprehension of the environment. tradeoffs involved in design choices. K. An ability to apply design and development principles in the construction of software systems of varying complexity

The differences in CS ABET plan from the old plan are in the major compulsory/elective/ supportive requirements while in CIS the differences are only in the major compulsory/elective requirements. CS and CIS plans have in common 49% of total units, which can be taken in the first two years; while the last two years develop students’ knowledge and skills in their majors. All courses

79

Table II: Required units for both old and new (ABET) CS and CIS programs Program Plan Total Units university requirements faculty compulsory requirements major compulsory requirements major elective requirements major supportive compulsory requirements free requirements

CS

CIS

Old Plan 133

ABET Plan 134

Old Plan 132

ABET Plan 133

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27

27

27

21

21

21

21

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9

6

12

6

13

25

9

9

3

3

3

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and their Intended Learning Outcomes (ILOs) were completely reviewed by the FIT-ABET committee and the faculty members to reconsider the ILOs of the course and align them with the program outcomes of each program. In 2014, FIT-ABET committee started preparing the Self Study Report (SSR) which was required by ABET. The ABET criteria for accreditation consists of nine items: (i) students (ii) program educational objectives (iii) students’ outcomes (iv) improvement (v) curriculum (vi) faculty (vii) facilities (viii) institutional support (ix) program criteria [3]. FIT-ABET committee completed the Self-Study Reports (SSR) for CS and CIS that contained all the information regarding the ABET criteria and submitted it to ABET in July 2015. FIT-ABET committee did their best to prepare wellwritten and well-organized SSRs for CS program and CIS program. The 2016-2017 ABET commission has been selected the UOP SSRs for display as wellprepared Self-Study Reports at the 2017 ABET Symposium which is held in Baltimore, MD.

3 Assessment Methods The improvement process takes into consideration internal and external measurement tools. Figure 1, show the overall improvement process, which allows for continuous evaluation and improvement of the

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program. When designing this process, the following aspects were taken into consideration to ensure its effectiveness: x The frequency of assessment and evaluation activities is clear. x The responsibilities of faculty members, committees and departments and college councils are well defined. x Feedback of assessment are reflected and documented on the program. x Program constituents are involved in the process. Internal measurement tools include faculty evaluation of courses, performance indicators assessment results, students’ assessment of courses outcomes, and graduating seniors’ survey. External measurement tools include alumni survey, employee survey, and competency exam results in addition to the advisory board survey The Results of the evaluation process, which illustrate the degree of attainment of the program objectives and outcomes, are obtained from the following: a. Graduating senior project: the result of this survey assist in improving the program objectives, program outcomes, and courses taught in the curriculum. b. Performance Indictors Assessment results: faculty members using variety of methods do Assessments of the course outcomes. These results are used to calculate the attainment of program outcomes. c. Electronic Students’ Evaluation of the Instructor: Students are encouraged at the end of each semester to evaluate the course and the instructor using an electronic survey on the UOP website. d. Instructor Evaluation Form for a course: According to UOP-QA standards, the course instructor fills this form at the end of each semester, commenting on course content, assessment, and teaching. e. Student’ indirect assessment of the attainment of the course outcomes: Towards the end of each semester, students are encouraged to answer an electronic survey for each attended course. The survey asks the student to assess the degree that the

course has delivered the outcomes it was supposed to deliver. f. Direct assessment of students’ attainment of course ILOs: all instructors use An Excel template file at the end of each semester to calculate the assessment of ILOs of each given course at the semester. FIT-ABET used two methods of assessment for the program outcomes evaluation: direct and indirect assessment methods. The direct assessment method comprised of students work (exams, assignment, projects, quizzes … etc.), which may use rubric based assessment for all or part of the student work. Indirect assessment is based on different surveys, which utilizes rubric assessment. A rubric consists of three components: descriptor, performance indicator, and a scale. FIT-ABET developed rubric for all surveys and for the CS and CIS program objectives. Table III shows the CIS program objective rubric.

Figure 1: The overall evaluation and improvement process

4 ABET Visit The Faculty of Information Technology at UOP prepared well for the ABET's visit, which took place in October 2016. The ABET reviewers team visited FIT-UOP and evaluated the CS and

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CIS programs. All materials and related documents for both CS and CIS programs were prepared well and presented appropriately to the ABET team reviewers. At the end of their visit, they provided their observations verbally on the CS and CIS programs. Their observations were neither deficiency nor weakness, but only three concerns were made. In ABET terminology, “Concern indicates that the program currently satisfies a criterion, policy or procedure; however, the potential exists for the situation to change such that the criterion, policy, or procedure may not be satisfied” [6]. Two concerns for both programs were related to the following: x Criterion 3, Student Outcome, which states that the program must enable students to attain, by the time of graduation, an ability to communicate effectively with a range of audiences. Currently students communicate with their peers and team members, make required presentations, and participate in competitions. Classes and presentations are conducted primarily in English; currently students have the requisite verbal skills. However, with the continued influx of students from multiple backgrounds and nationalities, a potential exists that the criterion may not be met in the future. • Criterion 8, Institutional Support, which states that resources available to the program must be sufficient to attract, retain, and provide the continued professional development of a qualified faculty. Financial resources are available to attract and retain faculty. Faculties are offered fixed term contracts with a review. Continuing faculty has expressed a desire for formal process indicating that their position is stable and ongoing. This lack of performance may make the position less attractive in the future and can lead to a problem with retention. In addition, another concern for CS program was related to Criterion 6, Faculty, which states that the competence of faculty members must be demonstrated by such factors as education, professional credentials and certifications,

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professional experience, ongoing professional development, contribution to the discipline, teaching effectiveness, and communication skills. It stated that this program currently satisfies this criterion, records indicate that one of the factors relevant to this criterion, i.e., contributions to the discipline, is currently declining. If this extends to other factors, a potential exist that this criterion may not be satisfied in the future. The previous concerns were written on "ABET CAC Accreditation Commission Program Form", which was delivered to the UOP president by the ABET reviewer. UOP and FIT immediately start working on fixing up the concerns to ensure the satisfaction of all criteria.

5

Conclusions

This paper presents experience of successful ABET accreditation for the CS and CIS programs. The process of outcomes and program objectives assessment to achieve ABET-CAC accreditation presented in this paper depended six type of measurements, which consists of direct and /or indirect assessment. These measurements are: 1- Graduation senior project 2- Performance indicators of assessment results for program outcomes 3- Student evaluation of instructor measurements 4- Instructor evaluation of a course 5- Student indirect assessment attainment of the course outcomes 6- Direct assessment of students’ attainment of the course ILOs These measurements need to be implemented as a main part of an effective plan and integrated into the everyday work of the faculty for any CS/CIS program that aspire to obtain successful ABET accreditation. In addition, continuous improvement process should be designed with clear responsibilities, frequency of actions and preferably with tools to support its implementation. In addition, continuous improvement process should be designed with clear responsibilities, frequency of actions and preferably with tools to support its implementation.

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Table III: CIS Program Objectives Rubric Program Objective

Objective Professional Career

Objective Lifelong Learning and Graduate Studies

Objective Leadership and Community Service

Objectives components

Rubric 4

3

2

1

Assume successful professional career in the CIS Industry

All graduates Successfully promoted in IT industry

More than fifty present of graduates successfully work in IT industry

Fewer than fifty present of graduates successfully work in IT industry

most of graduates work outside the IT industry

Ability to assume successful professional career in the CIS Industry

Graduates have high ability level to assume successful professional career

Graduates have acceptable ability level to assume successful professional career

Graduates have minimal ability level to assume successful professional career

Broad understanding of the fundamental concepts, methodologies and tools, and applications of CIS

Graduates completely understand the fundamental concepts, methodologies and tools, and applications of CIS

Graduates mostly understand the fundamental concepts, methodologies and tools, and applications of CIS

Graduates partially understand the fundamental concepts, methodologies and tools, and applications of CIS

Ability to adapt to new technologies

Graduates have high ability level to adapt to new technologies

Graduates have acceptable ability level to adapt to new technologies

Graduates have minimal ability level to adapt to new technologies

Ability to pursue advanced education

Graduates have high ability to pursue advanced education

Graduates have sufficient ability to pursue advanced education

Graduates have limited ability to pursue advanced education

Ability to conduct research

Graduates have high ability to conduct research

Graduates have limited ability to conduct research

Show effective communication skills

Graduates show high level of effective communication skills

Graduates have moderate ability to conduct research Graduates show satisfactory level of effective communication skills

Graduates have unacceptable ability level to assume successful professional career Graduates have limited understanding of the fundamental concepts, methodologies and tools, and applications of CIS Graduates have unacceptable ability level to adapt to new technologies Graduates have unacceptable ability to pursue advanced education Graduates have unacceptable ability to conduct research Graduates fail to show effective communication skills

Show innovative thinking skills

Graduates show high level of innovative thinking skills Graduates have complete knowledge of professional and ethical manners

Graduates show satisfactory level of innovative thinking skills Graduates have good knowledge of professional and ethical manners

Graduates show very limited level of innovative thinking skills Graduates have partial knowledge of professional and ethical manners

Graduates fail to show innovative thinking skills

Graduates show high support for the community

Graduates show moderate support for the community

Graduates show limited support for the community

Graduates show no support for the community

Knowledge of professional and ethical manners

Support for the community

Graduates show very limited level of effective communication skills

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6 Acknowledgements

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Improvement”, WORLDCOMP 2012, FECS'12, July 16-19, 2012 Las Vegas, Nevada, USA

This research received financial support towards the cost of its publishing from the Deanship of Research and Graduate Studies at University of Petra, Amman - Jordan.

7 References [1] J. Enderle, J. Gassert, S. Blanchard, P. King, D. Beasley, P. Hale, & D. Aldridge, The ABCs of Preparing for ABET, IEEE Engineering in Medicine and Biology Magazine, 22(4), 2003, 122-132. [2] Khalid A. Buragga and Muhammad Asif Khan,” Successful ABET Accreditation at King Faisal University – Rubric based Assessment Plan for Continuous

[3] K.J. Milligan, Outcome-Based Accreditation and ABET, Proceeding of the international Conference on Transformations in Engineering Education, ICTIEE 2914, Springer India 2015. [4] Higher Education Commission, [http://www.heac.org.jo/]

Accreditation Jordan,

[5] Peggy Brouse, Hakan Yurt, Process for ABET Accreditation in a Systems Engineering Undergraduate Program, Volume 16, Orlando, FL, July 9–13, 2006, Pages 1780– 1792. [6] Accreditation Board for Engineering and Technology (ABET), [http://www.abet.org/

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SESSION TEACHING METHODOLOGIES AND STRATEGIES, ASSESSMENT METHODS, AND RELATED STUDIES Chair(s) TBA

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Aptitude and Previous Experience in CS1 Classes Lisa L. Lacher1 , Albert Jiang2 , Yu Zhang2 , and Mark C. Lewis2 1 Department of Computer Science, University of Houston at Clear Lake, Clear Lake, TX, USA 2 Department of Computer Science, Trinity University, San Antonio, TX, USA Regular Research Paper Abstract— This paper looks at different factors and how they correlate with student success in an attempt to determine effective ways to divide students between sections that maximize the chance of student success. While it has been relatively common to separate students with previous experience from those who do not have it, it is not clear that this is the most effective strategy. We find that aptitude, as determined by the Computer Science aptitude test from the University of Kent, is a better metric to determine the likelihood of student success. Once students are separated by aptitude, then experience is the next best metric. Values such as student comfort and their expected grade at the beginning of the semester, which have been indicated as good predictors in previous work, did not prove as useful in our data set, especially after separating by aptitude. Keywords: CS1; aptitude; experience; novice programming

1. Introduction The idea of separating advanced students from novice students is not new [4], but the idea has become more popular of late as a means of helping to retain underrepresented groups. Even though interest in computer science has grown dramatically over the past few years, interest from underrepresented groups has grown very little. The 2015 Taulbee report shows that for the eighth straight year, there was an increase in the number of new undergraduate computing majors. This year’s respondents reported 22.5 percent more new majors than last year. However, the proportion of women only grew 1.6 percent and the proportion of Asians grew 1.7 percent. Changes in all other ethnicity categories were less than one percent [22]. We are not doing enough to broaden interest in and access to computing. Although access to a computer science education is still not universal in K-12, it is increasing [1]. Many schools are offering more computing courses. This continued investment in computer science in K-12 will spur even more growth. However, due to the many challenges such as lack of standards and qualified teachers [20], the students entering CS programs come in with inconsistent preparation. Often students who want to major in computer science do not have the computational thinking or mathematical preparation necessary to succeed in college-level coursework. Yet, other

students have obtained the skills and training necessary to succeed. The US Bureau of Labor Statistics (BLS) predicts one in every two new STEM jobs in the country will be in computing occupations, with more than 150,000 job openings annually making it one of the fastest growing occupations in the United States. Although the BLS projects that employment, specifically for computer programmers, will decline 8 percent, research has found that there were as many as 7 million job openings in 2015 in occupations that required coding skills and that programming jobs overall are growing 12 percent faster than the market average. In part, this is because there is a need for programmers in many other fields, such as finance, manufacturing, and health care [6] and all these jobs will not necessarily be filled by computer science majors. Not every future job will involve programming. Many universities are including more computing courses in their general undergraduate curriculum because of its other benefits such as promoting critical and computational thinking. Computational thinking teaches a person how to tackle large problems by breaking them down into a sequence of smaller, more manageable problems. It helps them go from specific solutions to general ones because computation thinking involves creating models of the real world with a suitable level of abstraction, and focus on the most pertinent aspects. The application of this approach stretches beyond programming. Fields as diverse as business, physics, biology, mechanical engineering, music, and archeology are applying this computational approach. Thus, it should be easy to see that coding is becoming the most in-demand skill across industries, which may also increase the number of students joining introductory computer science classes. Because of the vast increase in the number of students taking introductory computer science courses, the wide variety of student skill sets entering the classroom, and a disappointing lack of interest from underrepresented groups, industry leaders, who need the diversified talent that universities produce, have tried to help. Google created an awards program [12], which aims “to support faculty in finding innovative ways to address the capacity problem in their CS courses.” In Google’s RFP, they suggest separating novices from those with experience. In that way, it is easier to make a course more interesting to advanced students and less intimidating for novices thus resulting in improved retention. The question is, how should we actually separate

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the students?

2. Background Dealing with diverse skills in introductory computer science courses has been a problem that researchers have been trying to tackle for many years. Students who find it easier to understand that material will get bored and lose interest if the material is covered at too slow a pace. However, if the material is covered too quickly, those with less experience often get lost causing them to feel incompetent. Students with more experience and a better understanding of the material can typically dominate classroom discussions leaving the less experienced to simply listen and often doubt their ability to succeed in the class. Many educators use collaborative classroom techniques, such as pair programming, in order to better engage the students in the material. Research suggests that the benefits of pair programming are many, including increased introductory course success rates, increased retention, higher quality software, higher student confidence, and improvement in learning outcomes [10]. However, if the two partners do not have similar levels of prior experience and aptitude then this technique can be a disaster. The stronger students do not feel that they benefit from the partnership and that the weaker partner is holding them back. The weaker students do not feel that they learn as much and have even less confidence in their abilities when working with a stronger partner. Students who feel that they are "at the same level" are more able to make equal contributions to the shared tasks thus facilitating more positive partnerships and learning outcomes[19]. Rather than separate students into different classes, some researchers have sought to level the playing field by using uncommon programming languages [16], or no programming languages with an emphasis on understanding abstraction [7]. Neither of these methods met with very promising results in retaining students. Other researchers have created separate courses and divided students based on whether they were computer science majors or nonmajors. They then customized the courses for the different majors. This approach has generally met with success for the non-majors because the students appreciated that the course was tailored to their major, they felt they could be creative, and they found the material relevant [8], [5]. Based on this success, researchers used similar materials in a CS0.5 course designed for students who think they want to major in computer science, but who had little or no background with programming [18]. They created a placement tests that required the students to write a short program in the programming language of their choice that required using nested control structures to determine whether or not a student should take the CS0.5 class or whether they should take the more advanced class. By splitting the students into different courses, it was reported to both retain more students as well as help more students pass the CS0.5 course. Harvey

Mudd has also had great success in separating computer science students based on previous experience. In 2005, Harvey Mudd’s introductory programming course “focused on hard-core programming, appealing to a particular kind of student – young men, already seasoned programmers, who dominated the class. This only reinforced the women’s sense that computer science was for geeky know-it-alls...To reduce the intimidation factor, the course was divided into two sections – ‘gold,’ for those with no prior experience, and ‘black’ for everyone else.[9].” There has been an abundance of research in the area of what student variables are predictors of success. Mathematical ability has been found to be an important predictor of performance in introductory computer science courses in several studies [2], [21]. Similarly, performance in and experience of science subjects has also been shown to be important [3]. Comfort level with a course was found to have a positive effect on performance [21]. Some researchers found that the strongest single indicator of success was whether or not students expected to get an A in the class [17]. Previous programming experience has often been cited as a predictor of programming success; however, studies have also found that both prior programming experience and non-programming computer experience (e.g. experience of computer applications, emailing, Internet usage, and game playing) to be related to programming performance [11], [3]. Trinity University also decided to separate students between different introductory computer science courses in an effort to give students the best experience possible. It is easy to separate students based on major and we have created customized courses to serve that population. However, at our university, students are not required to declare a major until the end of their sophomore year. Thus many students do not know what major they want to pursue and do not know which course to take in their first year. Beyond the customized major’s course, we have three different levels of introductory programming courses: one CS0.5 course, and two CS1 courses with one section offered at an accelerated pace and the others offered at normal paces. We have still struggled on what method to use to advise students as to which course would best suit them - especially between the CS0.5 and normal paced CS1. We were curious to see if there were even better predictors of success than previous experience. The rest of this paper describes our efforts to answer that question.

3. Study Design This study looks at six sections of CS1 taught by three instructors in the fall of 2015 that all used a flipped approach. One of the sections was designated as an “accelerated” section for students who had previous programming experience. Students entered this course through a combination of self-selection and consultation with faculty advisers. The

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selection of section was, in the end, up to students. There were a few students in the “accelerated” section with little to no previous programming experience who chose to be there for other reasons. In addition, there were students who did have previous experience who probably should have been in the “accelerated” section, but who did not go into for a variety of reasons, including schedule conflicts. The “accelerated” section moved slightly faster than the other sections, covering two additional topics near the end of the semester. The similarity in instruction style and material allowed all the sections to use roughly the same assessment instruments for the midterm, the final, and five quizzes. In addition to the grades for those assessments, students were asked to complete the University of Kent CS aptitude test[15] and a general survey of background information at the beginning of the semester.

3.1 Hypotheses Based on our previous research, we developed three hypotheses. • Hypothesis 1: The aptitude and previous experience of students would both positively impact grades. • Hypothesis 2: The aptitude and previous experience of students would highly correlate with each other. • Hypothesis 3: Students who had a higher comfort level with CS would earn higher grades. The first hypothesis really has two parts, positive correlation of grades with previous experience and positive correlation with aptitude. Both parts of this hypothesis not only seem logical, but have been seen to various degrees in a variety of previous studies[13], [14]. The second hypothesis is based on the idea that these two factors can reinforce one another. Having previous experience in thinking logically and algorithmically should boost student performance on the aptitude test. While the test doesn’t include any coding directly, we believe that the skills used on it should be enhanced by previous experience with programming. Conversely, it seems reasonable that high aptitude students might be more likely to gravitate toward CS, and therefore should be more likely to have some experience in it, all other factors being equal. The third hypothesis is similar to the first and is also based on results from previous research[21]. Students who feel comfortable with computers and their skills in CS would be expected to perform better than those who are nervous about working with technology and who doubt their abilities. We would also expect students with previous experience to express a higher level of comfort, so this hypothesis would naturally follow from the first hypothesis.

3.2 Participating Subjects There were eighty-five undergraduate students enrolled in the five sections of CS1 course at Trinity University during the fall semester 2015. There were fourteen students in the

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sixth section section that contained the accelerated students. Sixty-nine of the students from the first five sections and ten from the sixth section participated in the study. Students who withdrew from the class were not included in our data.

3.3 Artifacts Each course required the students to take in-class quizzes, do homework assignments, and take tests. The courses used identical in-class quizzes for the quizzes that were included in the study. There were minor differences in the material presented late in the semester between the instructors. Two instructors offered a sixth quiz, which was not included in the study. Each student took a midterm and a final at the same point in the semester. The midterm tests were identical for the five standard sections. The accelerated section had a minor difference in the midterm due to a slightly faster pace. Although the majority of the final test questions were identical between all sections, there was one question that was different - once again due to the pace of the courses. One rubric was created for each quiz to aid with consistency in grading. The department had two teaching assistants. Each quiz, for all sections, was graded by one of the teaching assistants using the rubric as a guide. The tests were graded by all three of the instructors with each instructor being responsible for a certain set of questions. This ensured consistency in grading across all tests. Each day in class, the students worked on writing code solutions for a variety of problems posed by the instructor. The students also completed four homework assignments throughout the semester. However, those were not included in the study because students who put forth time and effort basically received full or nearly full credit. Because we did not track time and effort spent on homework assignments, those grades do not provide significant information for analysis.

3.4 Experimental Procedure To investigate the hypotheses, students were asked to not only do the work required for the course, they were also asked, at the beginning of the semester, to fill out information surveys and take an aptitude test. 3.4.1 Course Assessments To determine the course semester grade for this study, inclass quizzes scores contributed twenty-five percent of the grade, and the midterm and final exam contributed equally to the remaining seventy-five percent of the grade. Actual course grades used a somewhat different formula to include assignments and other coding activities that are not directly factored into this work as they often wind up having a Boolean nature based on whether or not the students put in the time to complete them.

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3.4.2 Pre-Tests Students were asked at the beginning of the semester to complete a general survey and the computer programming aptitude test. • General Survey: This was an nineteen question general information survey administered at the beginning of the semester. In this survey students were asked questions such as: – previous computer experience (Internet searches, E-mail, Chat rooms, Discussion groups, games, or productivity software such as word processing, spreadsheets, etc.) – previous programming experience (formal programming course in high school, self-initiated learning of a language, or none) – what programming languages they have learned (if any) – confidence in their hardware technical skills (marginal, good, very good, excellent) – confidence in their software technical skills (marginal, good, very good, excellent) – what grade did they expect (A, B, C, D, F) – comfort level with this course (extremely uncomfortable, slightly uncomfortable, slightly comfortable, very comfortable) There was no time limit to complete this survey. For the analysis, the responses had to be converted to numeric values. This was straightforward for everything except for experience. We converted the experience responses to a 0-5 scale using the following rubric. 0 – No previous experience. 1 – Previous experience with a markup language like HTML, but not a programming language. 2 – Brief informal experience with a programming language. 3 – Self-taught with one programming language. 4 – Formal instruction with a programming language or self-taught with two programming languages. 5 – Formal instruction with 2+ programming languages or self-taught with 3+ programming languages. • Computer Programming Aptitude Test: This test was created by the University of Kent [15]. It consists of twenty-six questions composed of numerical problem solving, logical reasoning, attention to detail, pattern recognition, and the ability to follow complex procedures. Because it does not require students to have any programming knowledge, it is very appropriate for CS1 students. Numerical problem solving is similar to the logical thinking and trouble shooting skills required in programming. Pattern recognition is necessary in understanding the representations of symbols and pro-

cedures. It leads to the necessary attention to detail required to do things such as find missing semi-colons or misspelled variable names. The ability to follow complex procedures is necessary to trace code. Students were given forty minutes to complete this test.

4. Analysis and Results Using the data collected from the survey, the aptitude test, and the grades, we look at Pearson correlation coefficients for all the pairs of quantified data to test our original hypotheses.

4.1 Hypothesis 1 The aptitude and previous experience of students would both positively impact grades. Aptitude and experience proved to be the values most strongly correlated with outcomes, supporting this hypothesis. The Pearson correlation coefficient for grades with normal aptitude scores was 0.529 for all students, while that for grades and our numeric experience scores was 0.433. Both of these are well above the 0.3 level that indicates significance for the number of values in our data set. The correlation between grades and aptitude was the strongest correlation we saw for any pair of values. One significant difference between aptitude and all the other values other than grades was that the point scale was much more fine grained, with values ranging from 5 to 25. To make sure that this was not the reason for the high correlation, we also looked at a course grained representation of aptitude. The University of Kent page, where the aptitude test can be found, suggests that scores of 12 and below are low aptitude, scores in the 13-18 range are medium aptitude, and scores of 19 and higher are high aptitude. We assigned each of these categories to the values 0, 1, and 2 to produce a course grained measure of aptitude. This produced a Pearson correlation coefficient of 0.507, making it clear that the correlation strength is not simply due to the nature of the data. Figure 1 shows grades plotted against both aptitude and experience for all the students in our sample. The gray dots indicate students who were in the accelerated section. The dot sizes in the aptitude plot indicate experience, while those in the experience plot indicate aptitude. So small dots on the left frame indicate less previous experience while small dots on the right indicate low aptitude test scores. This allows you to visually see the relationship between all three values. Both plots have linear fit trendlines. The plots make it very clear that the while grades are positively correlated with both aptitude and experience, the aptitude score is a far better predictor of outcomes overall, as very few high aptitude students did poorly and no low aptitude students finished with a high grade. Given that the accelerated section was self-selected in a way that could potentially make it different, we decided to

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y = 2 . 2 x + 2 4, r2 = 0 . 2 7 4

y = 3 . 8 x + 5 6, r2 = 0 . 1 8 7

90 80 70

Grade

60 50 40 30 20 10 0 5

4

3

2

1

0

25

20

15

10

5

0

Aptitude

Experience

Fig. 1: This chart shows grades plotted versus both aptitude and student experience for all six sections of the course. The dots in gray are students in the “accelerated” course. On the plot for aptitude, the size of the dots indicates experience level, while the dot size on the experience plot indicates aptitude. Linear fits to the data are shown for both.

also calculate correlation coefficients using just the data from the five standard sections. This had effectively no impact on the correlation between grades and aptitude. However, it had a profound impact on the correlation between grades and experience, which dropped down to 0.277, which is slightly below the level needed to be significant. It isn’t clear to us exactly what this means. It could be a result of the small sample size, but it could also indicate that self-selection for the accelerated section is “measuring” experience in a different way than survey questions did. This doesn’t seem too unreasonable as it is easy for a student to put on a survey that they have certain experience, but there is potentially a real cost to signing up for the “accelerated” section, knowing that it would likely move faster and cover more material.

4.2 Hypothesis 2 The aptitude and previous experience of students would highly correlate with each other. The numerical experience scores and the aptitude results had a Pearson correlation coefficient of 0.258, below the level needed to indicate significance for our sample size, indicating that this hypothesis did not prove true for our sample. The lack of correlation is also apparent in figure 1 by the size of the dots. If these values were strongly correlated, the dots in each plot would get larger moving from left to right, which is clearly not true in general. It is true that the lowest aptitude students also had no

previous experience, which is likely the reason why the correlation coefficient was as high as it was. On the other end of the spectrum, it is clear that high aptitude was not merely the domain of experienced students. Many of the students who reported no previous experience at all had “high” aptitudes (values of 19 or higher). There was also one student with a reasonable level of previous experience who fit into the low aptitude category (12 or below).

As we did for hypothesis 1, we also looked at the correlation coefficient for only the standard sections to see if the self-selecting group might be altering the results in some way. Indeed, the groups do appear to be somewhat different as the correlation coefficient drops for the standard sections down to 0.148. While both correlation coefficients are below the level to be statistically significant, the difference between the accelerated section and the standard sections is actually quite remarkable. For the accelerated section, the correlation coefficient between aptitude and experience is 0.637. Note that while this is quite high, there are only 10 data points in this sample, so this is still only on the border of being statistically significant. The difference in correlation here does provide another line of argument that students reporting experience on a survey and being willing to take a special section that is intended for those with experience are somewhat different things.

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y = 6 . 8 x + 5 0, r2 = 0 . 1 2 1

same way.

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4.4 Other Correlations

80 70

Grade

60 50 40 30 20 10 0 3

2

1

0

Comfort

Fig. 2: This chart shows grades as a function of student reported comfort with Computer Science at the beginning of the semester. The dot size shows the value of self-reported experience on the same survey.

4.3 Hypothesis 3 Students who had a higher comfort level with CS would earn higher grades. The student’s reported comfort level with Computer Science and their grades had a correlation coefficient of 0.348, indicating that this hypothesis is true for our sample. However, the correlation was weaker than for either experience or aptitude. Part of the reason behind this hypothesis was that students with experience would likely feel more comfortable. Indeed, the comfort and experience values had a Pearson correlation coefficient of 0.485. Figure 2 shows grades as a function of comfort with the dots sized by experience. The dots clearly get larger moving from left to right, supporting this interpretation. As with experience, it is interesting to look at how the correlation between outcomes and comfort level differed between the accelerated section and the standard sections. As with the correlation between grades and experience, there is a stronger correlation between grades and comfort with CS, 0.447, in the accelerated section than in the standard sections, 0.290. As happened with the correlation between grades and experience, the correlation between grades and comfort drops slightly below our threshold for statistical significance for the standard sections and is well below the value for the accelerated section. This further strengthens the idea that there is a strong link between comfort and experience and that both impact the outcome in roughly the

For the sake of completeness, we calculated Pearson correlation coefficients for all pairs of values from our data set. These values are shown in tables 1, 2, and 3. When looking at these tables, keep in mind the sample sizes. In particular, there were only ten data points in the accelerated section, so the threshold for being considered statistically significant is quite large, 0.6. As such, little, can be drawn from table 3 alone. Instead, it is presented primarily for comparison to the standard sections. Note that these tables present aptitude in two ways. The first is the actual test scores that ranged from 5 to 25. The second was the more coarse grained division with values of 0, 1, and 2 that were based on the low, medium, and high aptitude categories recommended by the test creators. As a reminder, this data was looked at because all the other values, except for grades, were also fairly coarse grained with 3-5 possible values, and this alternate aptitude score is helpful to make it clear that correlations aren’t being altered significantly by what are basically binning effects. There are a few values in these tables that stand out and, as such, are probably worth briefly discussing. The first thing that jumps out is that almost every value in each table is positive. This indicates that while many values might not be correlated in a statistically significant way, nothing was anti-correlated. Given that almost everything is positive, the few negatives jump out when looking at the tables. It is important to note that all the negative values have a small magnitude, well below what is required to be statistically significant. More interesting is that the few negative values all appear in correlations between hardware technical skill and aptitude. Looking more generally, it is clear that student’s selfassessment of hardware technical skill is not predictive of their course outcomes or their aptitude scores. It is possible that some of this is a result of the course in question being primarily an introduction to programming, and therefore strongly software based, but that doesn’t explain why students’ self-assessment of software technical skills are also effectively not correlated with grades or aptitude. The last column in the tables, labeled “CS2”, is for data where each student had a 1 if they took the second semester course within the next year after this study was conducted and a 0 if they didn’t. This column is of interest because many Universities use the approach of separating sections as a tool to increase retention. Oddly, the grade in CS1 was not correlated with students continuing on to CS2 in a statistically significant way. Indeed, the only value in our data set that was correlated with continuing on to the second semester was their self-described comfort with Computer Science at the beginning of the original class, with aptitude being the next closest with a value that was just

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Grade Aptitude Software Hardware Comfort Exp. Grade Aptitude (0-2) Experience

Grade Aptitude Software Hardware Comfort Exp. Grade Aptitude (0-2) Experience

Aptitude 0.523

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Table 1: Grade Results (All Sections, 83 students with grades) Software Hardware Comfort Exp. Grade Aptitude (0-2) 0.221 0.076 0.348 0.288 0.507 0.256 0.032 0.349 0.239 0.902 0.481 0.276 0.165 0.244 0.267 0.190 -0.012 0.517 0.346 0.218

Table 2: Grade Results (Standard Sections, 73 students with grades) Aptitude Software Hardware Comfort Exp. Grade Aptitude (0-2) 0.522 0.067 0.074 0.290 0.253 0.510 0.184 -0.019 0.330 0.233 0.897 0.545 0.240 0.142 0.129 0.279 0.137 -0.079 0.544 0.305 0.212

below our threshold for statistical significance. It is worth noting though that the correlation between going on to the second semester and aptitude is another one of the values that was very different between the standard sections and the accelerated section. The correlation between those value was actually quite low for the standard sections and was only significant in the overall data set because there was an extremely high correlation in the accelerated section. It was found by [17] that the strongest predictor of student success was their grade expectations going into the course. This was not the case in our data set as both the full data set and the standard sections had a correlation below 0.3. This might be in part to the nature of the students/institution and the fact that the students, mostly in their first year of college, predicted very high grades. Specifically, 35 students predicted they would get an A, 27 predicted they would get a B, and only one predicted a C.

5. Conclusions and Recommendations The practice of creating separate sections of courses for students with previous experience has been done for some time, and has recently seen a resurgence as a possible tactic for increasing retention, especially among underrepresented groups. The standard logic for this is that students with previous experience can often intimidate those who don’t have previous experience. While our data set does not allow us to explore this line of argument, it does allow us to make some recommendations in regards to student success. As instructors, we should strive not just to retain students, but

Experience 0.433 0.258 0.401 0.237 0.485 0.463 0.248

CS2 0.258 0.299 0.282 0.096 0.370 0.248 0.263 0.260

Experience 0.277 0.148 0.255 0.284 0.454 0.452 0.110

CS2 0.142 0.207 0.224 0.125 0.334 0.287 0.141 0.158

to put our students into a position to succeed. In that way, even students who don’t continue on with CS in college will have a positive view of CS that they will take into later life. This will benefit them greatly when they get to the point where they realize that technology is essential for their path in life, even if that path wasn’t CS focused. Another reason we want to focus on student success instead of retention is that we have to acknowledge that because CS isn’t taught to most students in secondary school, students often enter the early courses with no idea of what CS really is. For those students, the decision to pursue a different major might actually be the correct life path, and we don’t want to damage their GPA at the beginning of their college career because they entered a class that they were unprepared for and unaware of what it really involved. The failure of the second hypothesis was quite surprising to us as it means that an ideal division of students to promote success can’t be done simply based on experience. Student aptitude really should be considered at well. The first major conclusion we would draw in this regard is that students with a lower aptitude should probably be given additional assistance to insure their success. Based on the data shown in figure 1 it seems reasonable to use a cutoff around 15. Note that this is three points higher than the “low aptitude” level recommended by the University of Kent. While it is possible that more data might point to a lower threshold, it is clear that students with the lowest aptitude scores are extremely unlikely to perform well in a rigorous CS1. For this reason, it is probably advisable to ask such students to start with a

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Grade Apt Software Hardware Comfort Exp. Grade Aptitude (0-2) Experience

Grade

Table 3: Grade Results (Accelerated Section, 10 students with grades) Aptitude Software Hardware Comfort Exp. Grade Aptitude (0-2) 0.590 0.529 0.350 0.447 0.824 0.551 0.454 0.340 0.402 0.338 0.934 0.403 0.269 0.297 0.561 0.302 0.481 0.318 0.398 0.517 0.302

Aptitude 0.184

Table 4: Grade Results (All Sections, Aptitude > 15 only) Software Hardware Comfort Exp. Grade Aptitude (0-2) 0.182 0.118 0.258 0.280 0.264

CS0.5 or some other course that is less rigorous and helps to both build their skills and give them a taste of what CS is really about in a manner where they are more likely to succeed. For those who enjoy this first class, hopefully it will prepare them to go on to CS1. For those who don’t, it should give them an idea about what the field is in a way that doesn’t set them back academically. In addition to showing that low aptitude students likely need additional assistance, our data also shows that selfreported values for experience and comfort, while also correlated with grades, are not a proxy for aptitude. As such, the type of intervention to put students in the correct starting class truly needs to be done by administering an aptitude test. This doesn’t mean that there shouldn’t be special sections for experienced students. After throwing out the students with an aptitude of 15 or below, which you can see in figure 1 is a small fraction of total students, there is still a correlation between experience and grade with a coefficient of 0.394. Table 4 shows just the correlation of grade outcomes with all the other values for this high aptitude subset. Based on this, and our own anecdotal evidence in teaching an accelerated course, we believe that there is still a benefit to separating students based on previous experience. In fact, based on this data, experience is the only value with a statistically significant correlation to grades in the higher aptitude subpopulation, making it the only value worth considering when dividing student after the low aptitude students have been moved to a class that better meets their needs.

References [1] Trends in the State of Computer Science in U.S. K-12 Schools. 2016. [2] J. Bennedsen and M. E. Caspersen. An investigation of potential success factors for an introductory model-driven programming course. In Proceedings of the First International Workshop on Computing Education Research, ICER ’05, pages 155–163, New York, NY, USA, 2005. ACM. [3] S. Bergin and R. Reilly. Programming: Factors that influence success. SIGCSE Bull., 37(1):411–415, Feb. 2005.

Experience 0.735 0.637 0.602 0.269 0.528 0.726 0.667

Experience 0.394

CS2 0.476 0.775 0.274 0.033 0.417 0.043 0.829 0.327

CS2 0.252

[4] K. B. Bruce. Attracting (& keeping) the best and the brightest: An entry-level course for experienced introductory students. In Proceedings of the Twenty-fifth SIGCSE Symposium on Computer Science Education, SIGCSE ’94, pages 243–247, New York, NY, USA, 1994. ACM. [5] J. P. Cohoon. An introductory course format for promoting diversity and retention. In Proceedings of the 38th SIGCSE Technical Symposium on Computer Science Education, SIGCSE ’07, pages 395–399, New York, NY, USA, 2007. ACM. [6] L. Dishman. Why coding is still the most important job skill of the future. Fast Company, Jun 2016. [7] C. Gibbs and Y. Coady. Understanding abstraction: A means of leveling the playing field in cs1? In Proceedings of the ACM International Conference Companion on Object Oriented Programming Systems Languages and Applications Companion, OOPSLA ’10, pages 169– 174, New York, NY, USA, 2010. ACM. [8] M. Guzdial. Exploring hypotheses about media computation. In Proceedings of the Ninth Annual International ACM Conference on International Computing Education Research, ICER ’13, pages 19– 26, New York, NY, USA, 2013. ACM. [9] K. Hafner. Giving women the access code. Apr 2012. [10] B. Hanks, S. Fitzgerald, R. McCauley, L. Murphy, and C. Zander. Pair programming in education: a literature review. Computer Science Education, 21(2):135–173, 2011. [11] D. Horton and M. Craig. Drop, fail, pass, continue: Persistence in cs1 and beyond in traditional and inverted delivery. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education, SIGCSE ’15, pages 235–240, New York, NY, USA, 2015. ACM. [12] M. Johnson. Google rfp-cs capacity, Nov 2014. [13] L. Lacher and M. C. Lewis. The value of video quizzes in a computer science flipped classroom: An empirical study. In THE 2014 INTERNATIONAL CONFERENCE ON FRONTIERS IN EDUCATION: COMPUTER SCIENCE & COMPUTER ENGINEERING, pages 94– 100. CSREA Press, 2014. [14] L. L. Lacher and M. C. Lewis. The effectiveness of video quizzes in a flipped class. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education, pages 224–228. ACM, 2015. [15] U. of Kent. Computer programming aptitude test. [Online; accessed 22-August-2016]. [16] S. Rauchas, I. Sanders, and B. Kumwenda. The effect of prior programming experience in a scheme-based breadth-first curriculum at wits. In Proceedings of the 11th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education, ITICSE ’06, pages 326–326, New York, NY, USA, 2006. ACM. [17] N. Rountree, J. Rountree, and A. Robins. Predictors of success and failure in a cs1 course. SIGCSE Bull., 34(4):121–124, Dec. 2002. [18] R. H. Sloan and P. Troy. Cs 0.5: A better approach to introductory computer science for majors. SIGCSE Bull., 40(1):271–275, Mar. 2008.

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[19] A. Tafliovich, J. Campbell, and A. Petersen. A student perspective on prior experience in cs1. In Proceeding of the 44th ACM Technical Symposium on Computer Science Education, SIGCSE ’13, pages 239– 244, New York, NY, USA, 2013. ACM. [20] C. Wilcon, L. A. Sudol, C. Stephenson, and M. Stehlik. Running on ˘ S12 empty: The failure to teach kâA ¸ computer science in the digital age, 2010. [21] B. C. Wilson and S. Shrock. Contributing to success in an introductory computer science course: A study of twelve factors. SIGCSE Bull., 33(1):184–188, Feb. 2001. [22] S. Zweben and B. Bizot. 2015 taulbee survey, May 2016.

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Class Behavior on Quizzes that include a Prisoner’s Dilemma Bonus Question Peter Jamieson Electrical and Computer Engineering, Miami University, Oxford, OH, USA Abstract— In this work, we tested class behavior on a prisoner’s dilemma bonus question over ten quizzes spread out through the semester. The prisoner’s dilemma bonus question was a viral internet phenomena that appeared in 2015 used by Professor Dylan Selterman. We took this idea and asked, what would class behavior be like if this question appeared more than once during a class? We provided this bonus question over ten quizzes in a second year digital design course. Our original hypothesis was that students would talk among themselves, and eventually, they would agree to cooperate to get a small bonus. However, after the semester long experiment we observed that the class never got any bonus points. Not only was this the case, but we observed a number of behaviors as related to these quizzes. In this paper, we describe the nature of these quizzes, the associated prisoner dilemma bonus question, and the various observed behaviors of students. Also, we attempt to supply a number of hypotheses of why we think students behave as they did, but many of them have no evidence.

quiz. Additionally, we observed almost half of the students changed their behavior throughout the semester. In this work, we provide a number of theories on why students would make different bonus choices for this type of problem, but our data shows that none of these are true. Finally, we make no great claims about the pedagogical value of this experiment. However, in the class, we collect this data and present it to the students as a segue artifact to a discussion on how important it is to work together as an engineering class, and that fellow students in a class are not so much competitors, but will be future colleagues and are valuable friendships that students should make earlier than later. The remainder of this paper is organized as follows: section 2 examines previous research into extra credit, bonus questions, and the prisoner’s dilemma problem used in classes. We then describe, in section 3, the details of this experiment including the phrasing of the bonus question and the class it is used in. In section 4, we describe the results, and in section 5 we conclude the paper and describe some future work.

1. Introduction In this work, we use the prisoner’s dilemma problem 5 for a group, called the n-person prisoner’s dilemma 12 , as a bonus question on ten quizzes. The basic idea for the bonus is that students can choose between two options where one potentially results in a large bonus value (50%) and the other in a small bonus (10%). If a large percentage of the class take the small bonus choice as defined by a threshold then everyone gets the bonus they chose, but if too many students select the big bonus then nobody gets any bonus. This idea is based on a viral post highlighting Professor Dylan Selterman using such a question on one of his assignments (where Selterman credits Dr. Stephen Drigotas as the originator of this idea). For the sake of a fun experiment, we decided to take this idea further and investigate how this type of bonus question impacts bonus points over a number of quizzes. Our hypothesis is that students may start out by being greedy and nobody getting a bonus, but over the course the class would cooperate to get some bonus points. Over the 2016 semester in a class, we provided our prisoner dilemma bonus question on ten quizzes. The results of this experiment were that the class never received bonus for any

2. Background Student motivation’s in a class is a tricky aspect to deal with 7 , and our goal in this work is not to delve deeply into this domain. Extra credit, however, is used by some teachers as a reward and is desired by students. Norcross et. al. 9 were one of the earlier researchers who investigated how courses use extra credit and perceptions of both student and faculty to the idea of having extra credit. Not surprisingly, the above work by Norcross concludes students liked extra credit and teachers did not. Extra credit has been reported on in a number of publications in its use to improve exam performance 8 10 , participation 11, attendance 13, research participation 4, and in general 2 . To our knowledge, there is no significant research as related to bonus questions as extra credit of study. The prisoner’s dilemma problem has been used in classes as simulation games to teach various ideas on game theory and decisions 1 6 , but the problem, to our knowledge, has not been used as extra credit in courses in the literature.

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3. Bonus Question Experiment To implement this study, we had students in a 2016 Digital System Design course at the 200 level complete 10 quizzes (as normal) that included the n-person prisoner’s dilemma bonus problem (with IRB approval). The quizzes are lowstakes assessment that is used to test students on their understanding of the past week’s material. The quiz has the following properties and implementation details: • • • • •

•

Each quiz is worth 1 point towards the student’s total points of 100. Each quiz asks one question similar to the ones in the previous week’s problem set(s). Students have 10 minutes to complete the quiz. If students complete the previous weeks problem sets and submit them, they receive partial marks. If students do not complete the problem sets, then they receive no partial marks, but can get a perfect score for a correct solution. There are 10 quizzes over the semester.

The bonus question on each quiz has the following phrasing: “Circle either: A= +0.5 OR B= +0.1 - If greater than 10% of the class picks “A” then nobody gets any bonus points.” Students select either “A” or “B” from the above bonus question, and the data is collected for the entire class on each quiz. We remove students who both did not do the problem set and did not score perfect on the quiz. Then depending on the percentages of “A” and “B” choices, we add the bonus points to each of the quiz scores if the class as a whole does not surpass the 10% threshold of “A” choices. In some cases, a student might not circle “A” or “B”. For these cases, we assume that a student chooses “B”, and we use this result in the collected data. This course is offered in at the 200 level and is done by electrical and computer engineering undergraduates. Other students can take the course as a general technical elective depending on their major and the course typically is composed of 40% electrical, 40% computer, and 20% other. This course is part of the undergraduate curriculum at a predominantly undergraduate engineering degree college at Miami University.

4. Results and Analysis The results of this experiment are provided for the 2016 year in this section as well as some analysis on why these results were found. Table 1 shows the class bonus question results. Column 1, 2, 3, 4, and 5 show the quiz number, quiz focus question,

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and stats on population and their selections between “A” and “B”, respectively. The most important number is column 6 that contains the percentage of students who selected “A” in relation to the threshold - 10%. Therefore, we can see that for 2016 class, no bonus points were awarded. The same behavior was observed for more than just the 2016 class, but we did not have IRB approval to report that data. In table 1, Column 3 shows the total population participating for each quiz. This number decreases from the original 60 students in the class for a number of reasons. A small percentage of students drop the course, and some students are absent and miss quizzes, but these do not account for all the decrease. The larger factor is that the difficulty of the quiz increases as the course progresses and around 25% or more of the students never do the problem sets, and so, many of these students are not counted in the stats because they do not get perfect on their quizzes. Our original hypothesis, which we call the developing altruistic tendency theory, is that the students as a whole will eventually cooperate enough to get a bonus. However, as illustrated in table 1, this is not the case. So, the question is, do students make more rational choices in selecting their bonus for particular reasons that relate to the individual quizzes, or are students following particular behaviors independent of this assessment? We make two possible explanations for the results based on a rational behavior perspective 3. The first explanation is that it is possible that depending on how well a student thinks they are doing, they may be more likely to choose “A” if they think they will do poorly on the quiz - we call this the catch-up theory. The second explanation is that some students who know what they are doing and will likely perform well on a quiz will choose “A” so that they prevent poor performing students from getting any bonus and keep a bigger difference between them and weaker performing students - we call this the differentiate theory. In either case, we would expect a correlation between quiz performance and the students bonus selection. Our analysis shows that neither theory happens. Table 2 shows some additional data on students behavior. In particular, the three categories are those students who never changed their picks (rows two and three), those students who changed their picks (rows four and five), and those students who changed their picks permanently (rows six and seven). Additionally, for the 21 students who changed from “A” to “B” and “B” to “A” not permanently, the average number of “A” choices made by this group is 3.3 with a standard deviation of 2.4. In all, there is a large portion of the class changing their picks, but we have not come up with a reason why.

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Quiz Number 1 2 3 4 5 6 7 8 9 10

Table 1: Base Results for Bonus Question over 10 quizzes Topic Population #A selections #B selections Syllabus CMOS Transistors Karnaugh Maps Karnaugh Maps Verilog HDL Two’s Compliment Multiplexers Finite State Machines Finite State Machines Finite State Machines

60 53 56 45 30 45 45 32 37 42

14 13 14 8 7 10 7 8 10 11

46 40 42 37 23 35 38 24 27 31

% of As 23% 25% 25% 18% 23% 22% 16% 25% 27% 26%

Table 2: Additional data on student behavior Behavior Number of students Always picked “A” Always picked “B”

3 28

Switched “A” to “B” Switched “B” to “A”

24 26

Switched “A” to “B” permanently Switched “B” to “A” permanently

3 5

5. Conclusion

References [1] D. R. Cruickshank and R. Telfer, “Classroom games and simulations,” Theory into practice, vol. 19, no. 1, pp. 75–80, 1980.

In this work, we observed the behavior of a class on a bonus n-person prisoner’s dilemma question over ten quizzes. Once all the data was collected, we observed that the class never got any bonus points. In reality, the class was not even close to getting bonus points when the threshold was set at 10%. Within the class a number of behaviors were observed including those who maintained their choices throughout the semester and those who changed. The data, however, showed no trends on why particular choices were made. As stated in the introduction, this study is mainly for the interest of what would happen as opposed to providing any pedagogical insight. We, however, have used these results to bridge a discussion into why the class as a group could not work together, and then framed the discussion to the importance of the class cooperating with each other as opposed to competing. This is the first class for the students in which the majority of students are majoring in electrical and computer engineers, and it is important that this group begins to cooperate and learn each others names as they will be together for the next three years. In future, our plan is to increase the threshold slightly to 15% for the bonus questions and to intervene halfway through the semester to show results of the classroom’s current performance on the bonus.

[2] M. Elbeck and D. DeLong, “Exploring the contribution of extra credit in marketing education,” The E-Journal of Business Education & Scholarship of Teaching, vol. 9, no. 1, p. 107, 2015. [3] J. Elster, “Ulysses and the sirens: Studies in rationality and irrationality,” 1984. [4] J. R. Ferrari and S. McGowan, “Using exam bonus points as incentive for research participation,” Teaching of Psychology, vol. 29, no. 1, pp. 29–32, 2002. [5] M. M. Flood, “Some experimental games,” Management Science, vol. 5, no. 1, pp. 5–26, 1958. [6] C. A. Holt and M. Capra, “Classroom games: A prisoner’s dilemma,” The Journal of Economic Education, vol. 31, no. 3, pp. 229–236, 2000. [7] A. Kohn, Punished by rewards: The trouble with gold stars, incentive plans, A’s, praise, and other bribes. Houghton Mifflin Harcourt, 1999. [8] T. W. Maurer, “Daily online extra credit quizzes and exam performance,” Journal of Teaching in Marriage and Family, vol. 6, pp. 227– 238, 2006. [9] J. C. Norcross, L. J. Horrocks, and J. F. Stevenson, “Of barfights and gadflies: Attitudes and practices concerning extra credit in college courses,” Teaching of Psychology, vol. 16, no. 4, pp. 199–204, 1989. [10] L. M. Padilla-Walker, “The impact of daily extra credit quizzes on exam performance,” Teaching of Psychology, vol. 33, no. 4, pp. 236– 239, 2006. [11] R. B. Powers, K. A. Edwards, and W. F. Hoehle, “Bonus points in a self-paced course facilitates exam-taking.” The Psychological Record, 1973.

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[12] R. L. Weil, “The n-person prisoner’s dilemma: Some theory and a computer-oriented approach,” Behavioral Science, vol. 11, no. 3, pp. 227–234, 1966. [13] D. A. Wilder, W. A. Flood, and W. Stromsnes, “The use of random extra credit quizzes to increase student attendance,” Journal of Instructional Psychology, vol. 28, no. 2, pp. 117–117, 2001.

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Integrating Modern Software Tools into Online Database Course Hong Wang Computer Science and Engineering Technology, the University of Toledo, Toledo, Ohio, USA

Abstract - Database courses are essential in modern computer science program curriculum. Online database course teaching poses many challenges. This paper presents teaching approaches in using multiple free software tools that help improve student learning. In contrast to previous approach that develops a single database teaching system; this paper presents a method using multiple software tools for each particular topic. The software tools used are either from open source society or software companies. These interactive and easy to use tools not only improve student learning outcomes on database modeling, design and query, but also give them experience with software tools that are actually used in the industry environment. Keywords: Database, Database Course, online teaching, Computer Science Course

1

Introduction

Database Management System (DBMS) plays very important role in modern data driven technology. Businesses such as banking, retailing, entertainment, and online shopping stores use DBMS to increase efficiency and accuracy. Data warehouse supports business reporting and analysis. Big data technology enables people to utilized enormous amount of ever-growing data and is changing the way business is conducted. With Database and Big data tools, scientist are now able to more efficiently study human genome leading to better treatment and prevention of cancer [1]. As an important course in modern computer science curriculum, database course best benefits from the combination of theory and interactive laboratory activities. Many efforts have been made to facilitate interactivity. Over the years, Systems for academic use have been built to integrate many parts of the course activities such as UML (Unified Modeling Language) design, normalization and SQL practice [2][3]. This paper presents a different approach – Using free tools developed by software companies or open source societies. Software tools are picked based on what the course needs instead of on a single system that try to cover all database topics. The author sees the benefit as follow. (1) The instructor can pick what is better suited to the course in its functionality and ease of use. (2) Students can be exposed

to the tools that are used in the industry in the real world thus helping them in future working environment.

2

Course Background

This database course has been traditionally taught on campus. In order to accommodate the students holding full time or part time job, frequently travel or study remotely, for student retention purpose, the course gradually migrates to online environment. A publicly accessible MySQL database server is dedicated to this course. The server is 24/7 on and publicly accessible from anywhere in the world via a dedicated public ip address. The students are advised to maintain strong passwords to prevent any hacking event. When using PHP to access the MySQL database, students are able to fully implement a database supported website which gives student a real world scenario on MySQL database application. Database concepts are enforced during the database driven website development process. When taught on campus, the students can easily interact with the instructor for questions related to graphical diagrams, debugging, networking and system access issues. When taught remotely, many issues arise because of the communication barrier. In many cases, student’s performance suffers from trivial problems. This paper introduces modern software tools in an effort to address these issues.

3

Course Content and Software Tools

This database systems course focuses on teaching relational database theory followed by SQL and finally teaches students how to apply SQL in ecommerce website environment. This section follow the flow of the course topics, with each topic, the paper presents the software tool(s) used.

3.1

Entity Relation Diagram

The first topic in the database course is Entity Relational Diagram (E/R Diagram). Initially, the graphs were developed in PowerPoint, Visio or by Handwriting. Because of its graphical nature, it is difficult for students to practice without drawing the same diagram multiple times until it is correct. Many of the graphs developed look far

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from professional. Since graph revision and improvement tend to be difficult using the tools above, the quality of the works suffers. ERDplus [4] is an online E/R tool where user can drag and drop E/R symbols such as rectangle and diamonds on worksheet. Not only could the students redraw and rearrange the components of the diagram, they could more efficiently implement multiplicity among entity sets which has been challenging to some students before. Figure 1 is a simple example. ERDplus allows you to pick many-side and one-side by choosing cardinality of the entity sets and generate correct connections automatically. Final results are saved to user’s account and can be exported as a graph file submitted to the instructor for grading. The resulting E/R Diagrams drawn by students are more organized and correctness of multiplicity implementation greatly improved.

Figure 1: ERDplus

3.2

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Figure 2: MySQL workbench UML

3.3

MySQL

After modelling exercises, the students implement the database through SQL. SQL is widely used in many different database systems. Relational database such as MySQL, Oracle; distributed database systems such as Hadoop, Stark, NoSQL or NewSQL can be managed through SQL [6]. This course uses MySQL DBMS hosted on the course server. The course server has a public ip address where each student is given an account. Apache, PHP and MySQL are installed on this server and students are granted basic privileges on their own private databases on this shared server. To access MySQL, students need to first login to their server account. All the queries are issued in command line through ssh terminal. This poses multiple challenges to students when the main focus is SQL syntax and function. Students need to debug in command line environment which adds overhead and decreases productivity. Restoring the database tables to their original states would require extra work that may introduce more buggy problems.

UML

After covering the modeling concept through E/R Diagram, the course teaches students how to develop UML models which are closer to actual implementation of a database. MySQL workbench [5] is a comprehensive database management tool that contains a UML Diagram module. The UML module supply multiple intuitive tools for implementing components such as entity sets, attributes, domains, keys and referential integrity. Tools for multiplicity and constraints appear to be very useful. A many-to-many relationship bridge/join table could be automatically generated by dragging between the two related tables. These features allow more organized and high quality diagrams. The resulting diagram can be directly translated into database tables if connected to a DBMS server. This last feature is not currently used in this course for the purpose of letting students to practice table creation statements in command line on server side. Figure 2 is an example UML in MySQL Workbench.

Figure 3: w3chools

Before teaching students to use MySQL command line query in a terminal, the author found it more efficient to use a browser based environment to get students started with basic queries. W3schools [7] have a product inventory database where contents are already setup for users. Anonymous users may create new table or update the table content. It is very convenient for students to start from basic SQL queries without having to create their own in the beginning. Students may practice basic queries such as select, insert, update or more advanced queries such as join, aggregation and sub-query. The database can be restored to its original state if needed. However, the author found that

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Figure 4: MySQL Workbench

foreign key restraints weren’t enforced in this database. A screenshot of w3school interface is in Figure 3.

Course Server After getting basic experiences on basic SQL queries, the course server becomes useful in giving students practical environment to practice on constraints. Basic create table queries are supplied to the students. Students are then required to modify the queries to implement relational database constraints. Test cases are supplied to help students to test and improve their queries’ correctness. During this process, the students learn how to access server with credentials, log in to MySQL via command line, and import/export data. The database server would eventually be used to support a website project with PHP as CGI. Having student to apply database in a website environment motivates the students by showing them some real application of the databases. Accessing MySQL through PHP programming gives students a chance to understand and practice on using database through an application program which is what the students would do in their future jobs.

3.4

Video Lectures on echo360

All teaching materials are supplied on Blackboard teaching system. Although text based tutorial documents

such as PPT and PDF with images are supplied, the author found that video tutorials are more helpful and can play significant role in helping students to achieve learning outcomes. The author uses echo360 screen capture software to record lectures teaching the database tools. There are better screen capture and editing software in the market but echo360 is supplied to the university for free. It is limited in editing functions and may freeze while performing editing. However, the recording is generally smooth and quality of the videos is very good. Blackboard system is limited in storage space therefore not very suitable in hosting large amount of video files. Ech360.com is used to host the video files and can be connected to blackboard as a direct link in teaching modules. Echo360.com also helps monitoring student’s video watching activity. This function helps the instructor in making an analysis on whether a particular performance issue is a result of the video quality or the lack of video watching activity. Follow up action could be made to correct potential issues.

3.5

MySQL Workbench

Although it is important for students to use terminal command line to manage the databases, it is inconvenient to show complex constraints queries that relates multiple database tables in video tutorials. The query interface in MySQL Workbench appears to be more useful in this case.

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With proper configuration, MySQL Workbench is connected to the course server database under the instructor’s demo account. MySQL Workbench interface allows the user to run a query under the cursor and show resulting table content or error messages in separate frames. Making video lectures on table creation with constraints can be done smoothly. Various effects the constraints have on the SQL queries that follow can be demonstrated more fluently and efficiently. Query files may also be stored and retrieved later in the same lecture making switching topics more natural. Figure 4 shows MySQL Workbench with MySQL queries.

6

3.6

[2] J. Soler, I. Boada, F. Prados, J. Poch and R. Fabregat, &ldquo,A Web-Based E-Learning Tool for UML Class Diagrams,&rdquo, Proc. IEEE Education Eng. Conf., pp. 973-979, 2010.

AMPPS

Students are generally happy with the course server which resides in a virtual machine hosted by a fast Dell server. Although the class server is quite easy to access, students still need internet connection and ssh or ftp tools to transfer files. When programs and databases get larger, it can be more convenient for students to implement the solutions locally and export to the course server when finished. AMPPS is a stack of Apache, MySQL, PHP, Perl, Python and Softaculous auto-installer that can be installed on student’s PCs [8]. It is fully functional as a web server supporting MySQL database. It is recommended to online students to install this program on their personal computers for easy access. It also gives student a chance to practice database backup and restore when exporting local databases to class server.

4

RESULTS

available in the future. Thus the database course development, like most other computer science courses would require continuous effort in employing new techniques.

REFERENCES

[1] Y. Yang, X. Dong , B. Xie, N. Ding, J. Chen, Y. Li, et al. Databases and web tools for cancer genomics study. Genomics Proteomics Bioinformatics. 2015; 13: 46–50.

[3] M. Cvetanovic, Z. Radivojevic, V. Blagojevic, M. Bojovic. ADVICE—Educational System for Teaching Database Courses. IEEE Transactions on Education, 2011. [4] https://erdplus.com/ [5] https://www.mysql.com/products/workbench/ [6] Y. Silva, I. Almeida, M. Queiroz. SQL: From Traditional Databases to Big Data. Proceedings of the 47th ACM Technical Symposium on Computing Science Education (SIGCSE) Pages 413-418, 2016 [7] https://www.w3schools.com/sql/ [8] http://ampps.com/

This course was offered online on both Spring 2016 and Spring 2017 with 27 students and 22 students respectively. The author compared the outcomes of the same assignments. The E/R diagram assignment submission not only significantly improved in visual appearance, the rate of correct answers also increased. The percentage of students getting more than 80% correct answer increased from 70% to 86%. The grading was not based on the appearance of the graph but the correctness. In the UML Diagram assignment, the percentage of students getting more than 80% correct answer increased from 37% to 77%. In the MySQL query assignment, the percentage of students getting more than 80% correct answer increased from 52% to 68%.

5

DISCUSSION

By using modern software tools, this online database course aims to make database learning an interactive and efficient experience. The author has witnessed improved student performance after using these tools. Although developing interactive tools in a single package would be helpful to database course teaching, free database tools available on the internet provides competitive alternatives. It may be worth noting that there may be better tools than the ones used by the author that are currently available or will be

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INTEGRATION OF AGILE EDUCATION SOLUTIONS FOR TEACHING COMPLEX SUBJECT MATTER: AI SYSTEM DESIGN CASE STUDY John N. Carbone, PhD, James A. Crowder, PhD Southern Methodist University, Raytheon Company { [email protected]} {[email protected]}

ABSTRACT Historically, educating students in complex subject matter has had well known inherent challenges. Decomposing ambiguous complexities into digestible learning modules requires thoughtful topic analysis and simultaneous adherence to state, federal or national goal based learning requirements and varying agendas. Additionally, education is sometimes characterized by voices across the education continuum acknowledging significant challenges and failures. Specifically, education continues to be hampered by serial learning, complacency and regurgitation education characterized by “teaching to standardized tests” and reward systems which continue to emphasize successful memorization instead of qualitative learning. These challenges are also well-known to be intensified by economic, social and cultural issues within the home environment. Thousands of years of educational methodologies are also not changed easily. Hence, it is well known that preparing global learners for complexities and technologies far beyond the 21st century requires significant qualitative education reform. Globally, studies and analyses have continuously concluded that lack of skills are an epidemic. Teaching complex subject matter only exacerbates the problem and drives some reductionist efforts to teach more simplified concepts. Therefore, this paper attempts to address these concerns by providing some novel education options for varying education levels, leveraged from recent agile innovations. These innovations were crafted as part of imparting complex/abstract undergraduate/graduate level Artificial Intelligence engineering system design concepts combined with leveraging mature Computational Science agile development Scrum and Scrumban methodologies.

KEYWORDS Agile Teaching, Scrum, Scrumban, Complexity Theory, Artificial Intelligence Design, Artificial Intelligence, Trust, Ambiguity Reduction

1. INTRODUCTION It is well known that the act of learning and developing new insights continuously develops from the combination of experiences, actions and inquiries, whether scientific or personal. For years, literature evidence contends that the more complex the subject matter becomes the higher probability that teaching efforts are adapted to minimize content in order to maximize qualitative learning about less subject matter(Cochran-Smith, 2003)(Goldman Kearns, 1995)]. Ideally one could consider strategies enhancing the teaching of complex subject matter within the same challenging time constraints (Goldman, Kearns, 1995). The potential solutions this paper describes reflect upon “how” to harness the power of mature agile computational science methodologies in concert with complexity theory to

express “how” to improve education qualitatively for all students. The objective is to maximize their knowledge and context about specific and potentially complex subject matter. Hence, proper organization of the knowledge to be imparted and the presentation of the subject matter can be critical for achieving qualitative learning. Therefore, the next two sections discuss some research in organizing and presenting knowledge and related context for maximizing learning. 1.1 ORGANIZATION OF KNOWLEDGE AND CONTEXT As far back as 1957 Newell et al. [27] and Simon [28] together developed models of human mental processes and produced General Problem Solver (GPS) to perform “meansend analysis” to solve problems by successively reducing the difference between a present condition and the end goal. GPS organized knowledge into symbolic objects and related contextual information which were systematically stored and compared. Almost a decade later Sternberg [29] described a now well-known paradigm called the Sternberg Paradigm where, observations of participants were taken during experiments to determine how quickly the participants could compare and respond with answers based upon the size and level of understanding of their knowledge organized into numerical memory sets. Sternberg Paradigm is known for (1) organizing knowledge and modifying context while using a common process for describing the nature of human information processing and (2) human adaptation based upon changes in context. Similarly, Rowley and Hartley [30] described the development of knowledge as the organization and processing required to convey understanding, accumulated learning, and experience. 1.2 PRESENTATION OF KNOWLEDGE AND CONTEXT Trochim [34] described Concept Maps to present knowledge and context as structured conceptualization used by groups to collaborate thoughts and ideas. Described was the typical case in which concept maps are developed via six detailed steps: the “Preparation,” which included the selection of participants and development of the focus for conceptualizing the end goal, such as brainstorming sessions and developing metrics, (e.g. rating the focus), the “Generation” of specific statements which reflected the overarching conceptualization, the “Structuring” of statements which described how the statements are related to one another, the “Representation” of statements in the form of a presented visual concept map, which used multidimensional scaling [35] to place the statements in similar proximity to one another and cluster analysis [36] which determined how to organize the presentation into

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Int'l Conf. Frontiers in Education: CS and CE | FECS'17 | logical groups which made sense, the “Interpretation” of maps which was an exercise in consensus building once the representation had been created; and finally the “Utilization” of maps which was described as a process by which the groups within the process collectively determine how the maps might be used in planning or evaluation of related efforts. Stated was that concept mapping encouraged groups to stay on task which then resulted relatively quickly into an interpretable conceptual framework. It also expressed the framework entirely in the language of the participants and finally yielded a graphic or pictorial product. The product simultaneously presented all major ideas and their interrelationships and often improved group or organizational cohesiveness and morale. A survey of lessons learned from the use of novel AI course teaching methodologies is presented below. The descriptions and course constructs provided are derived from teaching graduate level Artificial Intelligence design at Southern Methodist University, Spring, 2017. 1.3 DISCOVERING THE NEED FOR CHANGE This section describes the path to discovery of pragmatic solutions and motivations involved in creating qualitative mechanisms for enhancing the AI system design class learning processes of ~22 Computer Science seniors, graduate students and distance learning students from different engineering and technology disciplines. The AI system design class was constructed by the university as a once a week 3-hour Tuesday evening elective. This meant that the complex curriculum would be professed as large bursts, in the evenings, following a long days at work for myself and at school for my students. Surveying the students early on revealed that the topic was at least compelling, however, after the first 3-hour marathon complex subject matter class lecture, it became intuitively obvious that concentration would become an issue and that my teaching style and curriculum would have to change quickly. Hence, the next sections describe my path of least resistance for addressing the complex subject matter and related challenges for potentially achieving maximum learning and comprehension. 1.4 DECOMPOSITION AND REDUCTION OF COMPLEXITY A Decomposition and Reduction process within the Recombinant Knowledge Assimilation (RNA) cognitive workflow cycle of natural human learning derived from (Sternberg, 1966) and formulated by (Carbone, 2010) is depicted in Figure 1. We focus upon decomposing the complex learning challenges of the AI design class by reducing them into workable solutions. Achieving optimal reduction of complexity also requires infusion of thought patterns regarding managing complexity (Suh, 2006, 2015). In addressing global education concerns (Carbone, 2016) formulated that computational thinking can support achieving a positive threshold of education completeness and level of modularity (Barrett, 2005), (Carbone, 2016) that enables students to effectively learn who are not generally predisposed

105 to learning in a computational manner. Therefore, the following sections describe details on managing complexity while advancing the growth of achieving the learning of new knowledge and context.

Figure 1, Recombinant Knowledge Assimilation (RNA) Cognitive Workflow

2. ESCALATING COMPLEXITY Our approach to learning has served us well except that most discipline learning has evolved over centuries as inwardly focused areas of specific and valuable but myopic expertise. Hence, as information content and science continues expanding at “warp” speeds and engineering continues to explode with new complex interdependencies, disciplines become wrought with increasing levels of uncertainty and unknowns. Therefore, to achieve continuous advancement, to manage the complexity, we are driven to broaden our scope of investigation beyond the boundaries of our expertise and knowledge of our existing discipline’s First Principles and concepts; the components comprising the

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Int'l Conf. Frontiers in Education: CS and CE | FECS'17 | ontology/language, concepts, algorithms etc. which make up the core of each discipline. In order to advance knowledge and manage related complexities today requires modern mechanisms, which simply do not exist in education and many engineering disciplines today. New mechanisms are required to manage complexities providing for current and future engineering education and solutions across many disciplines. A fundamental realization and understanding of what forms of complexity exist [15] is required. This must be accompanied by education research, new learning and building of methodologies that can transcend traditional disciplinary or organizational boundaries, to enable appropriate solutions to large complex problems by teams of people from diverse languages and backgrounds [3]. 2.1 Defining Complexity Suh, describes that significant confusion exists within the definition of what is complex. He explains that many people attempt to understand complexity in terms of physical entities instead of focusing upon what is trying to be achieved. He describes many types of complexity (e.g. Computational, Algorithmic, and Probabilistic). Suh’s computational thinking approach, describes 4 types of complexity: Time-Independent Real and Imaginary Complexity and Time-Dependent Combinatorial and Periodic Complexity. Suh mitigates overall system complexity by using a few specific set of actions: Reduce the Time-Independent Real Complexity, Eliminate Time-Independent Imaginary Complexity wherever possible, Transform Time-Dependent Combinatorial Complexity into Time-Dependent Periodic Complexity which decomposes the complexity into easier understandable and operable units. Suh describes this as determining the Functional Periodicity in whatever domain you are operating within (e.g. Biological, Chemical, Thermal, Circadian, Temporal, Geometric etc.). 2.1 Managing Complexity Suh, stipulates that complexity must be viewed within the functional domain. Hence, fundamental management of complexity becomes a process of defining what we want to achieve or understand within the functional domain of a discipline, project or need. These pieces of information become the Functional Requirements (FR). How we plan to achieve a goal becomes a set of Design Parameters (DP). Figure 1, shows this concept using a Probability Density Function (PDF) description. When the System Range is completely within the Design Range then Functional Requirements can easily be achieved and is therefore considered as not complex However, when the System Range extends outside of the Design Range boundaries then the design and the satisfaction of Functional Requirements become more difficult to achieve and is therefore considered more complex. Axiomatic Design processes are used to achieve optimized designs, domain decomposition into four important domains: Customer, Functional, Physical, and Process and using two important

axioms. The Independence Axiom, to maintain the independence of functional requirements and hence minimize their overlap where possible and the Information Axiom which states to minimize the information content throughout the iterative design creation wherever possible. The outcome of performing these actions during any system design phase is hopefully a completely Uncoupled Design where all functional requirements are independent with each being satisfied by independent design parameters and functions. In software terminology axiomatic design provides the logical decoupling of dependent interfaces and supports development of what is common and what can therefore be abstracted effectively. This type of brain exercise is an example of the unique analogous perspective to what Computational Thinking is and how it can be applied.

Figure 2, Design vs. System PDF

3. COMPUTATIONAL THINKING Computer Science evolution, created a now inherent revolutionary way of thinking and approaches to problem solving, primarily due to the vast application of computing for satisficing computational needs across every scientific domain. There are numerous existing publications primarily focused upon what Computational Thinking is and numerous educators/professors who have voiced speculation that Computational Thinking is either inherent or not, and hence cannot be learned. Some universities prescribe preexisting domain coursework as the mechanism for enhancing the development of Computational Thinking. Very few, if any, explore the steps to actually develop/train or rewire an individual’s thought processes to allow them to benefit from Computational Thinking’s value. True efficient and optimized research cannot be accomplished today without Computational Thinking. Additionally, engineers working in varying domains today generally know their domain well but have little or no understanding, theory, tools, concepts, methods or guidance to assist them in understanding other disciplines much less the ability to engineer across disciplines developing viable solutions within complex environments. Computational Thinking is key to future design and engineering across the Multi-, Trans-, disciplinary global evolution. Hence, to enhance the quality of learning, the next sections explore details of how humans think, remember and learn.

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3.1 Brain Structure Shaping Characteristics Years of psychological research show that different people learn differently and that varying differences in learning ability, among others, is tied to varying degrees of ones ability to sense. Some are more visible learners and others not. The blind intensely develop their other senses to make up for vision. Some learn with more depth when accompanied with high degrees of emotion and/or pain. Hence, research shows that the structure of knowledge shaped in our minds is distinctively shaped within each individual based upon an extensive set of individual characteristics. Yet all of these important physiological differences are completely unaccounted for within the architecture of every machine/system we have developed, continue to develop and interact with every day. Therefore, based upon vast historical modular learning citations and in order to attempt to enhance learning potential of complex topics for individual learning types we hypothesized that modularizing curriculum with adding reinforcement learning could support the enablement of each student to digest and process complex content more suitably mapped to their individuality. The next section discusses our new but similar approach. 3.2 Agile Computational Learning (ACL) Approach Upon examining the research regarding modular learning approaches, patterns began to emerge that paralleled computational design patterns used for agile development of large complex systems. Thus, we chose to extend agile processes, learned from the management of complexity, computational science agile scrum (Nonaka and Takeuchi, 2004) and abstraction principles and apply them to the classroom. The chosen scrumban approach comprised four distinct objectives focused upon maximizing learning, minimizing ambiguity and complexity. 1) Optimizing Class Time Usage 2) Improving Knowledge Quality 3) Improving Knowledge Retention 4) Improving Knowledge Application 3.3 ACL Approach: The Agile Scrumban AI Curriculum The chosen Scrumban approach was implemented because coursework is generally a continually flowing production similar to Kanban development as opposed to a Sprint based process where careful short sprints are devised and fully completed. Although curriculum for AI can be broken into and taught as multiple concepts, they are intertwined as in other domains and require additional thought to optimize course time the concepts which are required to be professed. Additionally, within the construct of a three hour class on a single night multiple topics need to be covered simultaneously. The Professor is both the Product Owner (Curriculum) and the Scrum Master not because it is somewhat intuitive but specifically, since the Product Owner is to provide the priorities of the class and empathy to put oneself in another’s (students) shoes in order to achieve the best results. The

107 Professor is also the Scrum Master who is responsible for the ultimate quality of the product delivery removing impediments and making sure the agile methodology is followed. Hence, as the Product Owner, effective Professorial communication is key. Therefore, we develop the classroom environment user stories and modular approach below. User Stories: Two simple user stories emerged. Student AI Learning (SL) with the actors being students and another, AI Teaching (PT), with the actor as the professor. The inputs into SL were AI curriculum, AI discussion, AI research and test questions while the inputs into PT were AI curriculum research, AI curriculum requirements, AI discussion and student test/homework/project metrics for determining complex learning potential and learning actuals. The outputs from SL were discussion, project, test and quiz answers, and from PT were AI curriculum, grades, discussion, and lecture. Backlog: During a standard Scrum based Sprint a backlog is generated and managed throughout the cycle in order to prioritize the items which need to be developed. In the case of the AI System design class the Backlog was a weekly assessment and update of Unstarted, Ongoing and Completed Syllabus topics expressed and presented weekly at the start of class. Additionally, as the students began creating their AI design projects backlogs were assessed per their remaining project tasks each week after the lecture. The complex AI System design subject matter taught during Spring 2017 was as follows: • AI Introduction • Information Continuum • AI Psychology, Cognition, Intuition • Synthesizing Brain Function • Artificial Memory Systems • Synthetic Reasoning • Uncertainty, Risk and Reliability • Complexity Theory • Requirements Theory & Application • System Design Theory and Application • Relationships: Probabilistics & Possibilistics • Fuzzy Logic and Search • Domain Analysis • Knowledge Presentation • Research and Research Paper Process • Knowledge Representation • AI Operating Systems • Intelligent Agent Theory • AI Security Theory • AI Ethics • AI System Development/Robotics • Sensing and Processing Systems • Natural Language Processing • Genetic Algorithms • AI Software Curriculum o Recursion

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o o

Functional Programming Intelligent Agent Development

3.3 ACL Approach: Measuring ACL Effectiveness Whereas, during Scrum and Scrumban development, daily scrums are trying to answer questions like What did I do yesterday?, What will I do today?, and what are any impediments. For a weekly three hour AI System Design class this was augmented as a weekly curriculum reinforcement exercise at the beginning of each class which reinforced topics from the previous class and was an optimized discussion based upon quizzes given to the students between each class to validate and distinguish what topics were adequately understood and which required additional reinforcement. Hence, the Scrum like questions we answered were: 1) What did we do last week? 2) What will we do today? 3) What items based upon quiz results did the students not understand and what impediments did that imply? Lastly, class team AI System Design projects were also used as input into the decision process supporting assessment of Knowledge Quality, Knowledge retention and application of the curriculum given.

4. SUMMARY AND NEXT STEPS In summary, the students created compelling projects when given the freedom to expand their horizons while simultaneously being guided by AI System design structure viable processes and curriculum. The students performed well on their weekly quizzes and some of their projects are under review for being considered for intellectual property protection by the university. Some of the projects are also being reviewed for conference and/or poster session presentations at the 19th Conference on Frontiers in Education and/or Artificial Intelligence. Therefore, the chosen scrumban approach comprising the four distinct objectives focused upon maximizing learning, minimizing ambiguity and complexity improved our efficient usage of class time enabling us to focus better on what the students learned each week, seemed to improve their knowledge quality through the assessment of their application of AI System Design concepts learned from the curriculum on their projects, improved their knowledge retention as seen by their continued successful quiz assessments. Potential next steps would be to implement agile Scrumban based education in various topic areas and continue to assess and measure performance as it compares to general education processes and paradigms.

REFERENCES 1.

3.3.1 ACL Approach: AI System Design Projects The following team based AI System Design projects assignments were created in order to simultaneously convey knowledge within the following areas and sub-areas: 1) Learning working as a team as in industry 2) AI System Design 3) Capturing detailed User Requirements and Mission Understanding 4) Preliminary and Detailed Design including introduction to DoDAF artifact creation 5) Complexity Research, Complexity Design, Complexity Analysis and application 6) Academic Research Quality and Processes 7) Proper research methodologies and citation references 8) Conference research paper presenting and formatting Simultaneously, while students were working their team projects, new AI curriculum was being presented each week, in priority order, in order to enable the continuous development of the AI projects with building blocks of AI curriculum. The teams self-organized into maximum groups of five and were required to choose a team leader within the first week and develop collaboration methodologies which enabled efficient work by the team and access for the professor.

Weintrop, D., Beheshti, E., Horn, M., Jona, K., Trouille, L., and Wilensky, U. 2016. Defining Computational Thinking for Mathematics and Science Classrooms. Journal of Sicence, Education, and Technology, Vol. 25 (1), pp. 127-47. 2. Voogt, J. 2015. Computational Thinking in Compulsory Education: Towards and Agenda for Research and Practice. Education and Information Technologies, Vol. 20 (4), pp. 715728. 3. Daily, S. 2014. Embodying Computational Thinking: Initial Design of an Emerging Technological Learning Tool. Technology, Knowledge and Learning, Vol. 20 (1), pp. 79-84. 4. Eisenberg, H. 2010. Bead Games, or, Getting Started in Computational Thinking without a Computer. International Journal of Computers for Mathematical Learning. Vol. 15 (2), pp. 161-166. 5. Bagley, S. and Rabin, J. 2010. Students' use of Computational Thinking in Linear Algebra. International Journal of Research in Undergraduate Mathematics Education, pp. 1-22. 6. Mittermeir, R., Syslo, M. 2008. Informatics Education Supporting Computational Thinking. Third International Conference on Informatics and Secondary Schools - Evolution and Perspectives, Proceedings of ISSEP, Torun, Poland. 7. Czerkawski, B., and Lymann, E. 2015. Exploring Issues about Computational Thinking in Higher Education. TechTrends, Vol. 59 (2), ppl 57-65. 8. Papert, S. 1980. Mindstorms: Children, Computers, and Powerful Ideas. Basic Books, Inc. 9. Booch, G., Rumbaugh, J., and Jacobson, I. 1998. The Unified Modeling Language Users Guide. Addison Wesley Publishing, Boston, MA, ISBN: 0-201-57168-4. 10. Ejigu, D., Scuturici, M., and Brunie, L. 2008. Hybrid Approach to Collaborative Context-Aware Service Platform for Pervasive Computing. Jounal of Computers, Vol. 3, pp. 40.

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Int'l Conf. Frontiers in Education: CS and CE | FECS'17 | 11. Gruber, T. 2008. Collective Knowledge Systems: Where the Social Web meets the Semantic Web. Web Semantics: Science, Services, and Agents on the World Wide Web, Vol. 6, pp. 4-13. 12. Van Ittersum, M., Ewert, F., Heckelei, T., Wery, J., Olsson, J., Andersen, E., Bezlepkina, I., Brouwer, F., Donatelli, M., and Flichman, G. 2008. Integrated Assessment of Agricultural Systems – A Component-Based Framework for the European Union (seamless). Agricultural Systems, Vol. 96, pp. 150-165. 13. Rowley, J. and Hartley, R. 2008. Organizing Knowledge: An Introduction to Managing Access to Information. Ashgate Publishing Company. 14. Sternberg, S. 1966. High-Speed Scanning in Human Memory. Science, Vol. 153, pp. 652-4. 15. Suh, N. 2006. Application of Axiomatic Design to Engineering Collaboration and Negotiation. Proceedings of the 4th International Conference on Axiomatic Design, Firenze, Italy. 16. Newell, A., Show, J., and Simon, H. 1957. Preliminary Description of General Problem Solving Programs-i (gps-i). WP7 Carnegie Institute of Technology, Pittsburgh, PA. 17. Simon, H. 1961. Modeling Human Mental Processes. In Papers presented at the May 9-11 Western Joint IRE-AIEEACM Computer Science Conference, Los Angeles, CA. 18. Barrett, H. Clark. "Enzymatic Computation and Cognitive Modularity." Mind & Language 20.3 (2005): 259-287 19. Carbone, J.N., Addressing Global Education Concerns Teaching Computational Thinking, Int’l Conference on Frontiers in Educations, FECS 2016

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Teaching Parallel Programming Using CUDA: A Case Study Timothy W. O'Neil and Yingcai Xiao Department of Computer Science, The University of Akron, Akron, Ohio, USA

Abstract - A recent prevailing trend in microprocessor architecture is the constant increase in chip-level parallelism. However, practical parallel processing instruction is made difficult by short-comings in existing platforms. The programming of graphics processing units (GPUs) is emerging as an effective alternative to the traditional paradigms, permitting students the chance to construct and assess parallel applications in a real-life setting. In this paper, we discuss our experiences teaching GPU programming to computer science graduate students in a classroom environment.

used in parallel courses. We also talk about our program at the University of Akron and outline what we have done in the past with our courses. We then describe the CUDA model and contrast it with these established archetypes. Next we discuss the overall organization of the CUDA course offered to University of Akron graduate students beginning with the Fall 2011 semester, followed by reflections on the course and the student's performance. Finally we summarize our thoughts and mention planned refinements for future course offerings.

2

Background

Keywords: parallel programming, computer science curriculum, general purpose graphics processing units, high performance computing

We now discuss the traditional means of teaching parallel processing and what we ourselves have historically done at the University of Akron.

1

2.1

Introduction

Increasingly complex applications in science, engineering, business and medicine fuel an ongoing search for computational speed and power. The physical limitations of traditional computer designs have guided this search in the direction of increasing parallelism at the chip level. Multithreading and multiple instruction issue techniques are prevalent in modern computer architectures. Multi-core CPUs are ubiquitous in commodity PCs. This paradigm shift has even led to calls to teach only parallel programming and let training in sequential coding fall by the way [1]. While this argument is something of an overstatement, it is clear that parallel processing is an important part of any modern computer science curriculum. Despite its importance, available platforms for parallel programming instruction prove problematic. Developing multithreaded code is challenging due to the unpredictable interactions among threads. In addition, we still lack an effective way to start and manipulate threads on different CPU cores in many cases. The other common choice, message passing programming, is beset by well-known problems of excessive overhead. An emerging option lies in the programming of general-purpose graphics processing units (GPGPUs or just GPUs), a variation on multithreading which promises increased programmer control over the threads. Our purpose herein is to discuss this approach and document our initial experiments in presenting this method to graduate students. In the next section we will give a brief history of parallel processing education, touching on the paradigms typically

The Challenge of Parallel Programming Instruction

Despite the argument in [1], separate courses in parallel programming are preferred. As the field has evolved, many degree programs have chosen to integrate object-oriented programming (OOP) concepts throughout the undergraduate curriculum and deemphasize traditional procedural coding. Since the purpose of OOP is documentation and maintainability of code and not performance, performance measurement and improvement topics tend to be neglected, and a stand-alone course discussing them becomes most appropriate. In a related point, high-performance code is intrinsically linked to its underlying hardware configuration and network structure in a way lacking in most programs. Indeed, the OOP paradigm has done everything it can to separate hardware and software. As specialized hardware like GPUs becomes more important to high performance computing, focusing on and reviewing concepts only touched on in some half-forgotten required architecture course is useful, not just for training the budding parallel programmer, but for the overall maturity of the young computing professional. Finally, as noted in [2], a separate course in parallel programming benefits a program regardless of which direction the curriculum evolves. If we don’t integrate parallel topics across undergraduate courses, the need for a class on this material is evident. If we do, such a course can be tailored to start from a more complex stage and add depth to the students’ background. In either case there is a clear need for stand-alone parallel courses in the modern computing program of study.

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2.2

Models of Parallel Programming Instruction

Most courses or units in parallel processing restrict themselves to one of two overall models, although interesting examples which mix the two approaches exist [2]. The first and oldest of these builds on the exposure to multithreading most students pick up as part of their studies in operating systems. (This approach also encompasses OpenMP, whose programs are constructed using threads in the background [3].) Synchronization becomes a critical topic in such classes, with discussions on critical sections, mutual exclusion, and deadlocks taking up a great deal of class time, as well as implementation-specific solutions such as condition variables. Most current courses, and indeed the IEEE's recent proposed model parallel curriculum [4], base their syllabi on messagepassing programming. The most popular current example of this paradigm is the Message Passing Interface (MPI) standard [5], which permits interaction and data sharing of computing nodes only through explicit communication. This radically different structure lends itself to algorithms unique to it, such as Batcher's sorting algorithms [6]. The most notable downside to the message-passing approach is the high cost of communication between nodes. Some highperformance clusters mitigate this cost through clever network topologies, but since most college courses utilize existing equipment, such overhead is unavoidable when learning to write parallel programs using this approach.

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early generation CUDA-capable GPU appears in Figure 1 below [7]. As can be seen, the processor consists of some number of symmetric multiprocessors (SMs), each with 8 cores in this example. (The latest Fermi generation cards have 14-15 SMs each with 32 cores.) Each SM contains a common memory shared by the cores, as well as registers, texture and cache.

Figure 1. CUDA-capable GPU architecture.

As we have alluded to, either methodology comes with model-specific topics for discussion in addition to generic themes related to parallel programming, such as program construction and evaluation. 2.3

Parallel Instruction at the University of Akron

The computer science department at the University of Akron maintains both bachelor's and master's degree programs. The annually taught operating systems course, which is crosslisted as both a required senior-level undergraduate class and a lower-level graduate elective, includes a lengthy unit on threads and concurrency. There are also two courses devoted exclusively to parallel processing, a joint undergraduate/graduate elective serving the same audience as the described o.s. class, and an upper-level master's degree elective. The lower-level course is taught every other year and primarily deals with OpenMP and MPI programming. However, the graduate class had not been taught for several years prior to its resurrection. It was the desire to develop this course that led us to consider a curriculum based on GPU programming with CUDA.

3

CUDA as a Platform

The Compute Unified Device Architecture (CUDA) is the programming environment developed by NVIDIA which permits programming of general-purpose graphics processing units (GPGPUs) directly in C. A typical architecture for an

Figure 2. CUDA thread organization. A CUDA programmer views a program's execution as consisting of a warp of threads running in parallel on a SM. Such threads are visualized as in Figure 2 [7]. A CUDA program creates a grid consisting of multiple blocks of threads. Each thread executes code in the kernel using data from the common device memory. Since each grid, block and thread is uniquely addressable within a CUDA program (as

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shown), each thread executes the same kernel on different data sets, leaving the user with the view of a massively parallel SIMD processor. As a simple example, consider the initial trapezoid rule program in Figure 3 which we use as the basis for a running example throughout the course. After reading the number of threads to use from the command line (third line of main()), the user enters the endpoints of the region (a and b) plus the number of trapezoids to use. The program then reserves memory on both the CPU and GPU (via cudaMalloc) before launching the kernel (line in boldface) using one block of thread_count threads. Each thread then runs its own copy of the kernel, first determining its own subset of the region before performing the trapezoid calculation and storing its result in an assigned entry of the result array, stored in global memory on the GPGPU. The CPU waits for the kernel to terminate, copies the results from the card to RAM, then reduces the intermediate results into one final estimate. __global__ void Trap(float a, float b, int n, float* thread_result) { float h, my_result, local_a, local_b; int i, local_n; int my_rank = threadIdx.x; int thread_count = blockDim.x; h = (b - a) / n; local_n = n / thread_count; local_a = a + my_rank * local_n * h; local_b = local_a + local_n * h; my_result = (f(local_a) + f(local_b)) / 2.0; for (i = 1; i @ 

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( 7KRPSVRQ $ /X[WRQ5HLOO\ - / :KDOOH\ 0 +X DQG 3 5REELQV ³%ORRP¶V WD[RQRP\ IRU &6 DVVHVVPHQW´LQProceedings of the tenth conference on Australasian computing education-Volume 78 SS± ³3URJUDPPLQJ*UDGV0HHWD6NLOOV*DSLQWKH5HDO :RUOG´ >2QOLQH@ $YDLODEOH KWWSZZZHZHHNFRPFD$SSOLFDWLRQ 'HYHORSPHQW3URJUDPPLQJ*UDGV0HHWD6NLOOV *DSLQWKH5HDO:RUOG>$FFHVVHG2FW@ $%HJHODQG%6LPRQ³6WUXJJOHVRI1HZ&ROOHJH *UDGXDWHVLQ7KHLU)LUVW6RIWZDUH'HYHORSPHQW-RE´ LQ Proceedings of the 39th SIGCSE Technical Symposium on Computer Science Education 1HZ 2QOLQH@ $YDLODEOH KWWSVZZZWXWRULDOVSRLQWFRPVGOFVGOFBRYHUYLHZ KWP>$FFHVVHG0D\@

ISBN: 1-60132-457-X, CSREA Press ©

Author Index Abdeyazdan, Marjan - 228 Abdulwahed, Mahmoud - 23 Abu Arqoub, Muhammad - 77 Aguar, Karen - 58 Al-Khazl, Fatimah - 116 Al-Otaibi, Norah - 116 Alsabbagh, Jamal - 122 Amer, Suhair - 207 Arabnia, Hamid R. - 58 Burge, Legand - 252 Carbone, John N. - 104 Cavalli, Matt - 73 Chavez R., Norma Elva - 172 Chen, Peter - 234 Chiou, Paul - 234 Colarik, Andrew M. - 165 Crowder, James A. - 104 Danduri, Chandra Kishore - 31 Dang, Quoc-Viet - 50 Debacker, Roslyn - 151 Dozono, Hiroshi - 34 El-Khalili, Nuha - 77 Enda, Tomonori - 34 Fareg, Kerules - 161 Farhat, Hassan - 215 Ferens, Ken - 151 Friesen, Marcia - 151 Fuchs, Andreas - 182 G, Young - 116 Garcia V., Erick Berssain - 172 Gashgari, Ashwag - 221 Grocholski, Marian - 151 Grocholski, Winston - 151 Gutierrez, Juan B. - 58 Guzide, Osman - 137 Hajiev, Fuad A. - 239 , 245 Haque, Ziaul - 15 Hicks, David - 3 Huang, Ching-yu - 161 , 179 Humos, Ali a. - 203 Hussain, Sajid - 15 Hussain, Shakir - 77 Issa, Gassan - 77 Jamieson, Peter - 96 Jean-Pierre, Ketly - 252 Jiang, Albert - 87 Kaabouch, Naima - 73 Keiller, Peter - 252

Kevorkian, Meline - 20 Kini, Ramesh G. - 239 , 245 Lacher, Lisa - 87 Lewis, Mark - 87 Liang, Xuejun - 203 Liao, Weidong - 68 , 137 Marasinghe, Kanishka - 73 McDowell, Rory F. - 165 Mejias, Marlon - 252 Morales, Nathaniel - 182 Morgan, Steven - 15 Moschler, Nathan - 151 Nakakuni, Masanori - 34 Niina, Gen - 34 O'Neil, Timothy - 110 , 142 Obimbo, Charlie - 128 Oncel, Nuri - 73 Otoum, Nesreen A. - 77 Pei, Tzusheng - 203 Pierce, David - 73 Potter, Walter D. - 58 Quick , Jacob - 179 Raigoza, Jaime - 8 Schwartz, Donald R. - 43 Shaer, Bassam - 182 Simco, Greg - 20 Sun, Jiasong - 62 Sun, Yu - 189 Taha, Thiab R. - 58 Tande, Brian - 73 Tavakol, Elham - 228 Thomas, Dwight - 252 Umarov, Timur F. - 239 , 245 Villa, Adam H. - 196 Wang, Hong - 100 Wang, Shengjin - 62 Washigton, Gloria - 252 Worley, Deborah - 73 Xiao, Yingcai - 110 Yang, Jeong - 3 Yang, Tsaihsuan - 189 Yang, Yi - 62 Yoshioka, Hiroki - 34 Young, Gilbert - 221 , 234 Zargham, Mehdi R. - 31 Zhang, Yu - 87 Zhao, Julia - 73 Zhong, Xian - 62