Towards Third Generation Learning and Teaching: Contours of the New Learning 9781839984600, 1839984600

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Towards Third Generation Learning and Teaching

Towards Third Generation Learning and Teaching Contours of The New Learning Edited by Murat A. Yülek and Johan G. Wissema

Anthem Press An imprint of Wimbledon Publishing Company www.anthempress.com This edition was first published in UK and USA 2023 by ANTHEM PRESS 75–76 Blackfriars Road, London SE1 8HA, UK or PO Box 9779, London SW19 7ZG, UK and 244 Madison Ave #116, New York, NY 10016, USA © 2023 Murat A. Yülek and Johan G. Wissema editorial matter and selection; individual chapters © individual contributors The moral right of the authors has been asserted. All rights reserved. Without limiting the rights under copyright reserved above, no part of this publication may be reproduced, stored or introduced into a retrieval system, or transmitted, in any form or by any means (electronic, mechanical, photocopying, recording or otherwise), without the prior written permission of both the copyright owner and the above publisher of this book. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. Library of Congress Control Number: 2022936466 A catalog record for this book has been requested. ISBN-13: 978-1-83998-4-600 (Hbk) ISBN-10: 1-839-98460-0 (Hbk) . This title is also available as an e-book

CONTENTS Preface

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Part I.

INTRODUCTION

Chapter 1.

Introduction—Three Generations of Learning Johan G. Wissema

Chapter 2.

Learning in Perspective—A Brief History of the Brain and Learning Sciences Ismail Güven

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Insights from Brain Research on Teaching and Learning David A. Sousa

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Chapter 3.

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Part II.

DRIVING FORCES ON THE DEMAND SIDE

Chapter 4.

Which Skills do Employers Want? A Case Study in a Transition Economy Aydin Fenerli

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Social and Emotional Learning—The Lessons from Neuroscience Hagar Goldberg

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Chapter 5.

Part III.

DRIVING FORCES ON THE SUPPLY SIDE

Chapter 6.

Habits of Mind: New Insights into Teaching and Learning Arthur Costa, Bena Kallick and Allison Zmuda

Chapter 7.

Beyond the Reach of Teaching—Differentiating the Role of Phenomenologically Oriented Vignettes in Learning and Teaching from Phenomenon-Based Learning Evi Agostini and Vasileios Symeonidis

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Chapter 8.

Chapter 9.

Contents

Where Immersion, Experimentation, Gaming and Learning Meet—Learning in Virtual Realities Carla Aerts Experiencing Digital Storytelling Khaldoun Dia-Eddine

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Chapter 10. Learning by Gaming in Management Evgeniya Kaz and Evgeniya Nekhoda

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Chapter 11. The Challenge of Artificial Intelligence Iaroslava Kharkova and Wayne Holmes

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Chapter 12. Agnogenesis Breeds Kakistocracy Bruno della Chiesa

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Part IV.

INDUSTRY–UNIVERSITY COLLABORATION

Chapter 13. Public Education and the University Murat A. Yülek

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Chapter 14. Learning in the Industrial University Murat A. Yülek and Ahmet Uludag

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Chapter 15. A New, Effective Model of Industry–University Cooperation Vazgen Shavarsh Melikyan Chapter 16. Case Study—Network Young Entrepreneurs NJO L.M. van der Mandele

Part V.

223 235

CONCLUSIONS

Chapter 17. The Future of Learning Johan G. Wissema

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Notes on Contributors

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Notes

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Index

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PREFACE According to Bill Gates, it is “a special time in education” (The Economist, 2016). The entire world of learning and education is shaking. New insights appear almost daily. Experimental new approaches and insights are emerging rapidly and maturing at breath-taking speed. The idea emerged to try and capture the most important developments in one volume. It soon became clear that the field of learning and education is not moving because of one or a few powerful trends. Rather, new insights and approaches come from such different worlds as educational science, information technology (IT), neurology and many others; the entire field is being put upside down. It was obvious that a book discussing the most important drivers and backgrounds could not be written by one person; it would require collaboration between authors of quite different disciplines. And so, we invited scholars of different disciplines, from different parts of the world, established academics as well as promising new talents and writers in the twilight zone of their careers, such as, alas, one editor, to contribute. We composed the book for students, academics, teachers, course developers, staff of overseeing (governmental) bodies and anyone interested in the fascinating subject of learning. We hope readers may gain valuable insight and inspiration from this volume. Although the chapters follow a model—outlined in Chapter 1—they can be studied individually or in arbitrary order. We would like to thank our authors for their enthusiastic cooperation; working with them was a delight. We also wish to thank Anthem Publishers to make this volume possible and offering invaluable assistance on the way. Johan G. Wissema and Murat A. Yülek

Reference The Economist. Must Try Harder. December 10th, 2016.

Part I INTRODUCTION

Chapter 1 INTRODUCTION—THREE GENERATIONS OF LEARNING Johan G. Wissema

The Learning Challenge “We stand on the brink of a technological revolution that will fundamentally alter the way we live, work and relate to one another. In its scale, the transformation will be unlike anything humankind has experienced before. We do not yet know just how it will unfold, but one thing is clear: the response to it must be integrated and comprehensive, involving all stakeholders of the global polity, from the public and private sectors to academia and civil society.” This quote, from Professor Klaus Schwab, founder and executive chair of World Economic Forum, continues to list some of the emerging technologies: “artificial intelligence, robotics, the Internet of Things, autonomous vehicles, 3-D printing, nanotechnology, biotechnology, materials science, energy storage and quantum computing” (Schwab, 2018). He could have added neuroscience, genetic engineering, blockchain technology and a host of other new technologies that have digitization as a common base. Further in his article he writes: “The demand for highly skilled workers has increased while the market for workers with less education and lower skills has decreased. The result is a job market with a strong demand at the high and low ends, but a hollowing out in the middle.” Hyperbole? Well, nobody doubts the impact of the technologies now being developed. Just about every day we are confronted with estimates of the alarming number of jobs that are going to be destroyed, making you wonder whether anyone will have a job at all in the not-too-distant future. Yet, the passage quoted above could have been written equally well in say 1880, when electrical power, (international) railways and motorized shipping, telegraph, telephone, photography, cars, motorized farming and, eventually, aviation were entering into the lives of our great-grandparents.

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They certainly “involved all stakeholders of the global polity, from the public and private sectors to academia and civil society” (op cit). Closer in time, many of us will remember the massive layoffs of administrative staff when computers became a commodity. Despite these enormous shifts, unemployment levels have remained low and not only because we work (slightly) less. How come? The answer is education. Each wave of technological change calls for workers with new skills, lured to the new professions by the early monumental salaries; think of the salaries of IT staff at the end of the previous century. This is then followed by an expansion of educational capacity, resulting in more skilled workers and a return to normal salaries. Is this going to happen again? Of course, it is, but there is one big “but.” Higher education has expanded significantly: in 1945, there were 500 universities across the globe, while in 2018, this has expanded to 20,000 and we should continue increasing (Willetts, 2018). The “but” is that we not only need more educational efforts but also an increase in the effectiveness of learning and teaching. People have a natural inclination to learn, but does this inclination vibrate with the educational system? “I am always eager to learn, although I do not always like to be taught”—attributed to Winston Churchill.

Three Generations of Universities— the Medieval University An intermezzo. In the earlier work, we have distinguished three generations of universities (Wissema, 2009), starting with the Medieval Universities or First Generation Universities (1GU). The University of Bologna, established in 1158, is generally regarded as the oldest uninterrupted university but the origins of universities lie in ancient times at different locations on the globe. The objective of the 1GUs was not the pursuit of new knowledge but the protection of the wisdom of the past and the teaching of obedience to the doctrines of the church. “Universities were not armed for the conquest of science, born as they were in an epoch when the only question was to preserve the deposit of traditional beliefs. They commented and discussed, they invented nothing. They wore themselves out in subtleties, in fine distinctions, in quibbling. But they laid the ground for the great harvests of the sixteenth and following centuries” (Rüegg, 2004). Le Goff points out: “It should be noted that nothing could become an object of conscious reflection in the Middle Ages except by way of religion. It would almost be possible to define the medieval mentality by its inability to express itself apart from religious references. This remains as late as the sixteenth century. Craft guilds would make the tools of their trade attributes of a saint,

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integrated in a hagiographic legend.”… “During the Middle Ages technical progress was perceived as a miracle, as a domination of nature which could have no origin than divine grace” (Le Goff, 2016).

The Second Generation University While the 1GU were spreading out over Europe and later Latin America, the Renaissance induced the development of the modern scientific method. The scientific method gained considerable momentum under the influence of the Enlightenment, a philosophical movement emerging in the mideighteenth century, that advocated, among other things, reason as the basis of analysis and decision making. Conclusions about the workings of nature became based on objective evidence, rather than the teachings of ancient scholars. Think of Leonardo da Vinci who dissected bodies to see how they were really composed, instead of relying on the anatomical atlases of Galen and other Roman physicians. The scientific method was developed outside the universities, most of which strongly opposed it. As an exception, in the fifteenth century, the University of Salamanca made great strides in navigation, which eventually enabled Columbus to prepare for his trip to discover America. It taught the forbidden Copernican system while Galileo was in prison. The scientific method led to the creation of new universities, the Second Generation Universities (2GU), the first being the University of Berlin, now called Humboldt University after its founder Wilhelm von Humboldt, established in 1810 in the latter years of the Napoleonic era. The foremost objective of these universities is the exploits of science, with teaching in its slipstream. Teaching in Berlin was given in German, rather than Latin, the lingua franca of the 1GU. Universities gradually adopted the new model during the nineteenth century while their numbers and geographical spread expanded greatly.

The Third Generation University In the mid-twentieth century, the 2GU were challenged by a number of developments that are the driving force of a gradual change to a new paradigm, the Third Generation University (3GU). One development was the vast increase of student numbers as of the 1960s which forced universities to adopt new teaching methods in order not to become “learning factories.” The emergence of multidisciplinary and later transdisciplinary research was another development. Faculties lost their significance in advanced research and were substituted by interdisciplinary research institutes that focus on

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subjects and not disciplines. Globalization meant that the three markets—for students, academics and contract research projects—became international, forcing universities to adopt English as the new lingua franca. The nineteenthcentury universities were not involved in the application of their research findings; all great new technologies—the steam engine, telegraph, telephone and photography, you name it, were developed outside the universities. In contrast, 3GU are active in commercializing their know-how and stimulating students to start their own technology-based firms, resulting in technological “valleys” all over the world. Commercialization adds a third objective to universities in addition to teaching and research. One cannot say that the 3GU model substitutes the 2GU one; only advanced universities pursue that road while others stay in the 2GU mode or even the 1GU mode with teaching as their only objective.

Learning and Teaching—Classical Learning Back to learning. Parallel to the development of universities, we can distinguish three stages in learning and teaching which we will call Classical Learning, Industrialized Learning and New Learning. This concept forms the thread of this book, and we will discuss the stages in brief. Classical or First-Generation Learning is characterized by the direct interaction between teacher and student. The picture is familiar: the teacher in front of the classroom “the sage on the stage”—transfers his knowledge to students who sit on benches and make notes. It was practised in antiquity— Socrates challenging his students at the agora—and the Middle Ages, and it still comprises a considerable part of our learning system, especially in postgraduate courses. The method is perhaps much older; think of the Stone Age when fathers were teaching their sons how to position themselves in the wind lest the bear smell them. Some of today’s lectures are interactive, others are monologues. “Direct interaction” between students and teacher does not mean something like private lessons or small group teaching; thousands of students came to hear Abélard (1079–1142), and other famous scholars during the Middle Ages. Learning was and is a container concept that incorporates such different things as: ●

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Instruction, that is communicating indisputable facts, like telling a child the difference between a chair and a table or an electrician that the brown wire is plus and the blue one minus. Talent development, as in art schools but likewise for practically every profession.

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The art to solve problems or mysteries, as in scientific research and analytical activities. Learning social skills and empathy.

Industrial Learning After Napoleonic times, learning became increasingly industrialized. Industrial Learning, overlaying Classical Learning, constitutes the bulk of today’s learning. Born in Prussia around 1794—hence after the onset of the Industrial Revolution—Industrial Learning has all the elements of this revolution such as: ●

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Specialization: the number of professions and scientific disciplines expanded, and their content narrowed. It has exploded since WW II, scientists tended to know ever more about ever less. The “Renaissance man,” who could muster several disciplines, all but disappeared. Secondary school learning is also specialized, according to the intellectual level of students, their age, the trade taught and so on. The higher up the teaching ladder, the more specialized the courses. Standardization: courses, diplomas, students and teachers all became standardized by the government. Just as a label saying “1 kg of sugar” tells the consumer that the bag contains sugar, not salt and a bit more than 1,000 gms of it, the school or university diploma tells employers what they buy. School and university courses became standardized and so did diplomas. In contrast to the Middle Ages, it did not matter much in the Industrial Era at which university you would study or to which school you went. Students became standardized as well: the labels first-, second- and third-year students and diplomas in subjects A, B or C tell us what is in the pack. Teachers likewise became standardized with their various standardized professional qualifications. Synchronization: education is connected seamlessly to employment, with diploma being the “linking pin.” Diplomas are the communication tool between graduates and employers. Different types of schools and universities are interlinked, students can, to a certain extent, switch seamlessly from school to university and school to work, perhaps with the route of vocational education in between. Concentration: schools and universities became ever larger until they have become true learning factories. In universities, personal contact is usually confined to the later years; exams—often multiple choice that housewives or computers can mark—have become likewise industrialized. Maximization: output of schools and institutes of higher learning was maximized as the more education, the more prosperity; in addition, there

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should be equal chances for everyone. For this reason, education enjoys high esteem by politicians and employers. This esteem, however, makes the system very resistant to change. Maximization applies to quantity as well as the quality of education with institutes competing for the “best” results, “best” meaning for instance the percentage of pupils that pass the school exam. Educational institutes increasingly became the subject of all kinds of (quality) measurement, often a hobby of bureaucrats, except for the useful Programme for International Student Assessment (PISA test), a competition between 15-year olds in science, math and teaching skills, commissioned by the Organisation for Economic Co-operation and Development (OECD). Universities boast of their “production” of scientific papers, just like a car manufacturer boasts about the number of cars produced. Centralization: in just about every country, the Ministry of Education sits at the top of the National Education System. The Ministry designs the educational system, and implements, finances and controls it. In addition, it approves, or designs, courses, certifies teachers, sets examination targets and often conducts the exams itself. So, Big Education Brother is omnipresent. No part of society is further from free-market principles than education, except for the military, the police or the fire brigade.

The emerging Industrial Learning has been challenged since the Enlightenment in the mid-eighteenth century; think of the attempts at modernization by Rousseau and contemporaries. These experiments died out until the late nineteenth century. At the end of that century, a multitude of experimental new school types has emerged, some now well-established, like the Montessori schools. Schools and universities experiment with new types of teaching and learning. Distance learning has become a well-established dish on the menu. Home teaching has grown although it is statistically insignificant. A lthough t he c hanges a re o nly m arginal i n s cale, w e m ay say that the current educational and learning system is Industrial Plus. See Chapter 2 for a more educated history of learning. Brain research has led to a rapidly increasing corpus of insights into how the brain works. As subsequent chapters will show, these insights are pervasive throughout this book. To help readers, Chapter 3 presents an overview of the workings of the brain and how the new insights influence teaching and learning. One cannot study learning and teaching unless one has a good understanding of the workings of the brain and the new insights that bubble in every day. This chapter, Chapter 2 on the history of learning and teaching and Chapter 3 on the insights from brain research, together constitute Part I of this book—Introduction.

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Challenges to Industrial Learning— Driving Forces on the “Demand Side” Recent decades have seen strong forces of change that impact the way we look at teaching and learning. We distinguish driving forces on the “demand side” (coming from those who benefit from learning, that is students and employers) respectively the “supply side” (the “technology” of learning and teaching). Part II of this book (Chapters 4 and 5) is devoted to the changes on the “demand side.” Let us start with students and pupils. New generations of pupils and students demand different approaches to education. While there is much hot air in the discussion about the millennials, it is undeniable that young cohorts of school leavers and graduates have different career objectives than previous generations. The “youth of today” take fewer drugs and alcohol than those before them, they enter sex at later ages and stay at home longer. They are less violent than they used to be (The Economist, 2018a). Millennials seek challenges more than money, they want to work for a coach, not a boss, they want to substitute the annual job evaluation for ongoing discussion in which attention is given to strengths, not weaknesses. Most of all, they focus on life, rather than the job; status does not interest them and many don’t own a car, let alone a bling-bling one (Currid-Halkett, 2017). Millennials are twice as likely to invest in so-called responsible companies and are twice as likely to exit investments because of objectionable corporate activity as the average investor (World Economic Forum 2014). They don’t fit well in the current labor market; no surprise then that 55 percent of them feel unengaged at work. Millennials don’t aspire to ideologies; they want to optimize their immediate living conditions. Employers complain that millennials are selfish and inhibit no loyalty: on their first day of employment, they start searching the net for the next and better job (The Washington Post, 2016). The new generations want to design their own courses and even have a say (or the highest say) in the management of educational institutions. Already for many years, human resource (HR) practitioners advocate focus on human development, yet, in many enterprises, employees are considered cost factors that can be disposed of as easily as garbage in a household. The changes on the employers’ side are enhanced by what Klaus Schwab of the World Economic Forum calls the Fourth Industrial Revolution. The idea is that the “First Industrial Revolution used steam water and power to mechanize production, the Second used electric power, the Third uses electronics and information technology to automate production. The Fourth Industrial Revolution is building on the Third. It is the digital revolution that has been occurring since the middle of the last century. It is characterized

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by a fusion of technologies that is blurring the lines between the physical, digital and biological spheres” (quoted from the World Economic Forum website). We would add that the requirements of the digital age not only make it mandatory for specialists to be engaged in teamwork, but that most of them should have mastered more than one specialization. We have called this “transdisciplinary research and development”—more specialists sit around the table than people. It followed mono-disciplinary, multidisciplinary and interdisciplinary research and development (R&D). Harrison has argued that significant changes be made to single-discipline programs (Harrison, 2017). All this means that students must prepare bespoke courses with one eye on the market and one on what they find interesting and motivating. Chapter 4 reports on some remarkable changes in the attitudes of employers in a transition economy; however, the “lessons” from this chapter apply elsewhere. From all sides of society, there is a strong demand for better social skills (soft skills). One reason is that the vast majority of projects are carried out by multidisciplinary or transdisciplinary teams. Team members then have to learn how to communicate across the “languages” of their own discipline. Another reason might be that our society is increasingly open, and actions must be communicated to stakeholders and often also to the public. Industrial learning institutes have focused on cognitive learning, leaving the learning of social skills to the private domain. There is opposition against the notion that learning is just a matter of cost/benefit analysis. Nancy Rothwell, president and vice-chancellor of the University of Manchester, posits that university courses are not only a purely financial investment. “Studying at universities should be a unique and transformational experience, challenge your principles, take you out of your comfort zone” (Rothwell, 2016). Soft skills will become an integral part of just about any course, whether at kindergarten or university. Chapter 5 offers a compelling argument why teaching social skills—as an independent discipline as well as a coaching tool for students— need to be part of any curriculum. If courses become individualized, what will be the role of diplomas? There are two kinds of exams: “output exams,” which give evidence that a certain course has been passed successfully, and “input exams,” for instance, for admitting students to higher education; job applications can also be considered for entrance exams. Successful output exams are rewarded by diplomas. Output diplomas have two functions. For the student, they are highly motivational, vide the elaborate ceremonies followed by frivolous parties when a course is finally crowned with a diploma. For society, diplomas serve as an intermediary between graduate and the employer—the “1 kg of sugar” label. Is this still satisfactory? In the words of former Financial

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Times columnist and current teacher, Lucy Kellaway (2021): “The point of education as currently configured is as a signaling device to universities and employers—students with the right exam scores are allowed on to the next phase of life. The children need the qualifications not to understand the world, but to make their way in it.” “It seems daft to make students sit exams in all subjects. There seems no good reason why they shouldn’t sit exams only in core subjects, leaving the rest of the time free to be inspired, educated and stretched in something more intrinsically interesting than parroting exam technique. This way, half of education would send signals to employers and universities and determine which students should get the place or the job. The other half would help the survive there once they’d landed it.”

Driving Forces on the “Supply Side” The multitude of new insights and practices in learning and teaching creates a turbulent field, especially as they come from entirely different disciplines, from psychology via pedagogical research to neurology and much else. In Part III (Chapters 6–12), we will explore some of the driving forces on the “supply side” without claiming to deal with all that is presented. We begin with psychology. As we grow up, we create, consciously or subconsciously, habits in our thinking and acting that often turn out to be wholly or partly dysfunctional. The first step toward improvement is of course awareness; given that, a process of de-learning and learning better habits can set in. The educational institution can create an atmosphere in which this change of habits is stimulated. For a comprehensive approach, based on original research and much experimenting, turn to Chapter 6. In “phenomenon-based learning” (The Economist, 2016), as in Stanford’s d.school, Maastricht’s Medical Faculty, Finland’s Design Factory and practices at Olin University, students work on a project, either alone or in a team, in which they have to solve a problem or make a design. The teacher becomes a coach rather than an instructor. Phenomenon-based learning builds on problem-based learning, which has been around for several decades (Schwartz et al., 2001). The case study approach in business schools can be regarded as problem-based learning. The common base is that being faced with a challenge turns out to be much more motivating than, say, learning Chapters 14–19. Chapter 7 explores the theory and practice of phenomenonbased learning. The Internet already has a vast impact, partly because of specialized companies that put courses on the market—Udacity, Coursera, EdX and so on. One such company, Kahn Academy, has six million subscribers who solve three million math problems each day. In Russia, universities likeomsk T

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State University have specialized units that design online courses, for their own students or anyone who wants to use them. The popularity of Massive Open Online Courses (MOOCs), a rather primitive way of learning, has taken great flight. An entirely new way of learning takes up the popularity of (video) games. But it is older than that; children have always learned from games, long before they became computerized or part of the Internet. Virtual reality adds a layer on top of gaming and makes it more intense. Augmented reality offers enormous possibilities for learning. Learning with virtual reality and augmented reality together is called immersive learning. Chapter 8 gives a contemporary state of the art. One of the delights of old age is telling stories to one’s grandchildren. Together with the imitation of adults, it is perhaps the oldest way of learning. Storytelling is enhanced when children start to read books themselves—and that is usually where it ends, cognitive learning taking over. Or is it? Recently, a new form of learning has been introduced—digital storytelling—that can be applied at all levels of education and that turns out to be very powerful. Chapter 9 provides an introduction with experiences and examples. Another way of learning from ancient times is by playing games. Cubs do it and for children, it is a great delight. Not surprisingly, modern pedagogues, notably Maria Montessori, have adapted it for use in education. Nowadays, gaming is a new and powerful tool that can be used in all levels of education. See Chapter 10 for backgrounds and stunning results of gaming and an example of gamification in management training courses. This brings us to artificial intelligence (AI). Despite much publicity, AI-assisted learning is still in its infancy, but it holds vast promises. “AI and machine learning will improve the process of scientific discovery,” says Demis Hassabis, a co-founder of Deep Mind, the company known for its program to defeat the world’s best Go-players. Robots at the University of Aberystwyth can carry out an entire scientific process: formulating hypotheses, designing and running experiments, as well as analyzing data and deciding on further experimentation (Dodgson and Gann, 2017). Carnegie-Mellon uses virtual assistants that can tutor and guide personal learning; this gives the same results as human tutoring in fewer hours of study. Georgia Tech found no differences between robot and human tutoring. Chapter 11 gives the state of the art and what we can reasonably expect. As stated above, a vast amount of research into the workings of the brain is ongoing, all over the world, aided by ever sophisticated scanning techniques and a host of new tools such as Neuropixels, a probe, 1-cm long and 70-microns across, that is inserted into the brain and that can read signals from groups of brain cells ( James et al., 2017). Many efforts go into the design

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of brain–computer interfaces, allowing persons with artificial limbs to move them by the power of thought, just like we do naturally (The Economist, 2018b). Together, these efforts have led to remarkable results and new insights into the workings of the brain. These billions worth of research is bound to throw more light on the workings of the “last unknown organ” of the human body. Neuroscience has experienced rapid growth over the last three decades and tended to form links with other disciplines. Education is one of such disciplines that incorporating neuroscience can enhance our understanding of mental and physiological processes involved in learning. But we must be careful about our expectations. Chapter 12, in Wittgenstein-style, explores the options.

Industry–University Collaboration Once upon a time, universities and industries lived in strictly segregated worlds. At the end of the previous century, a collaboration between universities and industry became accepted, then mandatory and today it is an integral aspect of the 3GU. Collaboration in the field of research has received the most attention from scholars as this has become a multi-billion-dollar worldwide market; many industries have cut their efforts in basic research or abandoned it altogether in favor of joint projects with universities or farming projects out. Increasingly, the collaboration between industries and universities is not limited to research but includes education as well. In Part IV (Chapters 13–16) we will give four examples of such cooperation. In Chapter 13, the concept of the Industrial University is developed; this type of university is characterized by strong inputs from industry on the curriculum and extended periods of learning in enterprises rather than at the university itself. One could say that a group of industries has set up its own, private university. The aim is to achieve a perfect connection between study and employment and the first results show that this works. Chapter 14 expands on the IndU concept and focuses on the way of learning. Another approach is the creation of courses as a joint venture between an industry and a university. Such an approach is especially indicated in fields of rapidly developing technology, such as—see Chapter 15—in IT. The early cooperation formed the basis of a lively IT industry in Armenia and calls for imitation. Management courses have successfully employed business cases as learning material. Real-life cases are to be analyzed by teams of students who usually must answer the question: “What would you do if you were the CEO?” The idea of phenomenon-based learning is even stronger when students learn entrepreneurship by writing the business plan of their own company rather

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than following lectures on this subject. The motto could be: “Don’t teach them dancing, let them dance.” For a successful approach see Chapter 16.

Conclusions—The Future of Learning In the final chapter (see Chapter 17) of this book, a sketch of the future of learning is attempted by way of summary of the contributions.

References Currid-Halkett, E. The Sum of Small Things: A Theory of the Aspirational Class. Princeton, NJ: Princeton University Press, 2017. Dodgson, M, and Gann, D. Universities Have Sown the Seeds of Their Own Disruption, Internet article, 9 August 2017. The Economist. Helsinking, May 14th, 2016. The Economist. Teenager’s Behavior – The Youth of Today, January 13th, 2018a. The Economist. The Next Frontier and a Special Report, January 6th, 2018b. Le Goff, J. Time, Work and Culture in the Middle Ages. Chicago: The University of Chicago Press, 1980. Harrison, J. The emerging links between learning capability, experiential learning and research: Political implications for higher education. Paper presented at the Australian Association for Research in Education Conference, Melbourne, 2017. James, J. et al. Fully integrated silicon probes for high-density recording of neural activity. Nature, 551 (9 November 2017): 232–236. Kellaway, L. Education’s big test. Financial Times, 6 March 2021. See also her book: Re-educated: How I Changed My Job, My Home, My Husband and My Hair. Ebury Press, 2021. Rothwell, N. There is more to university than money. Financial Times, 16 March, 2016. Rüegg, W. Chapter 1 – Themes. In A History of the University in Europe, Volume III, edited by W. Rüegg. Cambridge: Cambridge University Press, 2004. Schwab, K. The fourth industrial revolution: What it means, how to respond. World Economic Forum, January 14, 2018. Schwarz, P., Mennin, P., and Webb, G. (eds.). Problem-Based Learning – Case Studies, Experience and Practice. London: Kogan Page Ltd, 2001. The Washington Post. What Companies Still Don’t Understand About Millennials. 13 May 2016. Willetts, D. A University Education. Oxford, UK, Oxford University Press, 2018. Wissema, J.G. Towards the Third Generation University – Managing the university in transition. Cheltenham, UK, Edward Elgar Publishers, 2009. World Economic Forum. World Wealth Report, 2014.

Chapter 2 LEARNING IN PERSPECTIVE— A BRIEF HISTORY OF THE BRAIN AND LEARNING SCIENCES Ismail Güven

Introduction The Mind, Brain Sciences and The Learning Sciences (in short Brain Learning Sciences (BLS), which is the later usage of concept respectively for separate studies), both developed very recently as interdisciplinary and multidisciplinary fields made up of education, psychology, neuroeducation, biology, science of the mind, brain and education (Sawyer, 2005; OECD, 2007; Lee 2018). The BLS aims at fostering understanding, instruction and learning. The BLS attempts to support effective learning and teaching environments by focusing on instructional, technological and social policy innovations and the course design at every level (Ferrari & McBride, 2011; Lee, 2018). The BLS highlights working on effective teaching and training, the improvement of learning environments, the development of new software and effective school administration. The BLS provides information about cognition, effective course design and socio-cultural settings that are essential for effective teaching and learning in formal and informal schooling. The BLS promotes the research and theory in cognition, learning sciences, socio-cultural context and course design (AECT, 2018). It aims to inform about the processes of teaching and learning, as well as scientific models of the structures for maintaining essential knowledge, understanding and skills when necessary. The BLS also addresses the socio-cultural basis of learning and scrutinizes the socio-cultural dynamics of learning and teaching. The field is based on learning and teaching not only with a particular focus on classrooms and schools but also on activities outside schools and informal learning settings such as museums, corporations and homes. The BLS has pointed out the need for new approaches for course design as well as building new environments

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such as infusing multimedia, AI and computer networks into learning and teaching, creating innovative curriculum and classroom activities for effective teaching. The BLS attempts to bring together interdisciplinary areas such as educational psychology, curriculum and instruction, with educational technology and research. Within this framework, it is aimed to clarify the differences between the BLS and educational sciences to create a history of the BLS. The field engages in analyzing learning, technology and instructional design. Therefore, there is a common misunderstanding regarding these fields t hat c omposes t he B LS ( Hoadley & Van H aneghan, 2 011). The history of the BLS dates back about 30 years. The First International Conference of the Learning Sciences (ICLS), which was held by the Artificial Intelligence in Education (AIED) in 1991, was the event where the term “Learning Sciences” was introduced for the first time (Sawyer, 2005). Before that, there was no available literature on this subject. Later, the Institute for the Learning Sciences and the first-degree program in Learning Sciences at the Northwestern University was established and the first issue of the Journal of the Learning Sciences appeared afterward (Lee, 2018). Although the field of Learning Sciences has emerged recently, when we look back on the history of education, it will be seen that this field existed in different forms before even if it is referred to other concepts and names. Henceforth, the historical traces of learning sciences and brain sciences will be scrutinized separately.

Historical Traces of Learning Sciences Thinkers and scientists such as Socrates, Plato and Aristotle along with several other philosophers were highly interested in better teaching methods and making knowledge perpetual for the next centuries. Many of them spent much of their lives fostering knowledge and learning and attempted to find the functioning of the mind since ancient times, and explained it based on sensations and experiences (Barnes, 1984; Guthrie & Guthrie, 1986; Güven, 2019). Because they are the forerunners of learning and inspired many contemporary thinkers and educators, it could be emphasized that their influence on education remains alive today. The interest in learning and better teaching has continued for centuries and was accelerated by the nineteenth and twentieth century’s thinkers and educators ranging from John Dewey, Jean Piaget, Benjamin Bloom, and David Kolb to Lev Vygotsky. The new generation of educators who established the fields of psychology and philosophy shaped learning and instruction by generating theories and research, and applied them to real-life situations (Hergenhahm & Henley, 2013). Therefore, it is not easy to delineate the historical framework of BLS since its development as a discipline of educational sciences is quite recent

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and has often been used synonymously with learning theories, pedagogy and educational sciences. While analyzing the history of learning sciences as a separate field, it is not possible to isolate it from educational sciences or learning psychology. By learning theories, principles are examined to define how well a student can acquire, recall, process and restructure new information. At first, learning sciences implies the concepts of learning and teaching and just like in educational sciences when we deal with learning. This sounds quite plausible because learning and teaching are inseparable. However, it is an undeniable fact that there are still some dark sides to the conceptualization of both education and learning sciences. Educational sciences is still not regarded as an established discipline and there is no clear consensus associated with it both institutionally and politically (Hofstetter & Schneuwly, 2004; Furlong, 2013). Therefore, defining learning sciences exactly becomes more complicated due to myriad issues in this field. Hence, developing a framework for the concept of learning sciences will make it much easier to deal with its history. On the other hand, it is known that different researchers perceive and conceptualize learning sciences slightly differently by mentioning its different aspects. BLS are impartially new and developing study topics; however, their roots may date back to the time of Aristotle and Plato (Güven, 2019), and it could be reasonable to analyze this historical framework so as to make it more comprehensible. Many theorists and thinkers have focused on how students learn from different perspectives since Plato. Both philosophers thought of teaching, learning and education differently. They also paid special attention to how learning and the relationship between teachers and learners could be empowered and promoted effectively. Plato does not base it on the psychology of learning normally but makes initial suggestions for our perceptions of the nature of learning. Plato, in his Republic elicits education, mentioned learning and educational activities as a vehicle that could foster soul, dialog and could maintain continuing education (Reble, 1999; Gutek, 2005). Aristotle tries to explain learning with categories unconsciously. Aristotle defines categories under the topic substance, quantity, quality, relation, place, time, position, doing, having and being affected. Aristotle grouped them into 10 categories, and they are called the basic predicates or basic concepts (Thomasson, 2008). They are also related to the mind and intelligence broadly and these efforts were addressed to the brain. These are important steps for BLS as they help to understand human cognition, learning, and social perception. Later, other thinkers or educators such as Comenius, Locke, Rousseau and Pestalozzi as well as others provided information about the learning (Gutek, 2005). Education and learning emerged as a field of activity under the field of philosophy or other scientific disciplines. These efforts, in a sense, formed

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the basis of learning sciences. Undoubtedly, it is not possible to talk about the concept of brain sciences at this stage since the information about the brain was scarce. In later years, educators such as Pestalozzi, Herbart and Frobel emerged and tried to conceptualize learning and the nature of learning. At the end of the nineteenth century, the field of educational psychology was developed and established by the pioneering efforts of William James, G. Stanley Hall and John Dewey (Hergenhahm & Henley, 2013). Hence, this century must be regarded as the golden era of educational sciences in general and learning psychology. Educators and psychologists such as James, Hall and Dewey who were the distinguished figures in education generated the new scientific m ethods o f o bservation a nd e xperimentation t o fi gure ou t educational problems (Martin, 2002). Some other important figures such as Binet, Thorndike and Piaget aimed to explain the learning processes deeply and created some theories and instruments to maintain the learning. Furthermore, theorists working in the field of educational psychology tried to explain how learning takes place with experimental methods. They presented learning more as a reflection of inner impulses. During the fi rst ha lf of the twentieth century, the behaviorist approach that dominated the fields of psychology and education adopted the definition of learning as a relatively permanent change in one’s behaviors that could result from experience on which most of the psychologists agreed. Behaviorism became a dominant theory in learning and teaching in this period, and research was shaped accordingly. Later, social learning theories emerged and followed cognitive learning that advocated the idea that learning is not only an individual activity but also a social activity that is fostered by observation and imitation. Attention, motor skills, motivation and memory are the essential factors for effective observational learning. These new ideas also fostered the studies of BLS (Hergenhahm & Henley, 2013).

Development of Brain and Learning Sciences The BLS has been generating valuable new knowledge in order to contribute to educational policies and practices throughout the world on a global scale recently. It generates new studies and creates new paths for many fields such as education, humanities and medicine. To understand the historical development of the BLS, it is necessary to closely examine the development of learning and teaching theories. Developed as a discipline, educational sciences dominated how learning and instruction took place in the nineteenth century. Meanwhile, educational sciences were regarded as a discipline of social sciences and institutionalized at the university level. New research areas such as teacher education, educational psychology, curriculum, instruction and

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educational philosophy emerged under the umbrella of educational sciences. Conducting research in educational sciences focused on more practical issues, whereas educational psychology elicited the mind and intelligence in terms of learning and instruction (Furlong, 2013; Hofstetter, 2012). Upon making the short history clear, it is useful to discuss the historical development of the BLS. BLS did not appear in the blink of an eye, it is obvious that there are some factors that accelerated their development. As clearly stated above, some great thinkers have engaged in the origins of the universe, the nature of the human mind for centuries; however, none of them provided any convincing explanations of how the mind makes it possible to learn due to a lack of powerful research or observatory tools for the brain. Some of them speculated the learning and processes of the mind. To exemplify it, it is significant to know that experimentalism and positivism influenced the scientific methods and thinkers began to use new methods to explain the learning and functions of the mind and brain. They created laboratories, they tried to find out how thinking and learning develop and take place. Although they provided very promising results, the studies did not explain the thought and learning and development of competence due to scarce data. It is also highly crucial to emphasize that the age of industrialization empowered research about learning and the mind. Research on behaviorism helped us understand the nature of behaviors. A great deal of special attention was paid to the importance of behavioral objectives for learning, mediating the learning environments and personal traits, as well as the enhancement of the “behavior modification techniques.” The theory still focuses on the observable processes and behaviors in learning. The inadequacy of behaviorism in understanding the internal and mental processes in learning has led to the emergence of cognitive learning. Cognitivism attempts to shed light on understanding the mind, and it gained credence in the 1950s, meaning that it has its own history. It originated from the thoughts and ideas of Plato, Kant and Descartes who elicited cognitive behavior and paid attention to cognitive knowledge and their ideas were spurred. Later thinkers and psychologists like Wilhelm Wundt, William James, John Dewey, John Watson and others all attempted to figure out how the mind and thought process together. Bandura paid special attention to the social dimension of cognitive theory whereas Jean Piaget elaborated his theories based on internal structures, knowledge and the environment (Meyering, 2012; Bandura, 2005). Cognitivism emerged as a study field in 1956, the second day of the Second Symposium on Information Theory was held at the Massachusetts Institute of Technology (MIT) (Bruner, 1983a). Cognitivism intended to complete the deficiency of this view, which connects the functioning of cognitive processes with the

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formation of behavior and environmental factors. Research on cognitive psychology contributed to understanding how internal and external factors influence an individual’s mental procedures for better learning. Cognitivism also tried to explain the nature of performance competency and the principles of knowledge organization that activate the skills of people to elucidate problems in diverse areas (Meyering, 2012). Developmental psychologists focused their research on the link between learning and development and explained that maturity played an important role in reasoning and learning. Developmental psychology also contributed to explaining how learning takes place by addressing physical, cognitive, social and emotional development (Hergenhahm, 2009). All these theories provided an important amount of data to understand learning in various settings. Both in educational sciences and psychology, significant studies were conducted about learning and intelligence; however, educators also pointed out the social and cultural dimensions that influence learning as well. The other social sciences areas such as sociology, anthropology and social psychology pointed out that socio-cultural norms and habits also influence learning and transfer in an effective way. These developments shaped the social learning and educational sociology disciplines. Neuroscience that aimed to show the impact of brain learning emerged and scholars carried out some studies to explain the learning processes in the brain through laboratory research. It was the beginning of the brain sciences in learning. As will be noticed, different branches of science have applied different methods and applications to explain learning, and most of them have provided reliable information within the scientific criteria (Ansari et al., 2017). All these developments have made it necessary for fields such as educational sciences, cognitive psychology and neuroscience, which deal with learning from different perspectives, to cooperate. Educators and psychologists are beginning to focus on both the nature of learning and the environments and practices in which learning takes place and try to include teachers in research. In addition, emerging technologies have provided important opportunities to explore the nature of learning and enriched learning in the last two decades. These developments caused pervasiveness in the study field o f l earning (Bransford et al., 2000). Apart from these, the knowledge, skills, competencies and attitudes expected from individuals have changed considerably in the last century, and these expectations have led to the progress of the BLS. For example, at the beginning of the century, people were expected to have mastered literacy and numeric skills but in the last two decades, they have been expected to have skills such as digital literacy and critical thinking, global citizenship and cooperation. Schools educate people to think and read analytically, to express themselves effectively and cope with complex

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problems in different subjects such as math, science and social sciences. Civic participation and democratic and global citizenship became so universal that the scope of learning shifted from national to global issues. The fastgrowing developments in Information and Communication Technologies (ICT) provided new opportunities to observe the brain and its activities, and this accelerated brain science to engage with learning. The inadequacy of addressing learning and educational problems from a one-sided perspective became more evident. Thus, researchers attempted to create a new study area and different approaches related to learning, teaching and curriculum design. These developments necessitated interdisciplinary cooperation with learning, and this led to the emergence of a new discipline called learning sciences first, then it was transformed into BLS later. The BLS spent more time on instructional practices, neuroscience, curriculum design and educational environments such as real and virtual (Darling-Hammond et al., 2020). The dissemination of research areas associated with teaching and learning has led to the emergence of a new and interdisciplinary field that addresses both the nature of teaching and learning, and other factors that affect them considerably. The conception of the BLS discipline emerged based on cognitive science that produces reports in a good order from a disorganized perspective and the research focus on cognitive science, educational psychology and AI was frustrating. Thus, a new perspective on learning was necessary and urgent. Some researchers felt a need to bring their efforts together and create a new cognitive science field about promoting learning. Roger Schank established the Institute for The Learning Sciences at the Northwestern University and invited some researchers to study and ponder on learning and fostering learning with computers. Xerox company followed this inclination and created a department focusing on researching reasoning and learning in real-life situations. Vanderbilt Center for Learning and Technology attempted to create technology-based curriculum materials for fostering lasting learning using the data of cognitive sciences (Kolodner, 2004; Jordan & Henderson, 1995). The fact that the studies carried out until that time were produced in an “abstract” and “isolated environment” played a role in the formation of learning sciences. It became more important to conduct studies based on data obtained from real-life situations in which learning took place. Learning sciences pointed out that one could comprehend cognitive and social processes that underlie effective l earning a nd h elp people learn meaningfully and efficiently in th e li ght of th ese fa cts. Thus, BL S br ought diverse disciplines such as educational psychology, curriculum and instruction, sociology, anthropology, information sciences, design studies and other fields into existence. Some researchers in these fi elds pointed out the necessity for a new scientific approach that goes beyond one discipline and

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based on interdisciplinary collaboration. This situation also brought about the publication of the studies in new periodical publications. The Journal of the Learning Sciences was founded to publish articles about promoting learning in real-life situations both in schools and other educational environments. Then learning sciences emerged in 1991 officially and the first international conference was organized, and later the journal was called Journal of the Learning Sciences. After creating the learning sciences, periodical publications, introduced in 1991, it was aimed to introduce the field to wider audiences by organizing its first international conference. The first conference was called the “International Conference of Learning Sciences” which was held at the Northwestern University in 1991. The international AIED community included learning science as a topic to the series conference of the community. The AIED conference not only focused on infusing AI into education but also focused on the possibility of promoting learning situations in a variety of fields such as digital learning, outdoor learning, museums and so on. This conference was the most important turning point in learning sciences as many researchers came together to share and exchange their ideas for the first time. Conferences and journals of learning sciences foster learning sciences to be recognized as a scientific field that could create an identity. The Journal of Learning Sciences also served as a medium to disseminate the studies and ideas about learning sciences (Kolodner, 2004; Jordan & Henderson, 1995; Sawyer, 2005; Tokuhama-Espinosa, 2019). Most of the articles published in this journal addressed methodology and different aspects of learning, technology integration and curriculum design. The journal also contributed to the establishment and internationalization of the International Society of Learning Sciences. All these developments motivated well-known universities, and the academic staff working in these universities, such as Indiana, Vanderbilt, Wisconsin, Stanford and so on, established learning sciences departments and started to establish graduate programs. Some universities and departments established graduate programs and major research centers and created networks of scholars to provide a platform for scholars and trainees who would later carry on developing the newly created field of Learning Sciences. These scholars and graduates dominated the scholarly agenda and developed a theoretical framework that might explain the nature of learning. They also conceptualized a new framework that was different from what was dominant in the previous academic discourse (Lee, 2018). The learning sciences developed its academic identity and gained a place by establishing graduate programs that could provide a framework for the general concepts and study topics of different international graduate learning sciences programs. (Sommerhoff et al., 2018). They revealed that the programs addressed the concepts such as learning in school and out-of-school contexts, designing

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learning environment, cognition and technology-integrated learning, and so on. The departments providing graduate education in BLS differ from each other in terms of courses and study topics. This pervasiveness may stem from different perspectives and multidisciplinary approaches of the learning sciences department. The learning sciences emerged in the USA and then, they pervaded to other, non-English speaking countries. Even though most of the studies carried out at graduate departments of the BLS were empirical and technology supported, there were still inconsistencies among the departments in terms of core subjects and peripheral topics. This discrepancy occurred due to the academic staff with different sc ientific bac kgrounds, whi ch is normal for the BLS. Study topics ranged from education to math, science and engineering, and they focused on both individual and interaction among the learners and promoting learning in every setting. Conducting design-based research was common as well (Sommerhoff et al., 2008). The Computer Supported Collaborative Learning (CSCL) community implemented in Europe and other countries paid special attention to the studies related to cognition, new technologies and social dimension of learning. They included technology-mediated group cognition and online designed space in the BLS and made technology an essential part of the BLS. They focused on the interaction and motivation of learners in collaborative learning activities on digital information generating media with asynchronous participation and online discourse. These challenges enriched the learning sciences field ( Jordan & Henderson, 1995; Kolodner, 2004; Lee, 2018).

Historical Pathways of the Mind, Brain Sciences Hippocrates (460 to 380 BC) regarded the brain as a source of understanding and human consciousness, as well as knowledge, identifying it as a learning center. Stoic philosophers claimed that experience is a product of the whole human body. Considering the subject of the brain sciences, it is useful to emphasize the subject of mental discipline theories. Mental-discipline theories of teaching focused on training of intelligence and the mind in which they implied that effective teaching disciplines the mind and empowers intellect. Imitation and memorizing were very important. The theory of learning involving mental discipline could be regarded as the earliest version of the brain sciences as its history goes back to the ancient Greek times (Madonna, 2006). Aristotle first paid attention to the faculties in the mind; thus, this theory is called “faculty psychology”. According to Aristotle, there were several faculties, each of which was considered to perform independently (Kristjánsson, 2016). The medieval thinkers also used this theory for

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explaining the nature of learning and urged that the “faculties in the mind” were responsible for performing different duties and tasks. Therefore, one had to create some exercises that empower these faculties and mental capabilities. Exercising certain faculties helps train individuals to learn math, science or language. To do so, school subjects could be taught easily as long as faculties are trained for their own fundamental value. The faculties of the mind are linked to Aristotle’s categories and these categories put an impact on mental processing. Faculty psychology or theory focused on the mental process during learning. Faculty psychology claims that intellectual functions are performed in specific areas of the brain (Hergenhahn, 2009, pp. 189–92). Therefore, faculty theory could be regarded as a pioneer of the brain sciences in education. The speculations of faculty theory, which is the oldest theory about the nature of intelligence or the mind, caused some controversies. This theory urges that the mind consists of different faculties such as reasoning, memory, discrimination, imagination and so on, which are in the brain. By creating vigorous exercises, these faculties could be fostered. The faculty theory particularly emphasized on the activity of the mind. Even though this theory is disappearing nowadays, it is plausible to accept that it shaped the field of the brain sciences and put forward to study the functions of the brain in learning (Hergenhahm, 2009). Some Renaissance scientists or artists such as Leonardo da Vinci (1508) and Andreas Vesalius (1543) not only analyzed the human body physically but the brain as well. These attempts led to the way of detailed analyses and the emergence of some studies by scientific societies like the Royal Society of London. Some members of this society visualized and named specific areas of the brain. John Locke tried to explain the nature of learning; however, there were blurred ideas about the role of the brain in learning (Aldrich, 1994). Charles Bonnet in his book called Essay on Psycholog y (1755) mentioned that there was a relation between the mind, brain, and education for the first time. The nineteenth century witnessed more studies about the specific brain area functions. While Broca (1862) maintained that the language learning area is placed in left frontal of the brain, Wernicke (1874) mentioned the parietal (Wernicke) lobes. Later, Brodmann described the main visual motor and auditory pathways in the brain. Another important contribution was made by Ramon Cajal (1911) who explained that the brain’s neurons constitute the basic functional and physical components (Ferrari & McBride, 2011). These are important steps in brain science. Hall and Baldwin, evolutionary psychologists, studied the mind and brain and offered a framework for learning ability by referring to evolutionary selection (Broughton, 1981). Baldwin claimed that learning also helps

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the survival of species and changes the conditions of selection, that is the genes that are transferred to the next generations. This idea implied the physical or biological dimension of learning. Piaget borrowed these ideas from Baldwin and based his theory on biology and adapted them to education as phenotypic adaptation (Piaget, 1980). Piaget’s four stages of cognitive development that could be used for specific educational settings are based on a biological explanation that is the development of cognition, and the mind was closely related to biological development (Smith, 2000). Lev Vygotsky contributed to the Mind and Brain Education (MBE) science by drawing attention to the importance of socio-cultural elements in learning and developed a cultural—historical psychology. Cultural intervention and internalization emerged as a result of the personal “inner speech”. Vygotsky and his colleagues also engaged in the MBE studies within a developmental context (Vygotsky, 1978). Kurt Fischer combined the approaches of Piaget and Vygotsky and created the “Skills Theory” which was a promising work for the MBE (Fischer & Bidell, 2006; Stein & Fischer, 2011). Hebb developed a theory called as “Hebbian Synapse Rule” and pointed out the roles of neurons in learning and attempted to explain the processing of classical conditioning and associative learning. He developed an associative learning concept first suggested by Aristotle, as mentioned above, which was one of the most important steps for demonstrating the MBE itself as a discipline in the book called Brain Research and Learning (Claycomb, 1978). All these efforts inspired other researchers like Chall Mirsky who tried to integrate neuroscience with education, and wrote an influential b ook c alled E ducation a nd t he Brain. Another step was Howard Gardner’s Frames of Mind (1983) and Leslie Hart’s Human Brain, Human Learning that made the brain and mind a popular area to study and addressed the connection between learning and the human brain (Tokuhama-Espinosa, 2011). All these developments fostered the idea that there was a connection between the mind, learning and brain (Ferrari & McBride, 2011). Ten books and journals appeared to focus on the studies incorporating functional neuroscience with teaching. Gerhard Preiss also suggested a new discipline that brought the brain sciences and education together to promote effective learning (Sabitzer, 2011). The MBE science became the main study era that contains neuropsychology and neurodidactics. The proponents of this field d id n ot recognize the MBE as a subfield of psychology or education but as a separate discipline (Tokuhama-Espinosa, 2011). All these challenges showed that the MBE emerged from different approaches and sources in different parts of the world. The researchers engaged in educational psychology and neuroscience in the field concentrated on the neural learning mechanism and abilities to learn how to improve the brain through international and interdisciplinary

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studies. They developed neuroscientific based on interpretation of curricula, like retrieval, automaticity, vocabulary, engagement with language and orthography (RAVE-O) and Fast ForWord that were adapted to the classroom (Tokuhama-Espinosa, 2011). The first “Learning Brain EXPO” in San Diego was organized in 1999 and directed the attention of teachers and scientists to “brain-based” activities. Later, the “Learning & the Brain Conference” at Harvard University and MIT followed this EXPO in 1997 (Ferrari & McBride, 2011). The OECD reinforced these attempts by organizing brain-based conferences for educators. The aim of these conferences was to bring academics from different disciplines like neuroscience, psychology and education together to share new developments and information along with research agendas for the evolving discipline (Casper & Gavrus, 2017). The International Mind, Brain and Education Society (IMBES) was established and held conferences. As expected by these interventions, the number of conferences rose and there was an increasing concern about incorporating neuroscientific research and knowledge with education and teacher education. These challenges gave way to publish books about “the MBE.” A new journal entitled Mind, Brain, and Education appeared and the IMBES was founded (Tokuhama-Espinosa, 2010; Ferrari & McBride, 2011). The MBE program was constituted by the School of Education, Harvard University in 2004 (Fischer et al., 2007). The program was organized as a separate degree program in 2002 and provided master’s and doctoral degrees to academics with diverse backgrounds. Later, Dartmouth College and Cambridge University also generated the MBE programs, and China, Japan, South Korea, Argentina and Germany launched programs or institutes. The Brain, Neurosciences and Education Special Interest Group (SIG) of the American Educational Research Association (AERA) was established (Tokuhama-Espinosa, 2011; Ferrari & McBride, 2011). The fundamental aim of the MBE science was to study and focus on teaching practices by means of emphasizing greater professionalization and to make teachers more conscious to recognize their roles to cope with school problems as if they were neuroscientists and psychologists (Solso, 1999). The learning of the BLS studies has its own methods and techniques. For instance, neuroscientists adopted some different components of learning by studying the brain whereas the learning sciences handled it by studying the classroom contexts. The idea of collaboration among the diverse disciplines and researchers helped to generate and come up with new ideas, methodologies and ways of learning. The components of BLE as a diverse discipline is summarized in Figure 2.1. Moreover, many scholars began to disseminate their ideas and studies through the journal and in a comprehensive handbook

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Educational Sciences

Psychology Educational Psychology

Brain and Neuroscienc

Brain and Learning Sciences Educatonal Technology and Instructional Design

Humanities

Linguistic

Figure 2.1 Core subjects of the brain and learning sciences.

called The Cambridge Handbook of the Learning Sciences in 2006 (Sawyer, 2005; Casper & Gavrus, 2017). It seems that BLS change into transdisciplinary study field with unique strategies (Tokuhama-Espinosa, 2019).

Discussion It is difficult to make history of a field of science with a relatively recent history. The history of the BLS as a field of science is quite new, as stated earlier. As discussed above, the BLS did not fall from the sky. Like other fields of science, it emerged because of certain needs or attempts to find out solutions to problems. The interest in learning and education has continued in every era. Just as in the history of educational sciences, it is possible to take the history

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of the BLS back to ancient times. One can trace the ancient footprints of the BLS in the ideas of the Ancient Greek philosophers and the medieval age thinkers. The enlightenment and experiment age contributed to the development of inquiring the learning and teaching, in addition to the industrial revolution (Madonna, 2006). Technological developments, the inadequacy of existing disciplines in explaining the dynamics of education and learning, and the necessity of interdisciplinary cooperation made an important contribution to the development of the field of BLS. The BLS, as a separate discipline, is quite novel compared with education and psychology; thus, its conceptualization and history are still emerging. There is a certainty that the BLS is made up of different fields devoted to promoting learning and creating novel methodologies and solutions to problems of teaching and learning (Solso, 1999). The inspirational sources of the BLS are psychology, cognitive science, neuroscience, educational sciences, anthropology, instructional design, data mining and analytics, linguistics and computer science. Digital technologies need for intellectual exchange of diverse disciplines, collaborative production data and the sharing of knowledge forming history and the vision of the BLS. Cognitive science and AI, new socio-cultural developments accelerated the rise of the BLS as a discipline. The BLS stemmed from the USA first. The fading methods and organized study reports of educational sciences and psychology based on pure science and laboratory data motivated researchers to find new w ays for e xplaining effective l earning a nd t eaching ( Kirby, H oadley & C arr-Chellman, 2 005). The BLS claims that it is (itself) different from the other disciplines by intending to change the controlled experiments and added “contexts” such as museums and social relationships outside schools. The BLS developed new research methodologies such as design-based research for promoting learning and teaching. The BLS started with discussions by researchers of educational sciences, psychology and educational technology and emerged as a field in the 1990s. The idea was displayed in a conference in 1991. The Journal of Learning Sciences and the Learning Sciences Community were founded to disseminate and share this new idea. The BLS was accepted by an academic in the USA first and later in Europe. Researchers from diverse disciplines such as linguistic, education, engineering and humanities contributed to the development of the field. Although the field has not yet proven itself, it has found a place in universities as a research area. Thus, the BLS continues to augment with the introduction of graduate programs and establishment of Science of Learning Institutes in some universities. The BLS influences educational policy and practices widely. The number of brain research has been increasing for the last 20 years. Not only educators but also media and policymakers have been

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fascinated with “brain research” and attempted to infuse it into real educational environments pervasively. Consequently, the scope and effect of the BLS graduate programs, non-governmental organizations, journals and studies are expanding and growing. There has been a growing interest in the BLS education programs and so on, since research on technological developments and AI also benefits from learning models and theories to model machine learning and explore human understanding and brain–computer interaction. The history of the BLS still is being written as there are still grand challenges such as inadequate knowledge of process principles of the brain and the mind and brain structure vary, and it is very hard to map these variations within every human. There are still some concerns about how the brain solves the complex computational problems of life and how the brain copes with these problems. There is still a need for more sophisticated technological infrastructure to monitor the synchronic processing of the brain. These challenges make the BLS studies ambitious. Not only educators or psychologists but also engineers, health officials and graphic designers even judges all try to grasp how the brain processes information. These facts provide different paths to the BLS as a field to evolve and expand from education to medicine and law.

References AECT. (2018). History of LIDT. In Foundations of Learning and Instructional Design Technolog y: The Past, Present, and Future of Learning and Instructional Design Technolog y, edited by R. E. West. EdTech Books. Retrieved from, 2021 - edtechbooks.org Aldrich, R. (1994). John Locke. PROSPECTS: The Quarterly Review of Education, 24(1/2), 61–76. Ansari, D., König, J., Leask, M., & Tokuhama-Espinosa, T. (2017). Developmental Cognitive Neuroscience: Implications for Teachers’ Pedagogical Knowledge. In Pedagogical Knowledge and the Changing Nature of the Teaching Profession, edited by S. Guerriero. Paris : OECD Publishing. https://doi.org/10.1787/9789264270695-11-en Bandura, A. (2005). The evolution of social cognitive theory.In Great Minds in Management, edited by K. G. Smith, & M. A. Hitt. pp. 9–35. Oxford: Oxford University Press. Barnes, J. (Ed.). (1984). Complete Works of Aristotle, Volume 1: The Revised Oxford Translation (Vol. 96). Princeton University Press. Bransford, J. D., Brown, A. B., Cocking, R.. (2000) How People Learn: Brain, Mind, Experience, and School (Expanded Edition). Washington, DC: National Academies Press. Broughton, J. M., (1981). The genetic psychology of James Mark Baldwin. American Psychologist, 36, 396–407. Bruner, J. S. (1983a). In Search of Mind: Essays in Autobiography. New York: Harper and Row. Casper, S. T., & Gavrus, D. (Eds.). (2017). The History of the Brain and Mind Sciences: Technique, Technolog y, Therapy (Vol. 40). Rochester, NY, USA Boydell & Brewer. Darling-Hammond, L., Flook, L., Cook-Harvey, C., Barron, B., & Osher, D. (2020). Implications for educational practice of the science of learning and development. Applied Developmental Science, 24(2), 97–140.

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Ferrari, M., & McBride, H. (2011). Mind, brain, and education: The birth of a new science. Learning Landscapes, 5(1), 85–100. Fischer, K. W. (2008). Dynamic cycles of cognitive and brain development: Measuring growth in mind, brain, and education. In The Educated Brain, edited by A. M. Battro, K. W. Fischer & P. Léna. 127–150. Cambridge, U.K.: Cambridge University Press. Fischer, K. W.. & Bidell, T. R. (2006). Dynamic development of action, thought, and emotion . In Handbook of child psycholog y: Vol. 1. Theoretical models of human development, (6th ed.), edited by R. M. Lerner & W. Damon (Series editor). New York: Wiley. Fischer, K. W., Daniel, D. B., Immordino-Yang, M. H., Stern, E., Battro, A., & Koizumi, H. (2007). Why mind, brain, and education? Why now? Mind, Brain, and Education, 1, 1–2. Furlong, J. (2013). Education-An Anatomy of the Discipline: Rescuing the University Project? London: Routledge. Gardner, H. (1983). Frames of Mind: The Theory of Multiple Intelligences. New York: Basic Books. Gutek, G. L (2005). Historical and Philosophical Foundations of Education: A Biographical Introduction. Columbus, OH: Merrill/Prentice Hall, 2005, ss. 37–40. Guthrie, W. K. C., & Guthrie, W. K. C. (1986). A History of Greek Philosophy: Volume 5, The Later Plato and the Academy (Vol. 5). Cambridge U.K.: Cambridge University Press. Güven, İ. (2019) Eğitim Tarihi (History of Education). Ankara: PEGEMA Yayınları. Hergenhahm, B.R. (2009). An Introduction to the History of Psycholog y. Belmont, CA: Michele Sordi. Hergenhahm, B. R., & Henley, T. (2013). An Introduction to the History of Psycholog y. Wadsworth, USA: Cengage Learning. Hoadley, C. M. (2004). Learning and design: Why the learning sciences and instructional systems need each other. Educational Technolog y, 44(3), 6–12. Hofstetter, R., & Schneuwly, B. (2004). Introduction educational sciences in dynamic and hybrid institutionalization. Paedagogica Historica, 40(5-6), 569–589. Hofstetter, R. (2012). Educational sciences: Evolutions of a pluridisciplinary discipline at the crossroads of other disciplinary and professional fields (20th century). British Journal of Educational Studies, 60(4), 317–335. Jordan, B. & Henderson, A. (1995). Interaction analysis: Foundations and practice. Journal of the Learning Sciences, 4(1), 39–104. Kirby, J. A., Hoadley, C. M., & Carr-Chellman, A. A. (2005). Instructional systems design and the learning sciences: A citation analysis. Educational technolog y research and development, 53(1), 37–47. Kolodner, J. L. (2004). The learning sciences: Past, present, future. Educational Technolog y, 44(3), 34–40. Kristjánsson, K. (2016). Aristotle, Emotions, and Education. London, UK.: Routledge. Lee, V. (2018). A short history of the learning sciences. In Foundations of Learning and Instructional Design Technolog y - The Past, Present, and Future of Learning and Instructional Design Technolog y, edited by R. E. West. EdTech Books, Retrieved from, 2021 edtechbooks.org. Martin, J. (2002). The Education of John Dewey: A Biography. New York: Columbia University Press, 2002. Meyering, T. C. (2012). Historical Roots of Cognitive Science: The Rise of a Cognitive Theory of Perception from Antiquity to the Nineteenth Century (Vol. 208). Springer Science & Business Media.

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Murphy, M. M. (2006). The History and Philosophy of Education: Voices of Educational Pioneers. Upper Saddle River, NJ: Pearson/Merrill/Prentice Hall. OECD (2007). Understanding the Brain: The Birth of a Learning Science. Paris: OECD. Piaget, J. (1980). Adaptation and intelligence: Organic selection and phenocopy. Chicago: University of Chicago Press. Reble, A. (1999). Geschichte der Padagogik Bd. 1. Stuttgart: Klett-Cotta S. Sabitzer, B. (2011). Neurodidactics: Brain-based Ideas for ICT and Computer Science Education. The International Journal of Learning, 18(2), 167–178. Sawyer, R. K. (Ed.). (2005). The Cambridge Handbook of the Learning Sciences. Cambridge, UK: Cambridge University Press. Smith, L. (2000). A brief biography of Piaget. Jean Piaget Society Website (http://www. piaget. org/aboutPiaget. html). Solso, R. L. (Ed.). (1999). Mind and Brain Sciences in the 21st Century. Cambridge, Massachusetts, USA: MIT Press. Sommerhoff, D., Szameitat, A., Vogel, F., Chernikova, O., Loderer, K., & Fischer, F. (2018). What do we teach when we teach the learning sciences? A document analysis of 75 graduate programs. Journal of the Learning Sciences, 27(2), 319–351. Sousa, D. A. (Ed.). (2010). Mind, Brain, & Education: Neuroscience Implications for the Classroom. Bloomington, IN. USA: Solution Tree Press. Stein, Z. & Fischer, K. W. (2011). Directions for mind, brain, and education: Methods, models, and morality. Educational Philosophy and Theory, 43, 56–66. Thomasson, A. (2008). Categories. In The Stanford Encyclopedia of Philosophy, edited by E. N. Zalta (Ed.). First published June 3, 2004; substantive revision March 7, 2018. https:// plato.stanford.edu/entries/categories/ retrieved, 2021. Tokuhama-Espinosa, T. (2010). Mind, Brain, and Education Science: A Comprehensive Guide to the New Brain-Based Teaching. NY, USA: WW Norton & Company. Tokuhama-Espinosa, T. (2017). Mind, Brain, and Education Science: An International Delphi Survey. Retrieved 2021, - researchgate.net. Tokuhama-Espinosa, T. (2019) The learning sciences framework in educational leadership. Frontiers in Educcation, 4, 136. Vygotsky, L. (1978). Mind in Society: The Development of Higher Psychological Processes. Translated by M. Cole, V. John-Steiner, S. Scribner, & E. Souberman. Cambridge, MA: Harvard University Press.

Chapter 3 INSIGHTS FROM BRAIN RESEARCH ON TEACHING AND LEARNING David A. Sousa

Educational Neuroscience This chapter begins with a basic question: What is your definition of teaching? Is it “the act of one person imparting knowledge or skill to another?” Is it “the sharing of information that falls within a discipline?” Is it “the act of a person who teaches?” Or perhaps some variation of these? I would like to share my definition which will then set the stage for this chapter. Teaching is the only profession whose job it is to change the human brain every day. When learners decide to remember what the teacher has presented, their brains establish new connections between neurons and consolidate that learning into existing cerebral networks or create new ones. In sum, teachers are brain changers. What a remarkably different and more accurate description of the teaching–learning process. Teachers have been changing learners’ brains for centuries. Yet, until recently, most teachers had little knowledge about how the human brain really learns. They relied on what teachers had done in the past, so that teaching, in effect, was a handed-down art form. The artistry was manifested in the techniques, strategies and styles that teachers used when presenting their lessons. Sometimes their performance resulted in students learning, but sometimes it did not. And when it did not, teachers often did not know why. But that has now changed. In recent years, there has been an explosion of scientific research on the human brain, thanks to sophisticated imaging and recording devices. Much of that earlier research focused on diagnosing and treating medical problems that involve the brain. However, later research is revealing much about the brain’s growth and development, and even its inner workings during learning. These findings have led to the emergence of a new field of

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scientific inquiry—one that examines how some of the results of neuroscientific research can be translated into educational practice in schools and classrooms. This field is known as e ducational n euroscience. The good news is that neuroscience research continues to tell us more about how the brain learns. However, the bad news is that these findings are not getting to teachers—the brain changers—fast enough. Some of the findings in educational neuroscience confirm th ings ab out teaching and learning that we already knew from experience. An important added value of the neuroscientific research is that it often gives us insight into why some teaching strategies result in learning while others do not. And once teachers know the why, they can make the necessary enhancements or changes to their strategies that are likely to result in greater student achievement in future lessons. This chapter will present some of the major findings from brain research that have implications for teaching and learning. It will examine general findings that apply to learning in general as well as some fi ndings that are more closely related to learning specific topics. More importantly, it will also suggest how the research insights described here can be translated into educational practice. If knowledge is power, then the application of that knowledge is an even greater power. Here are a few of the insights we have gained so far.

The Changing Brain (Neuroplasticity) The concept of neuroplasticity refers to the brain’s ability to rewire and reorganize itself based on new information and experiences, a process that continues throughout our lives. Whenever we learn something new, the physical structure of our brain changes as it creates additional connections between neurons. It establishes new pathways and discards those that are dormant. This continuing rewiring allows us to learn, remember and acquire new skills. Every teaching lesson, therefore, has the potential for making new neural connections, especially in the rapidly developing brains of young school children. This is another example of how teachers are brain changers.

The Growing Brain (Neurogenesis) An even more exciting discovery is the brain’s ability to grow new neurons, called neurogenesis (Gage 2002; Urbán and Guillemot 2014). This finding opens up new frontiers and suggests that the strategies teachers use in the classroom can increase or impair the growth of new neurons in their students’ brains. What an awesome prospect!

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Power of Emotions over Learning and Memory Simply put, emotion drives attention, and attention drives learning and memory. Emotions and learning are maintained by interdependent cerebral processes, so they affect each other. As a result, emotions enhance memory (Fastenrath et al. 2014). People tend to remember the emotionally best and worst events in their lives. A small structure called the amygdala, buried deep within the brain’s emotional (limbic) area, is responsible for encoding emotionally significant experiences into long-term memory. Likewise, certain cognitive concepts, such as the Holocaust and abortion can evoke strong emotions. Emotions are always present in the school but rarely do teachers give attention to them. Upper-grade teachers tend to focus more on delivering the curriculum than on acknowledging emotions in the classroom. However, emotions do influence l earning i n t wo d istinct w ays. O ne w ay r efers t o the emotional environment where the learning occurs. Do students feel respected by the teacher and their peers? Are student opinions valued? When students feel positive about the classroom’s emotional climate, substances called endorphins are released in the brain. Endorphins produce a feeling of euphoria and stimulate cognitive processing, thus making learning more pleasurable and successful. Conversely, if students are stressed and have a negative perception of the classroom climate, then the body releases cortisol. This hormone races through the brain and body, activating defensive behaviors. Successful learning is much less likely to occur as the student’s brain is more focused on survival rather than acquiring the lesson content. These biological events explain how important it is for teachers and school administrators to maintain a positive learning climate in classrooms and schools. This happens when they demonstrate that they really care about their students’ success. This means spending less time on class rules and test schedules and more time on asking students how they learn and what teaching strategies work best for them. That includes providing students with an accurate record of how well they are doing in achieving the curriculum objectives. The goal is to stimulate the endorphins and subdue the cortisol. The second way refers to the extent that students make an emotional investment in what they are learning. The more emotionally involved students are with the lesson content the more likely they are to remember it. Teachers can promote this emotional connection by asking students to reflect on the motivations of the people they encounter in their curriculum, such as famous scientists, writers, artists, explorers and mathematicians. It also helps to get students involved in role-playing, simulations and cooperative projects based on the curriculum content.

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Social Neuroscience In recent years, researchers have begun to recognize the significant influence that social relationships have over the brain’s growth, development and organization. Studies show that social interactions stimulate substantial cerebral activity even in infants (McDonald and Perdue 2018). How students interact socially with their teacher and each other while learning can affect the quality and depth of the learning as well as its potential to be remembered. These interactions affect the development of regions of the brain involved in social cognition and self-awareness. This research suggests that if teachers understand the social brain, they can create class groupings that will result in productive student engagement and effective learning and remembering.

Learning Spoken Language Babies are born with a brain that is pre-wired for acquiring any spoken language. Shortly after birth, these language centers begin to discriminate between the repetitive speech sounds of their parents or primary caregivers and background noise. By the time they reach the age of six months, most babies recognize the basic sounds of their native language, called phonemes. Although children vary in their development of speech and language skills, there is a natural progression or timetable for mastering the skills of language. This mastery is particularly critical during the child’s first three years. This is the age by which children typically accomplish the major components of language development (Gervain 2020). However, the speed and quality of the child’s language development up to this point will depend largely on the level of literacy at home. Impact of technology on language development

Studies show that excessive amounts of time that young children devote to watching television and interacting with other technologies can result in cognitive, language and social/emotional delays (Radesky and Christakis 2016). During this critical time for language learning, the child should be in face-toface conversations with the parent or caregiver rather than spending excessive time with technology. This is not an easy task because the young brain is learning that engaging with technology is more appealing than conversing with humans. Remarkably, the very young brain has the immense power to learn several languages at once. Neuroscience research suggests that typical children can best distinguish the sounds of the different languages from 10 months

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to about 6 years of age when this ability slowly declines to about 17 years of age (Hartshorne, Tenenbaum and Pinker 2018). Because this is such a laborintensive cerebral process, the earlier one begins learning a second language, the better. That is when all those extra neurons are available for wiring new language connections. As youngsters move through their late teen and adult years, unstimulated neurons die off so fewer neurons are available for this task. Beginning to learn another language later is certainly possible but requires more effort and motivation. This research should not be interpreted as discouraging adolescents and adults from pursuing second language study. But adults should realize that learning a second language later in life is more difficult because the phonemes, grammar and syntax of the native language are likely to interfere somewhat with learning those of the second language (Pulido 2021). However, this interference, called negative transfer, can be diminished by consistent study and regular conversational practice in the second language.

Learning to Read The young brain’s ability to acquire spoken language with amazing speed and accuracy is the result of specialized cerebral areas pre-wired to focus on this task. But there are no specialized areas in the brain pre-wired for reading because reading is a relatively new phenomenon in the development of humans. For numerous cultures throughout human history, spoken language was necessary for survival, but reading was not. Even today, nearly one-half of the world’s 7,000 or so living languages do not have a written form (Eberhard, Simons and Fennig 2020). In reading, the brain must match the native language phonemes that the child has been using for several years with either a visual abstract alphabetic, syllabic or logographic unit found in written text (usually referred to as graphemes). In many languages, it is not a one-to-one match, that is, one sound is represented either by only one letter, group of letters or unique logograph. In English, for example, there are only 26 letters of the alphabet to represent the language’s approximately 44 phonemes. The fact that one phoneme can represent more than one grapheme, and that one grapheme can represent more than one phoneme, significantly complicates the brain’s work in learning to read. As a result, learning to read is probably the most difficult task the young brain undertakes. Exactly how the brain does this is still not completely understood. However, cognitive neuroscience research and brain imaging have produced findings that give us some significant clues as to how reading emerges through a set of learned skills. The research suggests that, when learning to read, the brain creates a

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neural network specializing in matching phonemes to graphemes or logographs and interpreting meaning (Dehaene 2009; Wandell and Le 2017). To use this network successfully, beginning readers need to develop five competencies. ●

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Phonemic awareness: The learners must be able to recognize the different phonemes of their native language and successfully manipulate them to create words and phrases. Phonics: This is the instructional approach that teaches learners to correctly match the language’s phonemes to the graphemes or logographs found in written text. Vocabulary: Readers must usually possess the word in their mental vocabulary to recognize it in print. Therefore, it is important for parents and caregivers to have regular conversations with pre-readers to enlarge their mental vocabulary and to understand the meaning of words. Comprehension: This is the ability to understand the meaning of words in context, to recognize grammatical structures and to ensure that the text makes sense. Fluency: Although learning to read is an intensive neural workout, it usually becomes easier with practice. The goal is for readers to be able to decode words and their meaning quickly so that reading becomes a useful learning tool rather than a burdensome mental struggle.

Some of these skills may begin to develop at home during the preschool years. The degree to which children experience literacy at home determines whether they will begin school not just able to learn to read but also ready to learn to read. Research studies clearly show that the more pre-reading language enrichment a child has, the more quickly the brain establishes networks needed to learn to read (Benischek et al. 2020).

Developmental Dyslexia Learning to read is a complex process, requiring various regions of the brain to form new and collaborating networks. This development does not always work as planned. Pre-natal modifications in certain brain regions while the brain grows may eventually impede the child’s typical progress in learning to read. These difficulties lead to a condition known as developmental dyslexia, a disorder characterized by severe and persistent problems in the acquisition of reading in the absence of problems regarding either intelligence, vision, motivation or educational environment. Fortunately, imaging studies have revealed some particularly useful findings that help us understand the brain with dyslexia. Researchers have

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identified areas of the brain circuits substantially involved in typical and dyslexic reading (Hancock, Richlan and Hoeft 2017). Studies also reveal that these circuits are across languages with different orthographies, including Chinese (Cao et al. 2017; Martin, Kronbichler and Richlan 2016). Other studies suggest there are several potential causes for developmental dyslexia, including genetic defects that can result in difficulties with phonological and auditory processing among other deficiencies. (Paracchini, Diaz and Stein 2016). Fortunately, these findings have recommended instructional strategies that have shown to be successful with students with dyslexia, especially those strategies involving assistive technology (Fälth and Svensson 2015; Rodriguez-Goncalves et al. 2021).

Teaching Reading Regardless of the language, reading is a complex cerebral process that relies heavily on previously acquired spoken language and requires learning-specific skills not innate to the human brain. The most effective methods for teaching reading depend on the relationship between the sounds of a language and the symbols used to represent those sounds. Here are the guidelines that the research suggests are most appropriate for learning to read in English, some of which will have an application to other languages. ●

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Start with a plan that logically moves learners through the steps for acquiring reading. Begin with manipulating the language’s phonemes, rhymes and meaning of words. Move then to construct simple sentences and introduce prefixes and suffixes. Learners now construct more complex sentences with correct grammar and syntax. Finally, the learner recognizes that the meaning of words can change in different contexts by reading examples from the literature that illustrate contextual variations of the language. Develop awareness of phonemes so that learners can accurately distinguish between those that are very similar in sound, for example bat and pat. Help students master the ability to match phonemes to their graphemes and to say the words aloud so the brain learns to match what it sees to what it hears. Ensure that the learners comprehend the meaning of the words they are reading and get a sense of the language’s syntax. Teachers should read aloud as learners follow along with the text. This helps learners understand prosody, the flow, rhythm and tonal changes of the language. Have students move a finger under the word to show that

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they are correctly matching what they hear the teacher say to what they see on paper. Introduce interesting literature appropriate for the learners’ reading levels that will motivate them to read further.

There are numerous published programs on the educational market for teaching reading. However, all are not based on the latest research findings that constitute the science of learning to read. It is important for educators to carefully study the reading programs they have in their schools, or intend to obtain, to ensure they are based on the latest research.

Learning Mathematics Not only is the newborn brain pre-wired for acquiring spoken language, but it is also pre-wired for understanding numbers and number relationships. This aptitude is called “number sense.” It describes a person’s ability to recognize that something has changed in a small collection when, without that person’s knowledge, an object has been added or removed from the collection (Dehaene 2011). It also includes the ability to compare the sizes of two collections shown simultaneously, and the ability to remember the numbers of objects presented successively in time. We have number sense because numbers have meaning for us, just like words, and very likely contributed to our survival as a species. As in the case of learning words, we were born with them or, at the very least, born with the ability to acquire them without effort at a very young age. Furthermore, brain scans have revealed a distinct neural network for mathematical knowledge (Amalric and Dehaene 2019). Just as the brain has a specialized word form area to assist with learning to read, it also has a specialized number form area to assist in mathematical processing (Yeo, Wilkey and Price 2017). Because we are born with number sense and a brain region for processing mathematical operations does not necessary mean that we all can become great mathematicians. But it does mean that most of us have the potential to be a lot better at mathematics than we think. If this is true, then why do so many students and adults say they “can’t do math?” Developing number sense seems to be critical to a child’s future success in learning mathematics. Studies show that the number sense developed by infants as young as six months of age predicts their success in learning mathematics in the elementary grades (Starr, Libertus and Brannon 2013). It is essential, therefore, that instruction in pre-school and in the early elementary grades focus on developing number sense. The goal is to help young students develop skills in mental calculation, computational estimation, judging the

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relative magnitude of numbers and recognizing part-whole relationships as well as place value concepts and, of course, problem-solving. As a child develops number sense, the research suggests that cerebral networks progress through three phases. In the first phase, the visual processing system recognizes objects in a collection. For small collections, the number can be determined without counting, thanks to our innate capabilities. As the collection size increases, the child’s brain progresses to the second phase by using number words to communicate the exact count. Because writing number words for large collections is tedious, and the words are not convenient for mathematical manipulation, the child moves on to phase three, relying on numerical symbols and operational signs (Griffin 2002). For example, suppose the visual collection is:  , which represents the first phase. In phase two, the corresponding number words are One, Two, Three and Four are included. And phase three incorporates the numerical symbols 1, 2, 3 and 4, and the operational signs are +, −, ÷ and ×. At first, the flow from one phase to another is linear. However, with practice all three phases interact as the child deals with mathematical operations (Grotheer, Ambrus and Kovács 2016). This research demonstrates the significant impact that language processing has on mathematical operations. For instance, when we do mathematical operations, such as multiplying a pair of two-digit numbers, we silently say what we are doing in our heads. Not surprisingly, studies show that children with strong language skills when entering the primary grades, especially in syntax, tend to do better in mathematics than children with poorer language skills (Chow and Ekholm 2019; Vukovic and Lesaux 2013). However, although these cerebral areas do cooperate during mathematical processing, they are still distinct and separate anatomical areas. Case studies show that one area can function normally even when the other is damaged (Brannon 2005). Therefore, teachers should not assume that students who have difficulty with language processing will also have difficulty with arithmetic operations, and vice versa.

Teaching Mathematics A summary of current research suggests a reasonable process for teaching mathematics to children and adolescents should proceed through four steps. ● ●

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Build on innate number sense and counting. Shift to symbolic representations (integers) as shortcuts for doing arithmetic manipulation. Explain arithmetic axioms so students understand mathematics concepts rather than just memorizing computation procedures.

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For older students, explain geometric and other mathematical axioms and theorems, showing practical applications.

Other considerations: Young students’ attitudes toward mathematics emerge in the early grades, mainly driven by their progress and success in learning the mathematics curriculum. These early attitudes become reliable predictors of the students’ achievement in mathematics in later grades (Burrus and Mooreb 2016). Therefore, it is important that teachers in preschool and primary grades present mathematics in a way that develops positive attitudes in students towards the content, structure and importance of mathematics in the real world. Here are some suggested instructional strategies for teachers of mathematics to consider. ●

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Use manipulatives whenever possible because the young brain has difficulty with the abstract concepts in mathematics. Manipulatives help convert the abstract to concrete objects, and they can be particularly useful to show patterns, fractions and three-dimensional shapes. Encourage students to talk with others in small groups about the steps they took to solve a problem and to discuss various problem-solving strategies. Look for ways to show how mathematics concepts connect to the real world, such as how mathematical operations are used in sports statistics. This helps students see the relevance of mathematics, and may reduce the number of times they ask, “Why do I have to know this?” When teaching number concepts, use visual aids such as number lines, pictures, charts and various forms of graphs to illustrate number relationships. Using visuals recruits additional regions of the brain that contribute to learning and remembering the concepts. Research shows that students who made accurate visual representations were almost six times more successful at solving mathematics problems than students who did not make visual representations (Boonen et al. 2014). Testing often raises anxiety in students. Use formative assessments to ensure that students are making adequate progress. Ungraded formative assessments are more brain-friendly than summative assessments because they are ongoing, and they give the students consistent feedback on their progress toward their learning goals in mathematics (Sousa 2015). Look for ways to integrate mathematics with other subject areas, such as science, music and the visual arts. These connections help students see the real-world applications of mathematics, thus making it more relevant and less abstract.

Although they are born with number sense, students may still ask why mathematics is even worth learning. One major reason is that it will develop their

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problem-solving skills, and thus allow them to better handle the challenges that life will certainly bring. On a broader scale, our world is full of patterns. We find them in snowflakes, in flowers, in seashells, in the distinctive songs of birds and in the markings on animals such as leopards and zebras. If mathematics is truly the study of patterns, then teachers should help students recognize that learning mathematics will not only be useful in their lives but will also help them appreciate the amazing patterns that exist in our world.

Developing Social and Emotional Skills Classroom instruction usually focuses on developing students’ cognitive processes. After the primary grades, teachers rarely purposefully consider addressing their students’ social and emotional development, assuming that will occur at home. But neuroscience research is discovering the strong influences that the social and emotional regions of the brain have on cognitive development, and schools need to rethink their approach. One prominent model of social–emotional learning (SEL) comes from the Collaborative for Academic, Social and Emotional Learning. It lists the five competencies for SEL to be self-awareness, social awareness, responsible decision making, selfmanagement and relationship skills. Here are some of the key findings from that research and their implications for teaching. ●

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We noted earlier that emotions enhance memory, thanks to the amygdala (Fastenrath et al. 2014). We tend to remember the best and worst events in our lives. Ordinary ones fade away. Meanwhile, another set of neurons, called “mirror neurons,” is developing in the pre-school brain (Dai, Li and Zhai 2019). Mirror neurons are thought to be responsible in part for empathy and for directing one’s social interactions. Apparently, the cerebral regions responsible for social behavior are maturing faster than either the emotional system, which typically matures between the ages of 10 and 12 years, or the cognitive system, which typically matures between the ages of 22 and 24 years. These research findings clearly remind educators and parents that they should not expect social, emotional and cognitive abilities to develop at the same rate as children grow up. Social competencies will emerge earlier, followed by emotional competencies, and high cognitive competencies will appear later. This staggered maturing implies that emphasis on developing social competencies should begin in the preschool years, recognizing that emotional and cognitive development are still in their early stages.

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Identify and manage emotions. One major task of the brain’s cognitive system (located in the frontal lobe) is to control emotional responses. Yet that area is still 10 years behind the emotional system in full maturation for most individuals, so its ability to restrain emotional excesses is limited. That is neuroscience’s partial explanation for irrational teenage behavior. It is important, then, to help preteens and adolescents detect when their emotions are running high, and what self-management strategies they can use to moderate them before they result in undesirable behavior.

Research-based models for developing social and emotional learning have emerged in recent years with considerable success. For instance, a metaanalysis of 82 international studies involved more than 97,000 students from kindergarten to high school, where the effects of SEL were assessed 6 months to 18 years after the programs ended (Taylor et al. 2017). Even 3.5 years after the last intervention, the academic performance of students exposed to SEL programs was an average of 13 percentile points higher than their non-SEL peers. The analysis also revealed that behavioral problems, emotional distress and drug use were all significantly lower for students who participated in SEL programs, while the development of social and emotional skills and positive attitudes toward one’s self, others, and their school was higher. Other studies of SEL also showed favorable results regarding improved social behavior and student achievement. For instance, a study of 531 third through fifth-grade students in urban classrooms involved in SEL interventions that focused on a supportive learning environment and interpersonal negotiation strategies showed a significant reduction in aggressive behavior over the course of the school year (Portnow, Downer and Brown 2018). A meta-analysis of more than 135,000 students in grades pre-K to 12 found that those with SEL interventions scored significantly better in tests of mathematics, reading and science than their non-SEL peers (Corcoran et al. 2018).

Format for Developing SEL Competencies It is still very common to find classrooms where the teacher is talking for most of the learning period while the students sit passively at their desks. This traditional format no longer engages the brains of today’s students because their hand-held digital devices have acclimated them to engage interactively with their learning. To develop SEL competencies, we need to encourage students to work in small groups of academic teams to achieve learning objectives. Properly implemented, academic teaming not only develop social and emotional skills but also helps students better learn and remember the lesson’s cognitive content.

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Why does working in small groups result in more productive learning experiences than working alone? Neuroscience research can help explain why. Studies measuring brain activity have found that the brain waves of some individuals working as a team to solve a problem begin to synchronize with each other (Szymanski et al. 2017). One study found this same result using high-school students (Dikker et al. 2017). The synchronized team members were able to make quicker decisions and solve a problem more efficiently than those members whose brain waves were not synchronized. This may explain why some small learning groups perform better than others. In academic teams, students develop SEL competencies like prosocial skills, selfmanagement and conflict resolution through working together on rigorous, relevant tasks that help them make meaning out of what they learn (Toth and Sousa 2019).

Integrating the Arts in Learning The results of studies in neuroscience have suggested ways in which the arts develop cognition. These studies seem to indicate that each art form involves different brain networks (Posner et al. 2008). Using brainwave detection technology with children, the researchers discovered that arts training required them to do sustained focus, and that this concentrated attention improved cognition. Children who begin participating in arts training at an early age get the benefit of improving their cognitive growth while their brain is still developing. Furthermore, the arts often involve powerful emotions, and we have already discussed how such emotions enhance cognitive processing and memory. Activities in the arts promote social growth because they often require collaboration through group planning, problem-solving and performance. They also foster discussion, debate and teamwork. In these ways, the arts become a counterbalance to the antisocial nature of technology. These research findings do not suggest that we teach the arts only because they enhance the learning of other academic subjects. They also justify teaching arts for the arts’ sake. One of the greatest advantages of integrating arts in the teaching of other subjects is that it stimulates creativity. Working with visual arts, linguistic arts, movement arts and music all require the brain to be creative, and this creativity can stimulate learning and deep thinking in other content areas. Neuroscientists studying creativity suggest that creative thinking involves communication among brain areas that do not normally interact with one another in noncreative thinking. More brain areas are stimulated and interact when performing creative activities than during conventional

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activities, especially in areas involving working memory, cognition and emotion (Rominger et al. 2020). We now know, too, that not only can teachers design lessons that stimulate creative thinking, but they can also teach their students to be more creative. Creativity is not a fixed cerebral aptitude but can be enhanced through appropriate learning challenges (Baer 2020).

Influence of Technology on Brain Growth and Development Today’s students are true digital natives. They have never known a world without digital devices and the Internet. To a certain extent, they are technology addicted. Research studies are alerting us to the effects that technology’s assault is having on the human brain, both positive and negative (Sousa 2016). And it starts early in life. For example, moderate screen time exposure reduces the attention abilities of preschool children (Zivan et al. 2019), and lowers the effectiveness of working memory, a vital component for new learning (Baron 2021). Effect on thinking skills

One of the more concerning aspects of extensive time with technology is the effect it may have on the development of higher-order thinking skills. Before the Internet, we faced a major problem by analyzing it, gathering needed information, discussing it with others and often resorting to higher-order thinking to solve it. Now, researchers have already found that students usually go directly to the Internet to get answers to challenging questions rather than solving them with their own brains (Sparrow, Liu and Wegner 2011). Because they know where to find the information, they are not motivated to learn the information itself. Thus, their brains are not practicing the mechanisms of higher-order thinking, such as application, analysis, evaluation, creativity and metacognition. We need to recognize that early and consistent reliance on the Internet may diminish the brain’s need to be creative, think critically and retain information. Teachers at all levels need to plan their instruction to use the Internet to expand student creativity and problem-solving skills rather than replacing them. Effect on social skills

The human brain has evolved regions that specifically promote social interaction because such behavior generally supports our species’ survival. Do digital devices and other technology do more to support or inhibit that

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genetic predisposition by causing neural networks to strengthen or rewire? Researchers are working to answer this question. For instance, the increasing time that children and adolescents are spending watching screens means less time in face-to-face conversations with their caregivers and peers. They are not learning that in-person interactions include many nonverbal cues such as body language, eye contact, emotional displays and facial changes. Failure to understand such cues can easily lead to misinterpretations of the speaker’s message. Communicating with others is how we establish and maintain relationships, and technology is having an impact here. Even when in a group, adolescents will often ignore those physically around them and focus on the distant phone conversation. Thus, they are learning that relationships are more device-centered than person-centered. Although many adolescents brag about the number of friends they have on social media, in reality, excessive dependence on it further diminishes interpersonal contact and robs the individual of the human experience of developing meaningful in-person relationships. Regrettably, people tend to say things online that they would never say to someone’s face. Because of the anonymity of the Internet, they make statements that would get them in serious trouble if said in person. Imprudent and insensitive remarks are troubling because the young people who make and receive them are so impressionable, tempting them to make poor decisions easier, faster and with more serious consequences than ever. Another concern is the degree to which excessive screen time is interfering with young people’s sleep patterns. The fear of missing out on messages often keeps these individuals awake until the early morning hours, resetting their bodies’ sleep clock and interfering with their academic performance during the daytime (Evers et al. 2020). Of course, there are positive aspects of technology and social media use. For example, peer tutoring, sharing learning with students all over the world, distance education, web seminars and blended learning. We also need to ensure adequate time for face-to-face communications. Teachers can do this through academic teaming, group projects, simulations and other personcentered activities that develop and enhance social skills.

Preparing New Teachers In my professional domestic and international travels during recent years, I have encountered very few teacher-preparation colleges and universities that have incorporated educational neuroscience into their curriculum. The explanations for this unfortunate situation vary, but often result from the lack of faculty members in these schools who are themselves so familiar with the

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educational applications of neuroscience research that they can effectively teach it. Consequently, these schools are not equipping their graduates with the latest information on how the brain learns, nor with the instructional strategies that will help their students engage in their learning and achievement. National, state and local authorities responsible for schools as well as administrators in teacher-training institutions must insist that their pre-service and in-service teachers are completely and consistently updated on the latest research on how the brain learns. We owe our children nothing less.

References Amalric, Marie, and Stanislas Dehaene. 2019. “A Distinct Cortical Network for Mathematical Knowledge in the Human Brain.” NeuroImage 189: 19–31. doi: 10.1016/j.neuroimage.2019.01.001. Baer, John. 2020. “Teaching Creativity.” In Encyclopedia of Creativity, edited by Steven Pritzker and Mark Runco (3rd ed., pp. 54–546). Cambridge, MA: Academic Press. Baron, Naomi S. 2021. “Know What? How Digital Technologies Undermine Learning and Remembering.” Journal of Pragmatics 175: 27–37. doi: 10.1016/j.pragma.2021.01.011. Benischek, Alina, Xiangyu Long, Christiane S. Rohr, Signe Bray, Debra Dewey, and Catherine Lebel. 2020. “Pre-reading Language Abilities and the Brain’s Functional Reading Network in Young Children.” NeuroImage 217: 116903. doi: 10.1016/j. neuroimage.2020.116903. Boonen, Anton J. H., Floryt van Wesel, Jelle Jolles, and Menno van der Schoot. 2014. “The Role of Visual Representation Type, Spatial Ability, and Reading Comprehension in Word Problem Solving: An Item-Level Analysis in Elementary School Children.” International Journal of Educational Research 68: 15–26. doi: 10.1016/j.ijer.2014.08.001. Brannon, Elizabeth M. 2005. “The Independence of Language and Mathematical Reasoning.” Proceedings of the National Academy of Sciences 102(9): 3177–3178. doi: 10.1073/pnas.0500328102. Burrus, Jeremy, and Raeal Mooreb. 2016. “The Incremental Validity of Beliefs and Attitudes for Predicting Mathematics Achievement.” Learning and Individual Differences 50: 246–251. doi: 10.1016/j.lindif.2016.08.019. Cao, Fan, Xin Yan, Zhao Wang, Yanni Liu, Jin Wang, Gregory J. Spray, and Yuan Deng. 2017. “Neural Signatures of Phonological Deficits in Chinese Developmental Dyslexia.” NeuroImage 146: 301–311. doi: 10.1016/j.neuroimage.2016.11.051. Chow, Jason C., and Eric Ekholm. 2019. “Language Domains Differentially Predict Mathematics Performance in Young Children.” Early Childhood Research Quarterly 46: 179–186. doi: 10.1016/j.ecresq.2018.02.011. Corcoran, Roisin P., Alan C. K. Cheung, Elizabeth Kim, and Chen Xie. 2018. “Effective Universal School-Based Social and Emotional Learning Programs for Improving Academic Achievement: A Systematic Review and Meta-Analysis of 50 Years of Research.” Educational Research Review 25: 56–72. doi: 10.1016/j.edurev.2017.12.001. Dai, Junqiang, Chaolin Li, and Hongchang Zhai. 2019. “Development of the Functional Connectivity of the Frontoparietal Mirror Neuron Network in Preschool Children: An Investigation Under Resting State.” Journal of Clinical Neuroscience 70: 214–220. doi: 10.1016/j.jocn.2019.07.070.

Insights from Brain Research on Teaching and Learning 49 Dehaene, Stanislas. 2009. Reading in the Brain. New York: Viking. Dehaene, Stanislas. 2011. The Number Sense: How the Mind Creates Mathematics (2nd ed.). New York: Oxford University Press. Dikker, Suzanne, Lu Wan, Ido Davidesco, Lisa Kaggen, Matthias Oostrik, James McClintock, Jess Rowland, et al. 2017. “Brain-to-Brain Synchrony Tracks Real-World Dynamic Group Interactions in the Classroom.” Current Biolog y 27(9): 1375–1380. doi: 10.1016/j.cub.2017.04.002. Eberhard, David M., Gary F. Simons, and Charles D. Fennig, . 2020. Ethnologue: Languages of the World (23rd ed.). Dallas, TX: SIL International. Evers, Katerina, Sufen Chen, Sebastiaan Rothmann, Amandeep Dhir, and Ståle Pallesen. 2020. “Investigating the Relation Among Disturbed Sleep Due to Social Media Use, School Burnout, and Academic Performance.” Journal of Adolescence 84: 156–164. doi: 10.1016/j.adolescence.2020.08.011. Falth, Linda, and Idor Svensson. 2015. “An App as ‘Reading Glasses’-- A Study of the Interaction Between Individual and Assistive Technology for Students with a Dyslexic Profile.” International Journal of Teaching and Education 3(1): 1–12. doi: 10.20472/ TE.2015.3.1.001. Fastenrath, Matthais, David Coynel, Klara Spalek, Annette Milnik, Leo Gschwind, Benno Roozendaal, Andreas Papassotiropoulos, and Dominique J. F. de Quervain. 2014. “Dynamic Modulation of Amygdala–Hippocampal Connectivity by Emotional Arousal.” Journal of Neuroscience 34(42): 13935–13947. doi: 10.1523/ JNEUROSCI.0786-14.2014. Gage, Fred H. 2002. “Neurogenesis in the Adult Brain.” Journal of Neuroscience 22(3): 612– 613. doi: 10.1523/JNEUROSCI.22-03-00612.2002. Gervain, Judit. 2020. “Chapter 15 - Typical Language Development.” In Handbook of Clinical Neurolog y: Volume 173, edited by Anne Gallagher, Christine Bulteau, David Cohen, and Jacques L. Michaud (pp. 171–183). New York: Elsevier. Griffin, Sharon. 2002. “The Development of Math Competence in the Preschool and Early School Years: Cognitive Foundations and Instructional Strategies.” In Mathematical Cognition: Current Perspectives on Cognition, Learning, and Instruction, edited by James M. Royer (pp. 1–32). Greenwich, CT: Information Age. Grotheer, Mareike, Géza Gergely Ambrus, and Gyula Kovács. 2016. “Causal Evidence of the Involvement of the Number Form Area in the Visual Detection of Numbers and Letters.” NeuroImage 132: 314–319. doi: 10.1016/j.neuroimage.2016.02.069. Hancock, Roeland, Fabio Richlan, and Fumiko Hoeft. 2017. “Possible Roles for FrontoStriatal Circuits in Reading Disorder.” Neuroscience & Biobehavioral Reviews 72: 243– 260. doi: 10.1016/j.neubiorev.2016.10.025. Hartshorne, Joshua K., Joshua B. Tenenbaum, and Steven Pinker. 2018. “A Critical Period for Second Language Acquisition: Evidence from 2/3 Million English Speakers.” Cognition 177: 263–277. doi: 10.1016/j.cognition.2018.04.007. Martin, Anna, Martin Kronbichler, and Fabio Richlan. 2016. “Dyslexic Brain Activation Abnormalities in Deep and Shallow Orthographies: A Meta-Analysis of 28 Functional Neuroimaging Studies.” Human Brain Mapping 37: 2676–2699. doi: 10.1002/hbm.23202. McDonald, Nicole M., and Katherine L. Perdue. 2018. “The Infant Brain in the Social World: Moving Toward Interactive Social Neuroscience with Functional NearInfrared Spectroscopy.” Neuroscience & Biobehavioral Reviews 87: 38–49. doi: 10.1016/j. neubiorev.2018.01.007.

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Paracchini, Silvia, Rebeca Diaz, and John Stein. 2016. “Chapter Two - Advances in Dyslexia Genetics—New Insights Into the Role of Brain Asymmetries.” In Advances in Genetics, edited by Theodore Friedmann, Jay C. Dunlap, and Stephen F. Goodwin (pp. 53–97). New York: Elsevier. Portnow, Sam, Jason T. Downer, and Joshua Brown. 2018. “Reductions in Aggressive Behavior Within the Context of a Universal, Social Emotional Learning Program: Classroom- and Student-Level Mechanisms.” Journal of School Psycholog y 68: 38–52. doi: 10.1016/j.jsp.2017.12.004. Posner, Michael, Mary K. Rothbart, Brad E. Sheese, and Jessica Kieras. 2008. “How Arts Training Influences Cognition.” In Learning, Arts, and the Brain, edited by Carolyn Asbury and Barbara Rich (pp. 1–10). New York: Dana Press. Pulido, Manuel F. 2021. “Native Language Inhibition Predicts More Successful Second Language Learning: Evidence of Two ERP Pathways During Learning.” Neuropsychologia 152: 107732. doi: 10.1016/j.neuropsychologia.2020.107732. Radesky, Jenny, and Dimitri Christakis. 2016. “Media and Young Minds.” Pediatrics 138(5). doi: 10.1542/peds.2016-2591. Rodriguez-Goncalves, Roxana, Angel Garcia-Crespo, Adrian Ruiz-Arroyo, and Carlos Matheus-Chacin. 2021. “Development and Feasibility Analysis of an Assistance System for High School Students with Dyslexia.” Research in Developmental Disabilities 111: 103892. doi: 10.1016/j.ridd.2021.103892. Rominger, Christian, Ilona Papousek, Corinna M. Perchtold, Mathias Benedek, Elisabeth M. Weiss, Bernhard Weber, Andreas R. Schwerdtfeger, Marina T. W. Eglmaier, and Andreas Fink. 2020. “Functional Coupling of Brain Networks During Creative Idea Generation and Elaboration in the Figural Domain.” NeuroImage 207: 116395. doi: 10.1016/j.neuroimage.2019.116395. Sousa, David A. 2015. Brain-Friendly Assessments: What They Are and How to Use Them. West Palm Beach, FL: Learning Sciences Iternational. Sousa, David A. 2016. Engaging the Rewired Brain. West Palm Beach, FL: Learning Sciences International. Sparrow, Betsy, Jenny Liu, and Daniel M. Wegner. 2011. “Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips.” Science 333: 776– 778. doi: 10.1126/science.1207745. Starr, Ariel, Melissa E. Libertus, and Elizabeth M. Brannon. 2013. “Number Sense in Infancy Predicts Mathematical Abilities in Childhood.” Proceedings of the National Academy of Sciences 110(45): 18116–18120. doi: 10.1073/pnas.1302751110. Szymanski, Caroline, Ana Pesquita, Allison A. Brennan, Dionysios Perdikis, James T. Enns, Timothy R. Brick, Viktor Müller, and Ulman Lindenberger. 2017. “Teams on the Same Wavelength Perform Better: Inter-Brain Phase Synchronization Constitutes a Neural Substrate for Social Facilitation.” NeuroImage 152: 425–436. doi: 10.1016/j. neuroimage.2017.03.013. Taylor, Rebecca D., Eva Oberle, Joseph A. Durlak, and Roger P. Weissberg. 2017. “Promoting Positive Youth Development Through School-Based Social and Emotional Learning Interventions: A Meta-Analysis of Follow-Up Effects.” Child Development 88(4): 1156–1171. doi: 10.1111/cdev.12864. Toth, Michael D., and David A. Sousa. 2019. The Power of Student Teams: Achieving Social, Emotional, and Cognitive Learning in Every Classroom Through Academic Teaming. West Palm Beach, FL: Learning Sciences International.

Insights from Brain Research on Teaching and Learning 51 Urban, Noelia, and Francois Guillemot. 2014. “Neurogenesis in the Embryonic and Adult Brain: Same Regulators, Different Roles.” Frontiers in Cellular Neuroscience 8: 396. doi: 10.3389/fncel.2014.00396. Vukovic, Rose K., and Nonie K. Lesaux. 2013. “The Language of Mathematics: Investigating the Ways Language Counts for Children’s Mathematical Development.” Journal of Experimental Child Psycholog y 115(2): 227–244. doi: 10.1016/j.jecp.2013.02.002. Wandell, Brian A., and Rosemary K. Le. 2017. “Diagnosing the Neural Circuitry of Reading.” Neuron 96(2): 298–311. doi: 10.1016/j.neuron.2017.08.007. Yeo, Darren J., Eric D. Wilkey, and Gavin R. Price. 2017. “The Search for the Number Form Area: A Functional Neuroimaging Meta-analysis.” Neuroscience & Biobehavioral Reviews 78: 145–160. doi: 10.1016/j.neubiorev.2017.04.027. Zivan, Michal, Sapir Bar, Xiang Jing, John Hutton, Rola Farah, and Tzipi HorowitzKraus. 2019. “Screen-Exposure and Altered Brain Activation Related to Attention in Preschool Children: An EEG Study.” Trends in Neuroscience and Education 17: 100117. doi: 10.1016/j.tine.2019.100117.

Part II DRIVING FORCES ON THE DEMAND SIDE

Chapter 4 WHICH SKILLS DO EMPLOYERS WANT? A CASE STUDY IN A TRANSITION ECONOMY Aydin Fenerli

Introduction Employers seek certain characteristics when hiring new employees. These characteristics are “signaled1” by candidates to employers via their credentials, and most of these credentials are based on learning, any kind of learning. In 2019, a research project on the mismatch between labor supply and demand in the manufacturing industry was conducted in Konya, a major province in central Turkey (Fenerli, 2019). This study investigated which characteristics employers prioritize when hiring and, next, which were the causes of this prioritization. The results are that employers were seeking nontechnical skills and even personal traits and attitudes as much as technical skills; there were even cases where non-technical skills were considered more important than technical skills. Technical or job-related skills, such as technical literacy (e.g., the ability to read technical drawings and guidelines) and the ability to work with tools and equipment, were expected to be present in hiring decisions. Employers were eager to invest in candidates by providing on-the-job training for technical skills, however, only if the candidate had the desired non-technical skills and personal traits. Desired non-technical skills were: ● ● ● ●

communication skills, openness to teamwork, problem-solving skills and a sense of responsibility as an indication of eagerness to take initiative when required.

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Expected personal traits and attitudes are honesty, loyalty, trustworthiness, high work ethic and discipline. As employers wanted to decrease employee training costs while maximizing the returns on their investment in human capital, they made efforts to retain their employees. The personal characteristics gave an indication of engagement in the job and the company. These skills, traits and attitudes were estimated by credentials such as education and training, previous work experience or references from informal networks, all strongly related to learning. This chapter will discuss the characteristics that employers are looking for today, how these characteristics are communicated to employers, and eventually, in a shifting working and learning environment, which signals will be relevant for employers.

Why a Case Study in a Transition Economy? The case study elaborated in this chapter, explores characteristics of, and reasons for, the mismatch between labor supply and industry demand in a transition economy. The study focused on the manufacturing sector and took the employers’ perspective. The research was conducted in 2019, utilizing in-depth interviews with employers and professional organizations in the Konya Province. Konya Province is an economy in transition, moving from agriculture to manufacturing and services. In the period examined (2008–17), economic growth increased the demand for labor. However, this was not matched by stronger labor force participation—as one would expect. This effect was not present in other provinces of Turkey. The research question therefore was: “How can the lack of increase in labor participation be explained by employers, despite the growing economy since 2008?”

In the sub-questions of the study, characteristics of unmet labor demand were investigated with specific attention to employee characteristics and employer expectations. The discussion in the study is structured on the demand side (employer), the supply side (employee) and their interactions. The study concluded that the lack of employment was due to: ● ●

●

inadequacy in remuneration and non-tangible aspects of compensation. quantitative and qualitative shortage of labor supply, in part by low women participation, extended schooling of young people and lowvalue attribution to vocational education and manufacturing sector in association. skill mismatch.

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Employers found it difficult to increase the value of jobs which made them invest in workforce training and increase investment in automation. Employers invested only in candidates with certain non-technical skills and personal traits by providing on-the-job-trainings (Fenerli, 2019). The experience of the economic transformation of Konya is relevant for developing countries that have similar transformations in their emerging industrial regions. Still, at first glance, it may look different from the global transformation of work as experienced today. Mismatch in labor markets and the underlying reasons for it are relevant in explaining how attitudes of employers are shaped during transitions.

What Do Employers Want? What employers want from employees depends on very diverse factors, such as type of work, required skills levels, sector and position of the company in the global value chain. All employers naturally want to maximize the return on their investment in labor. In our manufacturing sector, technical skills were strongly desired but scarce. But certain non-technical skills are also in short supply (Handel, 2003). The desired non-technical skills highlighted in Handel’s work are: ● ● ● ●

advanced cognitive/intellectual skills, problem-solving skills, interpersonal (soft) skills, attitudes and work ethic, effort, diligence, commitment, sense of responsibility and respect for authority.

Contrary to technical skills, the quality of non-technical skills is difficult to measure. In addition, the costs of developing non-technical skills and the temporal sub-economic performance of a new employee are also hard to estimate. Thurow argues that employers try to find suitable workers who require lower training costs (Thurow, 1975, pp. 88–91). This conclusion is supported by the findings in Konya, where employers were seeking non-technical skills and personal traits in candidates as listed above (Fenerli, 2019, pp. 89–92). The requested personal traits were perceived by employers as signs of attachment; employees that would engage with the work and company; so that they would stay enough for paying back the costs for training and create additional value to the company. Engagement is more than just performance; it can be also associated with a degree of attachment to the company and coworkers. These characteristics

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were most desired by employers as employee turnover is costly—loss of productivity and know-how. Therefore, employers want to engage and retain workers. These motivations are not unique to the employers in the case study. Universally, employers want to attract the best talent with a right fit with the company and the job, while showing engagement for high performance and best work outcomes. They would like to retain these employees.

What is Signaling? Spence claims hiring is an investment decision under uncertainty (Spence, 1973). In a labor market with imperfect information, employers are not sure which worker will fit in the job. They do not know the quality of a potential employee before or just after hiring; the future contribution of the candidate to the company is still unknown. They interpret signals from potential employees to decide whom to employ. They take their chances, which Spence resembles with a lottery. In time, they find out the value of their investment— or not. Signaling Theory, as proposed by Michael Spence, refers to the information exchange between parties by sending signals regarding traits or quality of themselves, either company, organization, or person. The signaling in job markets refers to indirect communication between employer and a potential employee regarding potential employee’s attributes, his productivity and future contribution to company (Spence, 1973; Spence, 2002, pp. 434–59). Employers make hiring decisions based on their interpretation of these signals. Education is the most important signal offered by the candidates (Spence, 1973). By investing in education and a diploma, a credential is obtained that signals for the unobservable quality of the candidate. This credential has been used by employers for a long time (see Chapter 6).

What Are Education Signals for? Education is standardized worldwide through degrees. Thus, a diploma is accepted as a credential for the skills that a candidate acquired during school. This credential is stronger if it is from a prestigious school, putting the candidate above many competitors in the job market. This competition, conceptualized by Thurow as the labor queue, is among workers who try to get better jobs with their background characteristics. Candidates with better credentials get the highest-ranking jobs, as they are perceived less costly to train by employers. In this competition, education has a broader meaning than proof of technical skills or ranking of a candidate according to the prestige of the

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degree or institution. In most parts of the world, education is still provided in the form of Industrial Education (see Chapter 1), despite the rise of alternative or hybrid models. According to Thurow, industrial education gives students a credential for “industrial discipline.” Through education, workers demonstrate abilities to “show up on time, take orders, do unpleasant tasks and observe certain norms of group behavior” (Thurow, 1975, pp. 96–98). Industrial discipline can be more important than job-related technical skills; it may not be provided through training. When education fails to provide industry discipline, it is time-consuming and costly for employers to add it on. In the case of Konya, with its economy in transition, the supply of an educated and qualified workforce w ith i ndustrial d iscipline proved to b e d ifficult. In the interviews, one of the human resources managers said: “Workers usually come to an industrial organization for the first t ime, t hey a re n ot familiar with it. It takes two to three years of work for them to acquire the right attitudes. We win the worker afterwards unless they go somewhere else.” (Fenerli, 2019, pp. 89–92). This illustrates the possibility of acquiring industrial discipline through on-the-job training. However, it is costly and unfavorable for the employer to provide this kind of training. If there are other candidates with experience in industry or who signal their industry discipline acquired during education, an employer would prefer those candidates. In addition to industrial discipline, other signals of education are meaningful for employers and their meaning is beyond technical skills. Education also signals the ability to learn. Even if the education would not include any job-relevant training, it would still be useful as a measurement of an individual’s trainability. According to Thurow, one learns how to learn and be trained through education (Thurow, 1975, pp. 96–98). If a candidate is trained in one skill, the marginal cost of providing a similar skill will be lower. If the candidate for a manufacturing job had an education in vocational school this would be considered a strong signal because the skills learned there are relevant to work, and other work-specific skills could be taught easily.

What Does Work Experience Signal for? The signal of education fails to explain skills gained and lost throughout the career. A job market has a dynamic structure; linear careers in a single company have ceased to exist. A worker has several jobs throughout their career; with job-specific, on-the-job trainings in each of them; they gain many skills. Meanwhile, some skills that have not been used for a while, may get lost. In some cases, those skills might not even be relevant anymore as new technologies, processes and techniques have been adopted.

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Previous work experiences are also utilized as signals in hiring decisions rather than just education. These are also credentials for learning; in each job the worker is assumed to take on-the-job training to gain additional skills. These investments are usually done by the employer and employee together. The employer bears low productivity for a while and makes an investment for on-the-job training while the employee accepts lower compensation in the first years of the career. Later, with tenure, most workers increase their compensation. In these cases, previous jobs signal for General and Specific Training2 are bought to a new employer. Waldman also argues that job assignments in previous firms are used as a signal for ones’ abilities (Waldman, 1984). On the other hand, as Waldman also acknowledged, there is a risk with previous assignments, as they may not reflect the full potential of the worker. If the assignments were inadequate in matching skills of the worker, or if existing jobs had a lower quality, they would devalue one’s abilities. This is often ignored in the job market. Becker distinguishes General from Specific Training. General Training is the basic training that provides skills to candidates for future jobs (Becker, 1964, pp. 33–51). General Training might be obtained in schools through vocational education, but it is usually only obtained in the early years of the career. This type of training is relevant for an entire career. General Training happens simultaneously with Specific Training, the type of training that is required by the company, and that might not be relevant for future jobs. For example, the ability to read recipes or technical drawings could be obtained by general training. On the other hand, the ability to use the interface of an inventory management system of a company is a specific skill that might not be useful in a future job, as another company will use a different system. The investment in General Training is purchased by the new employer. Previous jobs with signals of General Training and obtained abilities are key credentials for employers to invest in an employee. There is evidence that experience and on-the job learning are more efficient for learning and effective as a signal. Becker argues that entering directly into the labor force is a cheaper and more efficient wa y fo r te enagers to le arn wo rk-related sk ills (Becker, 1964, s. 17–21). Thurow argues that on-the-job training is efficient as a worker will learn essentials but will not learn the knowledge that will not be utilized (Thurow, 1975). On-the job training could be effective provided that basic skills are provided by either education or previous on-the job training. Especially with the high employee turnover in the Konya Case, an employer stated: “A worker with previous experience in another factory knows only their practices and way of working”, referring to the need for re-training each time worker changes jobs. To minimize training cost, training in a company

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focuses on Specific Training in the short term. Unfortunately, the short durations of jobs prevent workers from accumulating experience and gaining skills (Fenerli, 2019, pp. 62–64).

Which Signals Will Be Relevant for Employers in a Shifting Working and Learning Environment? The Morning Brew e-mail newsletter summarizes future-of-work-trends. “The biggest ‘future of work’ trends are: co-working, remote working, and not working.” (Morning Brew, 2019). Work was already in a transformation and with the worldwide Covid-19 outbreak, this is accelerated from 2020 onwards, and the trends mentioned above have become more prevailing. Career patterns were affected even before Covid. Careers are more nonlinear now. Workers pursue multiple jobs with shorter durations, and even different careers within a lifespan. Work is more flexible than ever, with the workplace, working hours and contract types changing.3 There is a demand for highly skilled employees, but less so for mid-skill jobs and even lesser for low-skill candidates. Competition for a job is denser in the mid and low-skill market and many low-skill employees are being pushed to unemployment and whence to unemployable status. The transformation of work is not happening on its own; it is going hand in hand with the transformation of learning. As workers have multiple jobs and careers, lifelong learning opportunities help them to adapt and learn new skills. With the increasing flexibility of work, learning also becomes more flexible with various options except for traditional schools or other types of institutionalized learning. There are many other supervised or selflearning opportunities such as online courses and programs, social networks and online communities, different types of online learning materials (videos, blogs, podcasts and presentations) and many other opportunities that can be accessed online. There are two major questions arising from these trends in the transformation of work and learning. The first is: what will employers be seeking in employees? The second is: what will employees be signaling as an indication of their abilities in a highly competitive job market? For the first question, there is no easy answer. In the previous, more rigid, industrial work setting, industry discipline was crucial. In the new, more flexible, post-industrial work setting, it is unclear to what extent it will be relevant. Still, it could be expected that some other form of discipline, with fewer industrial elements, would be required. The Konya Case employers criticized the absence of industrial discipline in employees with agriculture sector backgrounds such as difficulties

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in obeying norms of the workplace as absenteeism, respecting work hours or workplace rules (Fenerli, 2019, pp. 84–85). Such discipline is critical for a highly organized industrial firm. Failure to transform the workforce resulted in low productivity and high training costs for employers. Coming to the second question, education has been the most useful signal for giving hints about one’s potential value. It would be too ambitious to say education will not be relevant. It still would be an important signal in indicating one’s ability to learn. However, with eased access to information, and shortened expiration dates for knowledge, it will not be as useful as it was. Developing non-technical skills takes more time and maybe more challenging to build in on-the-job trainings as these focus on more job-specific elements. Thus, education could still have a vital role in developing non-technical skills, if it is structured in a way to help students learning to learn, making them resilient for changing circumstances and transferring non-technical skills that could be utilized in multiple careers in one’s lifespan. With these, education would still be the primary instrument and would keep its importance as a signal. The second important signal, the previous work experience, could be challenged as well. The learning with each job and previous job assignments to the employee were perceived as an indication of skills in an area. With nonlinear career patterns and shortened durations of a job, previous assignments might also be less relevant. However, they would still be valued by employers as a credential. Previous employers are usually assumed to develop an understanding regarding the skills of an employee. Thus, if a candidate is promoted to higher position in a previous job, this is usually interpreted as a demonstration of high performance, and this would still be considered by the new employer. Keeping these two, conventionally most important signals, in mind, there are new signals which might not replace, but add to the credentials of education and previous experience. A few of these signals could be conceptualized as credentials by training, credentials by doing, and visibility in social networks. The first one, credentials by training, are proof of achievement by completing a training or taking a certification process such as an exam. These could be provided by the existing employer or third-party organizations. In fact, this kind of credentials is not completely new; people were taking courses and receiving certificates before. However, with distance learning and the availability of online courses, it is easier and cheaper to access structured learning material. Most companies in high and mid-ends of the labor market provide access to in-house or third-party learning platforms to employees. In addition, the availability of open, low-cost training increased tremendously. These learning platforms provide digital badges or certificates that are visible

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and verifiable. Certificates and badges have a high potential to serve as a supporting signal for one’s abilities. The second one, credentials by doing, refers to visible work products of candidate, which could be used for a portfolio that would signal for one’s abilities. For instance, projects or works done by a candidate (like group or individual projects at school, in online communities or platforms, through maker movements and voluntary work or charity in a non-governmental organization) provide visible credentials, such as a verified achievement or an item in the portfolio of the candidate, which helps to build a story in explaining abilities to employers. The third one, visibility in social networks, especially in professional networks or knowledge-sharing platforms, helps to signal one’s engagement with the subject. This visibility could be provided by sharing other types of signals as well, certificates and achievements, completed individual or workrelated projects, creating and sharing content on expertise or interest areas in social platforms or online communities and knowledge sharing platforms. The visibility helps a candidate to build a personal brand, or at least helps to associate the candidate with a specific field of expertise. All these signals indicate the ability of self-learning, a type of learning that not necessarily require an instructor of institutional setting. Although learning materials are provided by public or private institutions, the learner is more independent in using and interpreting the materials. Moreover, skillbuilding, and personal development could also be supported by bilateral interactions such as coaching or mentoring, either organized by an institution or developed in an informal relationship. New or modified signals need not be limited to the ones mentioned here. A handicap of these new or modified credentials could be their relevance for different segments of workers. For highly skilled workers, or to some extent mid-skill, they could be very useful in competing for jobs. However, for low-skilled workers, these might not have same level of relevance. Proofs of previous trainings or individual achievements are valuable for each type of job. However, as observed in the Konya Case, when the value of a job is low and compensation does not differentiate by skill level, employees are not encouraged for skill development and credential building. Especially with low job security and shortened durations in a job at the bottom of labor market, employees may constantly stay in the unqualified status. As one of the employers noted, “unqualification became a qualification” (Fenerli, 2019, pp. 62–64). Still, these additional credentials are gaining popularity. They would not replace traditional signals, but they can have a strong impact in supporting the candidate as an additional signal. If two junior candidates with a similar

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educational background are competing for a job, the employer would prioritize the one who has been involved in a relevant school project, or the one who demonstrates a portfolio with relevant work. School projects would give an indication of participation in teamwork, while a portfolio in an area would signal for genuine interest in the subject. These would signal better value for the company in terms of engagement, or at least the employer would expect to spend less for training the candidate.

Conclusion Signaling was conceptualized to understand how the market is tackling information as an asymmetry problem in hiring. There are still information asymmetries leading mismatches and inefficiencies in the labor market; employers have limited information about the candidates they consider hiring. They used to utilize education as a primary signal, and then previous experiences of the candidate as they reflect on-the-job trainings and abilities gained. However, these signals are more diversified today. Education and job experiences are still important but in a post-industrial work setting with a shifting learning environment signals of credentials for training, doing and visibility are also being considered. Industrial discipline is being challenged as the work setting is in transition from an industrial setting to a more distributed, post-industrial setting. Together with that, self-learning is challenging traditional learning in an institutional setting. With eased access to information, technical skills are easier to gain, provided that the person has basic skills and skills for self-learning. However non-technical skills remain important as they are harder to learn. The motivation of employers in hiring hardly changes. They want to get best value from their investment in human resources. They want to keep training costs low, and attract employees who would engage with the work and stay in the company. These discussions could be relevant for the entire labor market, but its relevance varies per segment. There is a large low-skill labor group, and it is growing. As this group is competing for low-value jobs with low job security, the relevance of diversified signals and the motivation of low-skill candidates to invest in additional credentials requires further studies.

References Becker, G. S. (1964). Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education. Chicago and London: University of Chicago Press. Fenerli, A. (2019). Mismatch of Labor Force and Industry In Konya: Analysis of Structural and Individual Factors From Employer’s Perspective. Ankara: Middle East Technical University.

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Handel, M. J. (2003). Skills Mismatch in the Labor Market. Annual Review of Sociolog y, 29(1), 135–165. Morning Brew. (2019, September 21). Workers Take the Day Off to Protest Climate Change. New York: Morning Brew Inc. Spence, M. (1973, August). Job Market Signaling. The Quarterly Journal of Economics, 87(3), 281–306. Spence, M. (2002, June). Signaling in Retrospect and the Informational Structure of Markets. The American Economic Review, 92(3), 434–459. Thurow, L. C. (1975). Generating Inequality. London and Basingstoke: The Macmillan Press Ltd. Waldman, M. (1984). Job Assignments, Signalling and Efficiancy. The RAND Journal of Economics, 15(2), 255–267.

Chapter 5 SOCIAL AND EMOTIONAL LEARNING— THE LESSONS FROM NEUROSCIENCE Hagar Goldberg

Introduction For centuries schools were focused on the cognitive aspect of learning, while ignoring (or actively suppressing) its social aspect (see Classic and Industrial Learning in this book’s introduction). Accordingly, in the traditional classroom setting, students were separated in individual desks, and were expected to “passively absorb” knowledge from the teacher. From the mid-twentieth century neuroscience research has become an important front in understanding and unravelling the underlying mechanisms of learning processes. Recent educational-neuroscience research suggests an integrative view of learning, as a cognitive, emotional and social experience (Diamond, 2016; Immordino-yang, 2017; Immordino-yang & Darling-hammond, 2018; Schonert-reichl et al., 2015; Sira & Mateer, 2014). Furthermore, it implies that a learning experience consist of both individual and social-collective aspects. On the one hand, it holds an intimacy of subjective processing and private associations, that differ f rom o ne i ndividual to another. At the same time human learning is a social process and highly influenced b y c ultural, s ocial c ontext a nd e motional i nformation (Immordino-yang & Darling-hammond, 2018). Brain science has confirmed what antient societies k new by intuition; learning is not a one-way process and students are not empty containers in which teachers pour their knowledge. In addition, as the world becomes more global and more virtual, there is a growing need and challenge for people to connect. As mankind’s goals (in politics, science and technology) become more complex and ambitious, the need for effective social interactions and cooperation is stronger than ever. Students’ social competences are predictive to future success and well-being

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as adults in their relationships and as professionals (Taylor et al., 2017). Moreover, intervention studies suggest that unlike previous conceptions, these social competences are not fixed traits, but malleable skills that can be developed through education (Durlak et al., 2011). These findings have supported a paradigm shift in education toward a more holistic, learner-centered approach. Following the rise of a new educational field named SEL in the 1990s (Elias et al., 1997), the theory of SEL has become more accessible and actionable. As this theory was supported by research, and translated into practical teaching methods, school systems have started redesigning learning environments and pedagogy to be more social, interactive and inclusive. In this chapter, two important layers of social learning; content and process (learning about and learning through social skills), will be discussed. First, the neuro-mechanisms of learning will be described to shed light on the fundamental social nature of learning. Then, to understand how these biological principles translate to education, some key educational concepts will be explored through the lens of brain science and social learning, together with practical advice for educators. Finally, the evidence-based impact of social learning on students’ performance and well-being will be summarized.

PART I THE NEURO-MECHANISMS AND SOCIAL NATURE OF LEARNING

Learning Is Social by Nature because the Human Brain Is Social by Design As a social species, human survival and thrive is heavily based on social structures and behaviors: community, collaboration and caregiving. On the individual level social skills play a critical role in human Survival of the fittest (the Darwinian rule of evolution), and therefore the human brain is programed to connect and group with others. Human babies are highly dependent on human connection and at least one adult caregiver for their survival. Moreover, the quality of early caregiver–infant interactions, and interpersonal experiences are major factors in trajectory of brain development (Feldman, Braun, & Champagne, 2019). The special focus of the human brain on social information is evident from day one. The newborn’s brain is evolutionarily hard-wired to actively seek social interaction (Newman, Sivaratnam, & Komiti, 2015). Lack of social interaction (e.g., exclusion and neglect) or intense negative social stimuli (e.g., violence and abuse) were shown to significantly compromise the development

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of the social-mammal brain (Boyce, Sokolowski, & Robinson, 2020; Gapp et al., 2016; Gee et al., 2013; Tottenham & Sheridan, 2010). Creating social and emotional safety is therefore a necessary prerequisite for creating an effective learning environment for students (Immordino-yang, 2017; Immordino-yang & Darling-hammond, 2018), as neurobiologically there is a tradeoff between learning and survival mode. Taking this together, relationships are important factor in shaping deprived or enriched learning environments.

Learning and Brain Development: Education Is Key in Shaping Human Brains and Educators Are Professional Brain-Builders Meaningful learning is expressed in changes in brain networks and their connectivity to represent the new knowledge in a process known as Neuroplasticity. Fire together, wire together—the famous Hebbian law of neuroplasticity (Hebb, 1949, 2005), suggests that neural pathways are reinforced by strengthening specific synapses, as a result of repetition of the same stimulus (Kass & Jain, 2000; Mundkur, 2005). The concept of neuroplasticity ties together experience-based neuronal changes and learning; our learning experiences (both formal and informal) shape our brains (through neuronal growth and network formation) in a process called experience-dependent plasticity. The human brain is especially plastic and malleable during the developmental phase, typically in the first two and a half decades of life (Huttenlocher & Dabholkar, 1997; Kass & Jain, 2000). An enriched environment for enhanced neuroplasticity, offers sensorymotor and cognitive stimulation, opportunities for exploration and novelty and secured relationships (Kolb & Gibb, 2011; Petrosini et al., 2009). Lack of these conditions may slow down, or decrease, the level of neuroplasticity in the developing brain. Executive functions (EF), a set of higher-order cognitive processes that support control of attention, emotions, thoughts and actions (Wiebe & Karbach, 2018; Stichter et al., 2016), is a critical construct for learning. EF performance is not fixed but malleable and can be enhanced through environmental influences, challenges, and experience. The developmental potential of EF is critical in educational terms, especially given that EF is a strong predictor to academic success and to student’s self-esteem (Diamond, 2016). EF rely heavily on the Prefrontal Cortex (PFC) which develops gradually during childhood and adolescence (Moriguchi & Hiraki, 2013; Thompson-Schill, Ramscar, & Chrysikou, 2009). School years are therefore a precious opportunity to leverage

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the neuroplasticity in the PFC for training and enhancing students’ EF. School climate, particularly teacher support for learning and the way that instructional practices are designed, including curriculum, teaching expectations and student evaluation, have shown to impact students’ EF (Piccolo, Merz, & Noble, 2019). Moreover, studies highlight the importance of social, emotional and physical health for cognitive health as stress, loneliness, lack of sleep or exercise each impair EFs (Diamond, 2013). The teacher has an important role in unlocking the growth potential of students in their developmental stage. Creating learning environment of both (cognitive and physical) challenge and (emotional) safety can boost neuroplasticity and learning in the developing brain. But managing such delicate tension between challenge and safety is not trivial. A common question today among educators is about the duality of stress in learning motivation and the fine line between pushing students too much and not pushing them enough. As educators our goal is to work within the zone of learning and neuroplasticity and avoiding overwhelming stress on one hand and underwhelming comfort on the other.

Stress, Motivation and the Zone of Proximal Neuroplasticity The space in which learning happens, or as suggested by Vygotsky, the Zone of Proximal Development (ZPD), is the zone in between the student’s actual developmental level (indicated by what they currently can do by themselves) and their potential development (indicated by what they can do with the guidance of an educator or more capable peers, Vygotsky, 1980). This revolutionary view half a century ago fits perfectly with contemporary neuroscience of learning and the concept of neuroplasticity. It speaks to the dynamic, experience-based and social-dependent nature of learning and development (Gauvain, 2020; Säljö, 2010). It also speaks to the importance of personalizing learning and care per learner (Morgan, 2014). In terms of stress and motivation, we can use the classic model of ZPD and the newer concept of neuroplasticity to draw a dynamic relationship between stress, motivation and learning (see Figure 5.1). Meaningful learning is about stretching and expanding one’s capabilities, so if students are deeply and consistently in their comfort zone it holds them back from learning and growing. Nevertheless, overstretching may break the bough, so we want to keep students (and staff for that matter) out of the toxic stress zone as much as possible. Learning happens with support, in the stretch zone, which is different and ever changing for every individual. Learning is, therefore, not a place of destination, but rather a dynamic process of expanding.

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Stress and Learning Stress responses should be differentiated based on their amplitude, duration and available coping resources. Mild and brief elevation in stress hormones (e.g., before taking an exam) is beneficial for learning and performance (as mentioned with relation to the proximal zone of development). A chronic stress response (for example when dealing with difficult life events or adversity) can be tolerable, and even valuable for building resilience, but without intervention and social support (process known as social buffering), the wear and tear on the brain can be disruptive for its functioning, let alone learning.

When I’m Surviving, I’m not Learning In response to chronic stress most of the brain resources are directed to survive the threat. The PFC (prefrontal cortex), which is critical for EFs and learning, has been “put on hold”, while the limbic system (the antient and intuitive “mammalian brain”) takes over. The stressed brain, therefore, has a narrowed and fixed perception due to PFC shutdown, and is over-focused on threat and survival due to an unregulated limbic system. These neural changes fostering fight–flight–freeze responses, limit behavioral control and increase the likelihood of aggressive defense (Teicher et al., 2002). The more students experience uncontrolled stress, the more their brains practice survival mode activation and build the networks to support this state-of-mind (without intervention this may become their default). Between survival and growth, survival naturally takes precedence, and so, students in survival mode may be disengaged, avoid academic tasks and challenges. This can perpetuate a vicious cycle as stress is a barrier to academic and social success, and the pressures caused by failing induce even more stress, as the student feels rejected and incapable of meeting the expectations and succeeding in a (traditional) classroom environment. The antidote to toxic stress is supportive social connection and secure relationships. Social buffering is the powerful effect of social connection in mitigating the stress response, both physiologically and psychologically (Gust et al., 1994; Hennessy, Kaiser, & Sachser, 2009; Kikusui, Winslow, & Mori, 2006; Uchino, 2006).

When I’m Alone I’m Surviving, Not Learning What threatens our students and shift their brains from learning to survival mode? Some stressors are mostly physical (e.g., low nutrition, low hygiene,

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lack of shelter or lack of sleep), and some are psychological by nature (e.g., feeling estrangement, unseen, excluded or bullied). Both types effectively activate the brain stress response, but while physical health is usually visible, psychological safety is more covert and harder to detect in the classroom. Social isolation stressors (SIS) are a major trigger for activating the brain stress response in humans and non-human social animals (Beery & Kaufer, 2015; Mumtaz et al., 2018; Rivera et al., 2020; Smith & Vale, 2006). For humans, stress from threats to emotional safety hampers cognitive performance in the short term, and has been connected to premature aging of the brain and body in the longer term (Immordino-yang & Darling-hammond, 2018). Institutional rearing is an extreme example for a deprived environment, and a source of SIS. Early exposure to institutionalization has been associated with compromised neural development, and a variety of cognitive and emotional disfunctions such as lower IQ , language deficits, poor executive functions, poor social-emotional functions and a high prevalence of mental health problems (Nelson et al., 2007; Nelson, Kendall, & Shields, 2013; Rutter & Sonuga-Barke, 2010; Sheridan et al., 2012). Interestingly, children that were moved to a foster care early on have demonstrated a greater improvement (developmental catch up) (Sheridan et al., 2012; Vanderwert et al., 2010). This means that social connection is crucial for healthy development, and in case of adversity timely intervention increases the repair potential. Education systems should proactively detect and intervene in cases of students experiencing major stressors at school or outside of school. While educators could not always solve or eliminate the source of the stress, they can and should provide students social buffering, that life jacket they need to push through and make it a tolerable rather than a toxic storm.

When I’m Supported, I can Stretch and Expand Humans have an innate dependence on social connection and acceptance for both survival and thrive. This social sensitivity can cause massive stress response as a reaction to social threat, may it be isolation or abuse. Nevertheless, the same social sensitivity is an important key to unlock our students’ potential to grow and thrive. Given the social support the human brain has immense capability to change, repair and adapt. When feeling supported students tend to take on more challenge, more risks and stretch themselves more in their learning. (LePine, LePine, & Jackson, 2004; Walker, 2010). The way stress is contextualized to the students (challenge vs hindrance stress) impact their approach and performance in challenging learning tasks.

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The Transition in the Educator Role Understanding the social impact on human brain development and learning, calls for a paradigm shift in the roll of schools and teachers. For some students, school is the first and only place in which they can take off their armor and feel safe. These students, who are at highest risk of developing behavioral disorders and emotion regulation difficulties, ar e us ually those who challenge teachers’ emotion regulation the most, but they also have the greatest need for a supportive relationship with their teacher ( Jennings & Greenberg, 2009a). A regulated, sensitive and consistent response by a teacher to their challenging behaviors may stir their developmental trajectory for the better (especially in the early grade) and have a long-term positive effect on them (Lynch & Cicchetti, 1992). This is of course much easier said than done. In the stressful and highly demanding reality of a typical classroom, many teachers struggle with their own social and emotional regulation (many lack the schooling of SEL themselves); they find the new expectation to teach and model SEL to students overwhelming and threatening their professional identity. It is important to take these new expectations and trends in education in context. Public education is transitioning between industrial era to information and technology era. The schooling objectives have changed from a mass-production-oriented unified e ducation t o a s pecialization-directed personalized education. Students today grow through technological revolution and rapid cultural change into virtual communication and social media which identify their generation and separate them from the generation of their parents and teachers. While technology introduces many new possibilities, it is also linked to rising anxiety, depression, loneliness and cyber bullying (Landoll et al., 2015; O’Keeffe & Clarke-Pearson, 2011; Woods & Scott, 2016). The era of information and scientific revolution calls for changes in roles and goals of educational organizations. With the endless accesibility to online knowledge teachers are no longer the only or even the main knowledge source. Given the rapid growth of technology use and atomization processes, education should invest more than ever in human skills that cannot be delegated to algorithms (and might be of high demand in the future). In the words of London School of Economics director, Minouch Shafik, “In the past jobs were about muscles, now they’re about brains, but in the future they’ll be about the heart.” Students are called to be competitively creative, empathetic, and emotionally skilled in order to succeed in future markets. Accordingly, educators are much more than academic instructors; they are also role models, emotional mentors and social leaders that change lives forever.

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The understanding of the pivotal part educators play in their students life trajectory (way beyond school years) can be very empowering. At the same time, the enormace responsibility that comes with this realisation can be overwhelming. Here it might be helpful considering the teacher’s social brain, as principles that were discussed earlier in this chapter about the social brain with regard to learning, well being and performance, apply also to the teachers. Under chronic stress teachers will gradualy become less productive, less regulated and less atuned to their students ( Jennings & Greenberg, 2009b; Peeters et al., 2014). Without support and social buffer to alleviate the stress the teacher’s brain also shifts to survival mode where its access to creative and rational thinking is diminished. Social buffering by a supported, prosocial school culture may protect teachers from the ongoing stress of their work ( Jennings & Greenberg, 2009b). In summary, education is a social craft, and the educator is a key player in shaping students’  well-being, motivation, self-perceptions and academic performance (Davis, 2003; Immordino-Yang, Darling-Hammond, & Krone, 2018; Jussim & Harber, 2005). On a broader, systemic level, pedagogy that priorities and practice empathy, inclusiveness and psychological safety have shown positive results in students’ behavior, self-esteem, motivation and academic success (Immordino-yang, 2017; Jennings & Greenberg, 2009a; Okonofua, Paunesku, & Walton, 2016). Considering the science of development and learning, it is clear why the social and emotional context in a classroom is so critical for learning and growth. As education is a hands-on art, the million-dollar question for many educators is, of course, how do we translate this knowledge to beneficial practice in the classroom? How do we effectively teach and implement SEL? Especially as most of the teachers today were never schooled on the subject themselves. Creating a prosocial, inclusive, and safe learning environment is rooted in the everyday dynamics (big and small), between the teacher and the students. The social values, assumptions, expectations and working models demonstrated to the class by the teachers will scaffold the social culture in the classroom and create the space for meaningful social and emotional learning to happen. PART II. SOCIAL DYNAMICS IN THE CLASSROOM—TRANSLATING THE SCIENCE TO EDUCATIONAL PRACTICES

Levering the Social Brain in the Classroom The evolutionary pressure on the human brain to master social information and manage large groups dynamics have boosted the human neocortex in

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size and complexity, and resulted in the development of a large-scale cortical network that is dedicated to detect and process social information; mentalizing, and sorting others’ thoughts, feelings and motives (Lieberman, 2012a; Morelli, Lieberman, & Zaki, 2015). Furthermore, an intriguing overlap between the social network and the default mode network (activated whenever our brain is not occupied with an external task), suggests that the socialcognitive processing might have become, over the course of evolution, the default background processing of the human brain (Lieberman, 2012b; Mars et al., 2012; Yeshurun, Nguyen, & Hasson, 2021). This makes evolutionary sense when survival, status and thrive depends so much on effective s ocial interactions, and exclusion is a death-sentence. Considering the magnitude of social processing in the human brain, harnessing social interaction in the classroom for learning is much more promising than suppressing it. Attaching social context to the learning content or process, has a positive effect on memory encoding and recall (Sousa, 2011). This social encoding advantage is attributed to the social mentalizing system aforementioned, that is recruited in addition to traditional memory regions in such learning processes (Harvey, Fossati, & Lepage, 2007; Mitchell, Macrae, & Banaji, 2004). Some learning subjects are more social and story-based by nature (e.g., history and literature), while other (e.g., STEM fields) lack a meaningful social context. However, learning content is just one way to include social engagement. Another way to induce social learning is by process design and facilitation, such as group projects and peer teaching. The research point at many advantages to cooperative learning groups in math (Batton, 2010; Fullilove & Treisman, 1990; Hooker, 2011; Sailor, 2017; Smith, McKenna, & Hines, 2014; Springer, Stanne, & Donovan, 1999), such as better performance, higher motivation, better sense of competence and less math anxiety compare to solo learning. Furthermore, unlike the common practice of ability-based-grouping (i.e., performance level in math), the research suggests that group diversity is more beneficial in math studies, as students are exposed to multiple perspectives and have more opportunities to try different strategies and roles in their interactions (Boaler, 2008; Boaler, Wiliam, & Brown, 2000). Importantly, leveraging the group wisdom and diversity does not happen spontaneously and is subject to the group dynamics and ability to work collaboratively (Webb, 1991; Webb et al., 2013). It is therefore the teacher’s responsibility to facilitate a synergistic process in which students are developing and expanding both STEM and social competencies. Every teacher knows by experience the old Latin principle Docendo discimus—“the best way to learn is to teach.” Learning-for-teaching (learning with the social motivation to teach another person) as opposed to learning for a

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test, is one way to apply the social encoding and recall advantage in learning. Interestingly, social motivation by itself may engage the mentalizing system during the encoding of new information (even if the content is not social), and activate the social encoding advantage (Falk & Lieberman, n.d.; Lieberman, 2012a). Learning for self has been found less effective that learning-for-teaching, but most effective i s learning a nd a ctually t eaching (Annis, 1983). Peer tutoring (one student teaching another student) is therefore the next level in harnessing social motivation to boost the encoding of non-social information into memory. Beyond memory improvement peer tutoring has many further benefits to both tutor and learner, in academic achievements (Allen & Feldman, 1973; Rohrbeck et al., 2003), motivation and ownership over the learning process and deep conceptual understanding of the material (Topping, 1996). Considering the immense energy that the human brain invests in social processing, and especially during adolescence, it is recommended replacing the old educational approach of divide and conquer with a prosocial approach of unite and build. While peer interaction in the classroom is sometime a source of distraction, it is also a powerful learning resource when used intentionally and wisely.

The Teacher–Students Social Dynamics— The Dance of Learning Education is relational, it is what happens between a teacher and students. However, like parenting, this is not a symmetric relationship, and the educator is the one responsible for the process. The teacher is a role model not just on the academic level, but also on the social-emotional level and teachers have significant impact on the social culture in the classroom and on students’ self and peer perception. Students watch their teachers closely to learn and form social norms and values. They watch how their teachers lead the class, how they manage conflicts, how they handle and communicate their emotions. Students take cues from their teachers in determining whether a peer is likable or not, and the teacher approach toward students have a buffering effect on peers’ social preference (even in case of aggressive students; Hughes, Cavell, & Willson, 2001). In addition, teachers’ mindsets about discipline and relationships have an impact on their students. Specifically, teachers’ willingness to take an empathic rather than a punitive mindset and sustain positive relationships while encouraging better behavior, have resulted with decreased suspension rates, higher respect for teachers and higher motivation to behave well in class. Importantly, research has indicated that teachers’ approaches and mindset can be changed through scalable intervention (Okonofua et al., 2016). These findings highlight once again the power teachers have in shaping students minds on the most basic aspects; how they show up in the world

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and in relationships. Even more so these findings demonstrate the malleability of teachers’ mindset which is key in enhancing education on a systemic level. Educating teachers about the plastic and social brain, while supporting teachers with how to leverage neuroplasticity and social context in the classroom, can be empowering for both teachers and students, and drive a sustainable, broad and long-term change in education. The teacher–students relationship is bidirectional, and teachers’ motivation and well-being are highly affected by the social context in their classrooms. Socially and emotionally competent teachers show effective classroom management, healthy teacher–student relationships, effective SEL program implementation, and less burnout in their job ( Jennings & Greenberg, 2009b). These components are also interconnected, as according to research teachers who hold better relationships with their students face fewer behavioral problems, less stress and enjoy a smoother class management experience and higher satisfaction in their job (Marzano, Marzano, & Pickering, 2003). Furthermore, it has been shown that teachers’ perceptions of their students are linked with teachers’ experiences of stress, teaching efficacy, and job satisfaction (Collie, Shapka, & Perry, 2012). Building constructive dynamics in the classrooms begins with the teacher’s mindset and expectations regarding themselves, the process and the students.

Teachers’ Expectations According to research, teachers’ expectations and beliefs about their students influence t he s tudents’ i ntellectual d evelopment a nd t herefore m ay come to serve as a self-fulfilling p rophecy ( Alvidrez & W einstein, 1 999; Jussim, Eccles, & Madon, 1996; McKown & Weinstein, 2008; Rosenthal & Jacobson, 1968; Rubie-Davies et al., 2015). The interplay between teachers’ expectations and students’ performance works as a positive feedback loop that intensifies predispositions. (1) Teachers form expectations of students (based on social information and students’ performance in class), (2) teachers communicate (explicitly and implicitly) their expectations with the students, (3) students internalize the expectations and (4) students behave according to what is expected of them ( Johnston, Wildy, & Shand, 2019) (see Figure 5.2). Teachers are humans, and so they are subject to all the classic human biases when forming their expectations and assessments of students (Annis, 1983; Johnston et al., 2019). Social status (Namrata, 2011; Tobisch & Dresel, 2017), ethnicity (E. R. Peterson, Rubie-Davies, Osborne, & Sibley, 2016), and gender (Auwarter & Aruguete, 2008) are all factors. Teachers hold higher expectations for students who share their demographic/ethnic

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background (Gershenson, Holt, & Papageorge, 2016) or socioeconomic status (SES) ( Jamil, Larsen, & Hamre, 2018).  Moreover, teachers’ expectations for students from disadvantaged circumstances and communities tend to be lower than average (McKown & Weinstein, 2008; Rubie-Davies, Hattie, & Hamilton, 2006). These findings imply a v icious cycle in which teachers’ expectations might reinforce pre-existing narratives and prejudice which give advantage to the privileged and penalize the disenfranchised. Importantly, racial bias effect on teachers’ expectations could be mitigated through teachers’ education and awareness (Liou & Rojas, 2016; López, 2017). Perhaps the seed for educational equity is with teachers’ mindset and expectations, through which inherited circumstances could be changed, and new narratives can rise.

Growth Mindset Teachers’ influence on students could be utilized to support students new and positives self-beliefs and learning models. Growth Mindset, the belief that intelligence is not fixed and can be developed is vastly supported by behavioral and neuroscience research (Dweck, 2008, 2016). Holding a growth mindset has a positive impact on academic success, emotional well-being and motivation, while reducing racial, gender and social class achievement gaps (Blackwell, Trzesniewski, & Dweck, 2007; Broda et al., 2018; Claro, Paunesku, & Dweck, 2016; Rattan et al., 2015; Stephens, Hamedani, & Destin, 2014; Walton & Cohen, 2011; Zeng, Hou, & Peng, 2016). This idea fits nicely with the neuroscience of learning which is rooted in neuroplasticity—the functional and structural neuronal changes in response to learning experiences and repeated training. In fact, teaching students about neuroplasticity and how brain development and learning potential are dynamic and contextual have been shown to effectively reinforce a growth mindset (Sarrasin et al., 2018). The science-based concept of experience-dependent development (of brain and intelligence) is a paradigm shift in education; from “we work with what we have” (a belief that intelligence is given and fixed) to “we have what we work on” (an understanding that intelligence and talent are a result of nurturing and work). This is a very hopeful and empowering message for both educators and students, as it highlights the great importance of intentional and personalized education in the process of development and fulfilment. It also reframes the power balance between teachers and students, as they share responsibility and ownership on the learning goals and work as a team to achieve them (to change your brain YOU need to do the work). This openended approach also highlights the importance of a flexible, creative and

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inclusive education system to support every learner in the best way that suits them. Beyond the individual level, a growth mindset may support social equity and mobility through education, with a special significance for students atrisk, due to learning disabilities, racial, mental or socioeconomic reasons (Claro et al., 2016; Sisk et al., 2018; Yeager et al., 2019). Interestingly, the individual mindset is affected by the environmental social norms and both teachers and peers are influential. O n t he c lassroom l evel, a c orrelation between a highly growth-oriented class culture and higher levels of academic achievement has been demonstrated (Bostwick et al., 2020). In a recent study Yeager and colleagues explored whether teacher’s mindset affects students’ mindset using a large sample of over nine thousand North American highschool students and over two hundred math teachers. They found that teaching growth mindset alone is not enough to create change for students and pointed at an interaction effect between teaching growth mindset (intervention) and providing supportive context (teacher’s own growth mindsets). The authors concluded that for students to internalize the growth mindset, they need to experience it in practice and confirm that “it works.” The authors described the growth mindset intervention as the seed and the supportive context provided by the teachers as the fertile soil in which this concept can grow deep roots (Yeager et al., 2021). The psychological affordance of the social context, means that the intervention is delivered in a context which afford the way of thinking offered by the intervention (Walton & Yeager, 2020).

Recognizing the Blooming Potential in a Dormant Tree As mentioned earlier, the Proximal Zone of Development (PZD) is the space where student’s growth take place, between where the student is currently at, and where (or how far) the student can reach with support. To work with this framework, teachers are required to be imaginative and envision potential outcomes that are not yet there. Holding the best possible image of every student is not easy and requires perspective taking (putting oneself in other’s shoes), optimism and emotional generosity. If that is not enough, teachers need to curve many different paths as every student is different. With some students more than others visioning the growth path is a serious imaginative leap, but it is especially crucial that educators hold the space to grow for these students and show them the path. We can think of a teacher as a gardener, planting seeds and young saplings, imagining, and holding their growth potential. As the gardeners observes and studies the different plants, they construct and adjust the garden to include different species together and provide each one the best conditions to grow and flourish.

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Social Dynamics in the Classroom—What Can Teachers and School Leaders Do? SEL starts with the teacher and with school culture. The formula that emerges from the research for effective SEL implementation in schools is: Knowledge + Contextual Support (to apply the knowledge) + (opportunities to) Practice = Internalization and Skill Development (both in the individual and organization levels). 1. Knowledge: Leadership and social skills, what once was considered a soft skill, has become a cornerstone in progressive education. Due to shifts in economic and educational goals and accumulating evidence on the neuroscience of learning, it has become critical that teachers will be thoroughly and explicitly educated in SEL, as well as social psychology and neuroscience. Knowledge and skills should be shared, and teachers should be assessed and rewarded for excelling in those domains. 2. Contextual support (walk the talk): Unlike classic teaching subjects (e.g., math and history) SEL also represent a value system. To effectively implement SEL beyond a theoretic idea, the cultural context must reflect those values and support them across all levels of the education system. On the individual level the student and teacher should work as a team to define the student’s personal zone of proximal development (identify goals and passions, strengths and stretches). Both building a collective classroom culture, and a personalized learning path for each student are based on trusting relationship between the teacher and the students. 3. Practice: School leaders and teachers should be intentional and proactive about SEL practice and building a prosocial, inclusive learning culture. Opportunities should be created to practice specific tools and develop the social and emotional skills. For example, teachers can teach their students about the plastic brain and growth mindset to build constructive expectations and beliefs regarding the learning process. In addition to such preplanned and prompt intervention, the more the teacher is skilled in SEL, opportunities will be taken as they emerge naturally (e.g., solving conflict in real time, modeling emotion regulation and supportive group dynamics). PART III: THE IMPACT OF TEACHING SOCIAL SKILLS ON STUDENT’S SUCCESS AND WELL-BEING

The Contribution of Social and Emotional Competencies to Academic Success SEL involves five major emotional, cognitive a nd behavioral competencies: self-awareness, social awareness, responsible decision making, self-management and relationship management (Zins, 2004; CASEL).

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The intentional infusion of social and emotional skills is aimed at promoting the integration of emotion, cognition, communication and behavior (Crick & Dodge, 1994; Durlak et al., 2011; Lemerise & Arsenio, 2000). A common concern of teachers is that the existing workload to support the academic objectives leave no time for SEL on top of that. However, the research clearly suggests that SEL and academics is not an either/or but a both/and relationship, as they work in synergy. A massive meta-analysis of 213 school-based, universal SEL programs in North America examined the effects of these program on over 270 thousand students across the entire school age and diversity range (kindergarten through high school; Durlak et al., 2011). Overall, the findings indicated that SEL programs yielded significant positive effects on ta rgeted so cialemotional competencies such as emotions recognition, stress-management, empathy, problem solving, and decision-making skills. SEL programs were also associated with positive attitudes about self, others, and school. Importantly, the SEL enhanced students’ behavioral and social flexibility in solving problems more effectively, and improved academic performance on achievement tests and grades (Durlak et al., 2011). The contribution of social and emotional competencies to academic success is explained in the literature in several way. Some of the impact is attributed to self-development on the individual level that is translated into self-confidence and motivation, neuronal development of the Prefrontal Cortex (PFC) and Executive Functions (EFs) that serve academic performance. Some of the impact is attributed to the relational aspects of learning that were discussed in this chapter; positive relationships, psychological safety, prosocial school culture, and norms of high expectations and support for academic success (Durlak et al., 2011). In terms of program design and effectiveness the meta-analysis revealed that programs that follow the SAFE practices (sequenced, active, focused and explicit), were most successful (Durlak et al., 2011). The recommendation is a step-by-step design, that is focused on students active participance, with sufficient time and attention and explicit goals and instructions. A second meta-analysis examined the follow-up outcomes (collected 6 months to 18 years postintervention), based on 82 school-based, universal SEL interventions involving over 97 thousand kindergarten to high school students (Taylor et al., 2017). The results pointed at a clear connection between SEL enhancement and positive youth development. Specifically, participants in SEL programs exceeded significantly in social-emotional skills, attitudes and well-being compared to students who did not learn SEL at school. In fact, social-emotional skill development post-intervention was the strongest predictor of well-being at follow-up. Importantly, positive outcomes were blind of students’ race, socioeconomic background or school location. Analysis of a

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subsample examined the long-term effects of SEL school intervention later in adulthood, and found significant long-term benefits on social relationships quality, increased high school graduation rates and college attendance, reduced arrests and reduced prevalence of clinical disorders (Taylor et al., 2017). These finding demonstrate how impactful SEL can be for students’ developmental trajectories and lives way beyond their school years.

Figure 5.1  The learning zones.

Figure 5.2  The learning cycle.

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on motivation, achievement, and brain activity: A meta-analysis. Trends in Neuroscience and Education, 12(July), 22–31. doi: 10.1016/j.tine.2018.07.003 Schonert-reichl, K. A., Oberle, E., Lawlor, M. S., Abbott, D., Thomson, K., Oberlander, T. F., & Diamond, A. (2015). Enhancing cognitive and social – Emotional development through a simple-to-administer mindfulness-based school program for elementary school children : A randomized controlled trial. Developmental Psycholog y, 51(1), 52–66. Sheridan, M. A., Fox, N. A., Zeanah, C. H., McLaughlin, K. A., & Nelson, C. A. (2012). Variation in neural development as a result of exposure to institutionalization early in childhood. Proceedings of the National Academy of Sciences of the United States of America, 109(32), 12927–12932. doi: 10.1073/pnas.1200041109 Sira, C. S., & Mateer, C. A. (2014). Executive function. In Encyclopedia of the Neurological Sciences edited by Robert Daroff, Michael Aminoff. (pp. 239–242), Amsterdam, Netherlands: Elsevier. Sisk, V. F., Burgoyne, A. P., Sun, J., Butler, J. L., & Macnamara, B. N. (2018). To what extent and under which circumstances are growth mind-sets important to academic achievement? Two meta-analyses. Psychological Science, 29(4), 549–571. Smith, S. M., & Vale, W. W. (2006). The role of the hypothalamic-pituitary-adrenal axis in neuroendocrine responses to stress. Dialogues in Clinical Neuroscience, 8(4), 383. Smith, T. J., McKenna, C. M., & Hines, E. (2014). Association of group learning with mathematics achievement and mathematics attitude among eighth-grade students in the US. Learning Environments Research, 17(2), 229–241. Sousa, D. A. (2011). Educational neuroscience. Educational Neuroscience, 5(1), 1–181. doi: 10.4135/9781483387734 Springer, L., Stanne, M. E., & Donovan, S. S. (1999). Effects of small-group learning on undergraduates in science, mathematics, engineering, and technology: A metaanalysis. Review of Educational Research, 69(1), 21–51. Stephens, N. M., Hamedani, M. G., & Destin, M. (2014). Closing the social-class achievement gap: A difference-education intervention improves first-generation students’ academic performance and all students’ college transition. Psychological Science, 25(4), 943–953. Stichter, J. P., Christ, S. E., Herzog, M. J., O’Donnell, R. M., & O’Connor, K. V. (2016). Exploring the role of executive functioning measures for social competence research. Assessment for Effective Intervention, 41(4), 243–254. doi: 10.1177/1534508416644179 Taylor, R. D., Oberle, E., Durlak, J. A., & Weissberg, R. P. (2017). Promoting positive youth development through school-based social and emotional learning interventions: A meta-analysis of follow-up effects. Child Development, 88(4), 1156–1171. Teicher, M. H., Andersen, S. L., Polcari, A., Anderson, C. M., & Navalta, C. P. (2002). Developmental neurobiology of childhood stress and trauma. Psychiatric Clinics of North America, 25, 397–426. Thompson-Schill, S. L., Ramscar, M., & Chrysikou, E. G. (2009). Cognition without control: When a little frontal lobe goes a long way. Current Directions in Psychological Science, 18(5), 259–263. doi: 10.1111/j.1467-8721.2009.01648.x Tobisch, A., & Dresel, M. (2017). Negatively or positively biased? Dependencies of teachers’ judgments and expectations based on students’ ethnic and social backgrounds. Social Psycholog y of Education, 20(4), 731–752. Topping, K. J. (1996). The effectiveness of peer tutoring in further and higher education: A typology and review of the literature. Higher Education, 32(3), 321–345.

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Tottenham, N., and M. A. Sheridan. 2010. “A Review of Adversity, the Amygdala and the Hippocampus: A Consideration of Developmental Timing.” Frontiers in Human Neuroscience 3: 1–18. doi:10.3389/neuro.09.068.2009. Uchino, B. N. (2006). Social support and health: A review of physiological processes potentially underlying links to disease outcomes. Journal of Behavioral Medicine, 29(4), 377–387. Vanderwert, R. E., Marshall, P. J., Nelson III, C. A., Zeanah, C. H., & Fox, N. A. (2010). Timing of intervention affects brain electrical activity in children exposed to severe psychosocial neglect. PLoS One, 5(7), e11415. Vygotsky, L. S. (1980). Mind in Society: The Development of Higher Psychological Processes. Massachusetts, US: Harvard University Press. Walker, R. A. (2010). Sociocultural Issues in Motivation (Vol. 6). Amsterdam: Elsevier. Walton, G. M., & Cohen, G. L. (2011). A brief social-belonging intervention improves academic and health outcomes of minority students. Science, 331(6023), 1447–1451. Walton, G. M., & Yeager, D. S. (2020). Seed and soil: Psychological affordances in contexts help to explain where wise interventions succeed or fail. Current Directions in Psychological Science, 29(3), 219–226. doi: 10.1177/0963721420904453 Webb, N. M. (1991). Task-related verbal interaction and mathematics learning in small groups. Journal for Research in Mathematics Education, 22(5), 366–389. Webb, N. M., Ing, M., Kersting, N., & Nemer, K. M. (2013). Help seeking in cooperative learning groups. In Help Seeking in Academic Settings (pp. 56–99). New York, US: Routledge. Wiebe, S. A. and Karbach, J. (Ed.). (2018). Executive Function Development Across the Life Span New York, US: Routledge. Woods, H. C., & Scott, H. (2016). # Sleepyteens : Social media use in adolescence is associated with poor sleep quality, anxiety, depression and low. Journal of Adolescence, 51, 41–49. doi: 10.1016/j.adolescence.2016.05.008. Yeager, D. S., J. M. Carroll, and J. Buontempo, et al. 2022. “Teacher Mindsets Help Explain Where a Growth-Mindset Intervention Does and Doesn’t Work.” Psychological Science 33(1): 18–32. doi:10.1177/09567976211028984. Yeager, D. S., Hanselman, P., Walton, G. M., Murray, J. S., Crosnoe, R., Muller, C., … Hinojosa, C. P. (2019). A national experiment reveals where a growth mindset improves achievement. Nature, 573(7774), 364–369. Yeshurun, Y., M. Nguyen, and U. Hasson. 2021. “The Default Mode Network: Where the Idiosyncratic Self Meets the Shared Social World.” Nature Reviews Neuroscience 22: 181–192. doi:10.1038/s41583-020-00420-w. Zeng, G., Hou, H., & Peng, K. (2016). Effect of growth mindset on school engagement and psychological well-being of chinese primary and middle school students: The mediating role of resilience. Frontiers in Psycholog y, 7, 1873. Zins, J. E. (2004). Building Academic Success on Social and Emotional Learning: What Does the Research Say? Newyork, US: Teachers College Press.

Part III DRIVING FORCES ON THE SUPPLY SIDE

Chapter 6 HABITS OF MIND: NEW INSIGHTS INTO TEACHING AND LEARNING Arthur Costa, Bena Kallick and Allison Zmuda

Introduction The future challenges us with complex and conflicting models of what to value, what to believe, how to make decisions and how to live productively (Lieberman, 2021). Numerous international futurists, neuroscientists, educators and sociologists advocate for problem solving, creating, innovating and communicating to sustain our global society. The needs they list are dispositions that are necessary to lend oneself to learning (Costa & Kallick, 2016). This chapter makes the case for dispositional learning as most needed for learning, working, and citizenship for living productively in the new era.

What are Dispositions? In this chapter, the term, dispositions, refers to thinking dispositions—tendencies toward particular patterns of intellectual behavior. Perkins, Jay and Tishman (1993) propose three psychological components which must be present to initiate dispositional behavior: (1) sensitivity—the perception of the appropriateness of a particular behavior; (2) inclination—the felt impetus toward a behavior and (3) ability—the basic capacity to follow through with the behavior. For example, someone who is genuinely disposed to seek balanced reasons in an argument is (1) sensitive to occasions to do so (for instance while reading a newspaper editorial); (2) feels moved, or inclined, to do so; and (3) has the basic ability to follow through with the behavior—they identify the pro and con reasons for both sides of an argument.

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16 Habits of Mind The Habits of Mind is a set of thinking dispositions at the core of social, emotional and cognitive behaviors. These habits were derived from studies of successful, efficacious problem-solvers from many walks of life. While there may be more, Costa and Kallick (2008) described 16 Habits of Mind that can serve as a starting point for further elaboration and description. Each of the habits end with -ing verbs to signal that growth is a life-long journey rather than something that can be mastered. While there is no order of importance, each of the habits are numbered for easier reference.​ Habit 1. Persisting

Do your students ever give up in despair when a task proves to be difficult or when the answer to a problem is not immediately found? Do they say, “I can’t do this,” or “it’s too hard?” Do they sometimes write down any answer just to get a task over with or find themselves easily distracted from a task rather than sticking with it? These are typical problems that we all experience from time to time; however, you can be in control of those behaviors if you want to and if you have

Figure 6.1  The icons and descriptors by the Institute for Habits of Mind. Reproduced with permission. All rights reserved.

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the strategies to do so! Persisting means persevering in a task through to completion and looking for ways to reach your goal when stuck. People who persist apply strategies to stick with it, such as: ●

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Breaking the problem apart into steps and accomplishing each step that leads to the final outcome. Reviewing the ground rules, directions, or criteria for success: Finding something missed along the way or assuming understanding and discovering the misunderstanding. Seeking assistance and input from others: Sometimes others may have had experience with similar problems or can see a different array of solutions. Using self-talk: Tell yourself to hang in and stick with the task. Envisioning what it might look like and feel like to be successful.

Habit 2. Managing Impulsivity

Do your students ever blurt out whatever comes to mind rather than thinking about the most plausible answer? Do they ever find themselves regretting what they said thinking, “I really shouldn’t have said that? It isn’t what I really meant.” Do they ever jump to do some work before they read the directions? Managing impulsivity means thinking before acting: remaining calm, thoughtful and deliberate when working through a problem or developing an idea. Strategies that help us become more intentional include: ●

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Focusing on our breathing to settle down. Finding other strategies to help keep our emotions under control. Rewinding the situation to examine it with a more deliberate eye. Reflecting on what, where and why this is happening that is causing our feelings. Considering your options: Thinking about what actions could be taken and what advantages or disadvantages might happen as a result. Reframing possibilities: Believing that we can change the way we react when others “push our buttons,” seeing it as an opportunity to learn about ourselves, finding new ways to control our emotions.

Habit 3. Listening with Understanding and Empathy

Do your students struggle to devote mental energ y to another person’s thoughts and ideas, putting their own values and judgments aside? Instead of listening intently, do they carry on an internal dialogue rehearsing what they are going to say next when their partner stops talking?

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Listening fully is a complex skill requiring the ability to monitor one’s own thoughts while simultaneously attending to the words and feelings of others. You listen not only for what someone knows, but also for what emotions they are experiencing through their facial expressions, body language, voice intonation and eye movements. These strategies may slow your mind so that you can hear beneath the words to their meaning: ●

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Use the listening sequence to better understand their thinking. ● Pause: Wait for a few moments. Has the other person really finished? Sometimes waiting is the most helpful thing to do. In that quiet space they may clarify or reframe their point of view, solution or idea. ● Paraphrase: Summarize what you heard them say. A brief explanation that represents what was told to you. This is not the time to add your thinking, inferences or new ideas. ● Probe: Ask questions to seek clarity and precision of their point of view, solution, or idea. A few probing questions are: ○ Why do you think that is the case? ○ What’s another way you might …? ○ What did you mean by …? Empathize by listening and observing for feelings ● “You’re angry … because she called you a bad name. Name-calling hurts … ” “… and you want to be treated with respect.” ● “You’re overjoyed … because you got a high score on the test,” “you really cooled it …”  “…and you want earn a high grade in  that course.”

Habit 4. Thinking Flexibly

Are your students fixed in one way of looking at a problem? Do they stop thinking and just say, “my mind is made up, don’t confuse me with more facts.” Do they have difficulty in considering alternative points of view? Thinking flexibly is part attitude (openness to a new idea) and part action (knowing how and when to consider using new ideas and information). Flexible thinkers are open to additional information or reasoning, even if it challenges existing beliefs. They know what they know and see the need to open themselves to other options and alternatives to consider. Working with people from different cultures and who represent diverse perspectives they recognize the distinctness of other’s ways of experiencing and making meaning. They draw upon a repertoire of problem-solving strategies and practice style flexibility, knowing when it is appropriate

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to think broadly and globally and when a situation requires detailed precision. Practice thinking flexibly by asking yourself such questions as: ● ●

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In what other ways might I think about this? What’s another perspective? What else might I try when I get stuck? How does stepping back and looking at the big picture (the whole) open my eyes to new ideas? When and why should I change my thinking and my actions?

Habit 5. Metacognition (Thinking about Your Thinking)

Do your students ever get distracted and drift off task? Do they realize that they have made a careless error? Do they ever lose their place and have to begin again? Metacognition is our ability to know what we know and don’t know. Becoming more metacognitive requires inner awareness and greater selfobservance. It is our ability to plan a strategy for producing what information is needed, consciousness of our steps and strategies when problem solving, and reflective on and evaluative of the productiveness of our own thinking. Planning a strategy before embarking on a course of action assists in keeping track of the steps in the sequence of planned behavior at the conscious awareness level for the duration of the activity. It facilitates making temporal and comparative judgments, assessing the readiness for more or different activities, and monitoring our interpretations, perceptions, decisions, and behaviors. Invite your students to become more aware of their own thinking and to monitor their plans of action by asking themselves such questions as: ● ●

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How am I thinking about this? What kind of thinking will be called for in this situation? (e.g., Analyzing? Comparing? Creating?) How effective is the strategy that I am using? What changes might be needed? Did my efforts succeed? What could I have done differently?

Habit 6. Striving for Accuracy

Do your students ever turn in sloppy, incomplete, or uncorrected work? Are they more anxious to get rid of the assignment than to check it over for accuracy and precision? Striving for accuracy and precision suggests that you are committed to producing the best that you can, that you are seeking truth, and are open to

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feedback. People who strive for accuracy work to attain the highest possible standards and pursue ongoing learning in order to bring a laser-like focus of energies to task accomplishment. They take time to check over and refine their products, review the rules or constraints they have to follow, and apply criteria of excellence to guide their path to quality work. As you examine your work, consider the following strategies: ●

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Check your work with a colleague or peer: Seeing or hearing your work from the lens of another helps to know what changes are needed for clarity of meaning. Study the criteria and related descriptors that explain what quality looks like: If you are unclear about the explanation or need clarification, seek additional guidance so that you can re-calibrate as needed. Give yourself time to step back: Fast-approaching deadlines or wanting to get rid of a task may limit your striving for accuracy and precision, accuracy and fidelity.

Habit 7. Questioning and Posing Problems

Are your students curious and inquisitive? When they embark on a project or task, do they generate questions to guide their pursuits? If they do generate questions that lead them to the conclusions they seek or are their questions simple and closed-ended? Upon completion of the project do they raise further questions? One of the distinguishing characteristics between humans and other forms of life is our inclination and ability to find questions to investigate and problems to solve. Questioning and posing problems pushes us to think more deeply about the issue at hand. It requires having a questioning attitude, knowing what data are needed, and developing strategies to produce those data. Continuing to push your thinking using questions (e.g., Why does this problem exist and need solving? What is the real problem here? Am I getting to the root cause? What questions do we need to ask?) often leads to deeper and better questions that become more worthy of attention. The following 6-step sequence may help to generate deeper thinking: 1. Select a problem focus: some discrepancy, observation, community or global problem or project that attracts you and that stimulates your curiosity. 2. Generate questions: Pose as many questions as you can; do not stop to discuss, judge, or answer the questions; write down every question exactly as it was stated; and change any statements into questions. By listening

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to other students’ questions, you may generate even more and varied questions. Analyze your questions: Explore the differences between your questions by stating the intent of each question: What do you want to learn by asking that question? Categorize the list of questions you have just produced. Prioritize your questions: Choose the three questions that have the greatest possibility of yielding the information you desire or leading you into new territory. Decide on actions: Decide how to use the questions. What data will they yield? How might these data provide insights into resolving the problem deepening your understanding? Reflect on what you have learned: Review the steps of the process. What helped or hindered your understanding of question generating? What have you learned from this process? When else might you use this process?

Habit 8. Applying Past Knowledge to New Situations

Are you dismayed when you invite students to recall how they solved a similar problem previously and students don’t remember? When your students begin a task, even though it may be familiar, do they approach it as if they’ve never seen a problem like this before? It is as if each experience is encapsulated and has no relationship to what may have come before or with no relation to what follows. They seem unable to draw forth from one event and transfer it in another context. On the other hand, efficacious problem solvers learn from experience. When confronted with a new and perplexing problem they draw forth experience from their past. They may often say, “This reminds me of ...” or “This is just like the time when I ...” They explain what they are doing now in terms of analogies with or references to previous experiences. They call upon their store of knowledge and experience as sources of data to support theories to explain, or processes to solve each new challenge; they are able to abstract meaning from one experience, carry it forth, and apply it in a new and novel situation. Some strategies to new situations are: ●

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As you begin to learn something new, reflect on prior learning by asking such questions as: What do I already know? How does what I know apply here? What are some experiences that I relate this to? As you are learning, actively make connections by asking questions such as: What will be important ideas that I will take away? What can I do to remember the key ideas? After the learning experience is over, extend thinking by asking questions such as: How might I apply what I have learned in future situations or other subjects?

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Habit 9. Thinking and Communicating with Clarity and Precision

Do you hear your students (and other adults) using vague and imprecise language? They describe objects or events with words like weird, nice, or OK. They call specific objects using such non-descriptive words as stuff, junk and things. They punctuate sentences with meaningless interjections like ya know, er and uh. They use vague or general nouns and pronouns: “They told me to do it.” “Everybody has one.” “Teachers don’t understand me.” They use non-specific verbs: “Let’s do it.” and unqualified comparatives: “This soda is better; I like it more.” Language and thinking are closely entwined: enriching the complexity and specificity of language simultaneously produces more effective thinking. When people strive to communicate, they work to be accurate in both written and oral form by taking care to use precise language, defining terms, using correct names and universally understood labels and analogies. The following strategies may be helpful to keep in mind for developing thinking and communicating with clarity and precision: ●

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Mentally rehearse: Inside your head, practice what you are going to say before you say it. Engage your internal dialogue by asking questions and developing answers to help clarify and direct your skills as a speaker and listener. Avoid overgeneralizations, deletions and distortions: For example, “Everybody has one.” “They don’t understand me.” “I like it more.” Instead, support statements with explanations, comparisons, quantifications and evidence. Slow down when you are emotional: When you get angry or exasperated, your rational brain closes down and your emotional brain takes over. Take a deep breath and give yourself a chance to think before saying something. Become a spectator of others’ language as well as your own: Listen to the words chosen, choices made to elicit feeling/mood and details provided to support explanation or claim. Seek feedback from others to continue to improve the communication as well as craftsmanship. Checking for understanding and adjusting language, evidence, and tone demonstrates respect for the audience as well as keeping your purpose in mind.

Habit 10. Gathering Data through All Senses

Some students go through school and life oblivious to the textures, rhythms, patterns, sounds, and colors around them. Sometimes, children are afraid to touch, get their hands dirty or feel some object might be slimy or icky.

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They operate within a narrow range of sensory problem-solving strategies wanting only to “describe it but not illustrate or act it,” or “listen but not participate.” Where does curiosity come from? Remember when you were little and fascinated by small things such as weeds growing out of cracks in the sidewalk, worms burrowing their way through the dirt, or smells of the ocean? All information about the outside world gets into the brain through sensory pathways—sight, sound, taste, touch and smell. An apple, for instance, must be eaten to know its crispness and sweetness. To know a role in a play, it must be acted; to know the game, it must be played; to know a dance it must be moved. When you recall information from that experience, the brain reactivates or reconstructs the circuit in which it was stored. The more sensory modalities that were activated, the more triggers the brain has for reactivating the circuit. Those whose sensory pathways are more open, alert and acute often absorb more information from the environment. To experience the world through as many different senses, consider the following: ●

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Pay attention to the world around you. What am I noticing in my environment? What details capture my attention? Deliberately use your senses when you are trying to remember something. For example,

draw (or find) a picture that captures the idea. Act out a historical event to capture the feeling or mood. ●

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When engaging in a new topic or problem, ask yourself, “What sources of data should I consider? How is what I am experiencing impacting my thinking?” Take your students on sensory walks around the school and or community to experience the sights, sounds, smells in the environment. Have them keep a journal of what they experience. Pop popcorn. Invite students to describe their sights, smells, sounds and tastes.

Habit 11. Creating, Imagining, and Innovating

Do you ever hear your students say, “I can’t draw,” “I was never very good at art,” “I can’t sing a note,” “I’m not creative”? Some people believe creative humans are just born that way; it’s in their genes and chromosomes. However, all human beings have the capacity

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to generate original, clever or ingenious products, solutions and techniques—if that capacity is developed. Creative human beings try to conceive problem solutions differently, examining alternative possibilities from many angles. They project themselves into different roles using analogies, starting with a vision and working backward, imagining they are the objects being considered. Creative people take risks and push the boundaries of their perceived limits. They are intrinsically motivated, working on the task because of the aesthetic challenge rather than the material rewards.  Creative people are open to criticism offering their products for others to judge seeking feedback in an effort to refine their technique. They constantly strive for greater novelty, parsimony, simplicity, perfection, beauty, harmony and balance. Strategies to help: ●

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Go ahead, take a risk! When you try something and it doesn’t turn out the way you hoped, it isn’t a failure. Rather, it provides a rich opportunity to analyze what went wrong, to learn, and to generate alternative strategies. When you are less afraid to make mistakes, you open the environment for play and experiment. Think by using analogies. In what ways is a school like an airport? In what ways is soccer like a highway? In what ways is gravity like a feather? Answering such questions develops your creative capacities. You are realizing that, by comparing a main idea or topic you are working on and using a strange analogy, you may discover new and important attributes. We often assume we know something and then, when we make it strange, we discover a deeper understanding. Brainstorm absurd ideas. Albert Einstein once said, “If at first an idea doesn’t seem totally absurd there’s no hope for it.” Innovators move toward the absurd, the seemingly irrelevant, in order to create new insights rather than taking an obvious direction. Use divergent and convergent thinking in harmony with each other. When creating or innovating, seek a balance between when to converge on ideas by following rules or becoming precise and factual and at other times when you need to break away and use divergent thinking by generating new ideas. Become alert to situational cues—to know when to use which type of thinking. Don’t take yourself too seriously. Humor has been found to liberate creativity and provoke such higher-level thinking skills as anticipation, finding novel relationships, visual imagery and making analogies. Pose “What if … questions” to generate the thinking and imagination of children where the world is full of endless possibilities.

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Habit 12. Responding with Wonderment and Awe

Do your students avoid problems or are “turned off ” to learning? Do they make such comments as, “I was never good at these brain teasers,” or, “It’s boring.” “When am I ever going to use this stuff?” “Who cares?” or “I don’t do thinking!” Many people perceive thinking as hard work and therefore recoil from situations, which demand “too much” of it. However, have you ever witnessed a young child react to a magician’s sleight of hand, or exclaim in delight over the appearance of a rainbow in the sky and are thrilled at the sights and sounds of a fireworks display? When the world around us sparks our interest and ignites our sense of wonder, we are inspired to learn, to explore, to imagine possibilities. Efficacious people delight in making up problems and enigmas to solve for themselves and to submit to others. They enjoy figuring out perplexities and continue to learn throughout their lifetimes. Every thought and action are accompanied by emotions originating in the brain (the amygdala). Feel-good neurotransmitters (serotonin, endorphin and dopamine) are released whenever we experience such feelings as rapture, intrigue, amazement or fascination. Many of us never learn to tap into the source of our passions because we fail to discover what inspires us. Strategies to help provide experiences that trigger that sense of amazement and wonder: ●

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Use thinking routines (Project Zero) such as See, Think and Wonder; Options Explosion; or Peeling the Fruit to help students understand the power of shared thinking, collaboration and reflection and how it can spark interest and excitement. Explore new places. Take a walk outside, go to a museum, listen to music, watch a TED talk. Whether these are virtual or physical experiences, be open, to observe, explore and give yourself time to settle into the surroundings. Keep a notebook or journal. Make a list, draw, photograph or describe experiences or ideas that you have found to be delightful, magical or wonderous.

Habit 13. Taking Responsible Risks

Are your students reluctant to take risks? Some students hold back from games, new learning, and new friendships because their fear of failure is far greater than their experience of adventure. They are reinforced by the mental voice that says, “If you don’t try it, you won’t be wrong” or “if you try it and you are wrong, you will look stupid”. The other voice that might say, “if you don’t try it, you will never know” is trapped in fear and mistrust. Those

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students may be more interested in knowing whether their answer is correct, rather than being challenged by the process of finding the answer. They are unable to sustain a process of problem solving and finding the answer over time, and therefore avoid ambiguous situations. They have a need for certainty rather than an inclination for doubt. Risk-taking requires a leap into the unknown requiring a tolerance for ambiguity. People who are willing to take risks accept confusion, uncertainty and higher risks of failure as part of the normal process and they learn to view setbacks as interesting, challenging, and growth-producing. However, they are not just behaving impulsively. Their risks draw on past knowledge, are thoughtful about consequences, and have a well-trained sense of what is appropriate. It is only through repeated experiences that risk taking becomes educated. They learn that all risks are not worth taking. Encourage your students to: ●

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Develop the capacity to live with some uncertainty—to be challenged by the process of finding an answer rather than by avoiding what you don’t know. Be patient with themselves. Think about necessary resources they might need (e.g., time, feedback and conducive space) to sustain a process of problem solving, investigation or creation. Live on the edge of their competence. If you want to grow your brain, work on problems and ideas that are hard. Struggling and making mistakes provide the best opportunities for your brain to grow. Ideally this work is done in an environment where mistakes are openly analyzed to promote flexible thinking and perseverance.

Habit 14. Finding Humor

Do some of your students find h umor i n a ll t he “wrong p laces”—human d ifferences, ineptitude, injurious behavior, vulgarity, violence and profanity? Do they laugh at others yet are unable to laugh at themselves? Humor has been found to liberate creativity and provoke higher-level thinking skills such as anticipation, finding novel relationships, visual imagery and making analogies. People who engage in humor can see situations from a new vantage point or come up with the unexpected. Having a whimsical frame of mind, they thrive on finding incongruity and discontinuities; perceiving absurdities, ironies and satire; and are able to laugh at situations and at themselves. While they poke fun, they do so with a sensitivity to others’ feelings. They develop a heightened sensitivity to when humor will serve a purpose and

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when it is a distraction. Following are some strategies to increase student’s capacity to find humor: ●

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Retell or rewrite part of a story in a humorous way that was upsetting initially, but with a little time and perspective it no longer has that effect on you. Go hunting in joke books or online sites and ask yourself, What do I find funny? Topics, joke genres (e.g., observational, one-liners and knock-knock jokes) Appreciate the element of surprise. Whether you are finding humor or creating humor, every good joke disrupts expectations by changing the momentum of the story.

Habit 15. Thinking Interdependently

Do some of your students find difficulty working in groups? Perhaps they feel isolated, prefer their solitude, or think it is inefficient to work in a group. “They just don’t like me”. “I want to be alone.” Some students seem unable to contribute to group work being a “job hog.” “Leave me alone--It takes too much time to work together”. Human beings are social beings—we congregate in groups, find it therapeutic to be listened to, draw energy from one another and seek reciprocity. Thinking interdependently means knowing that we will benefit from participating in and contributing to ideas, inventions and problem solving. As people collaborate and remain open to others’ perspectives, their thinking can be enhanced by interchanges with others. Listening, consensus-seeking, giving up an idea to work with someone else’s, empathy, compassion, supporting group efforts and altruism … all are behaviors indicative of those who profit from thinking interdependently. Interdependent people envision the expanding capacities of the group and its members, and they value and draw on the resources of others to enhance their own personal competencies. Here are several questions for students to reflect upon as they work toward a common goal: ● ● ● ● ●

How might we agree on the quality of the work we produce? How can we pay attention to how well our group works together? How can I best contribute to this group? How am I affecting the group? How is the group affecting me? How can I help others feel more confident, powerful and satisfied?

Habit 16. Remaining Open to Continuous Learning

Do your students believe that learning means completing the assignment and moving on? Do you hear students saying, “our way is the only way” or “we’ve already done that?” From

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an early age, students have been trained to figure out the correct answer rather than developing capabilities for effective thinking. They have been taught to value certainty rather than doubt, to give answers rather than to inquire, to know which choice is correct rather than to explore alternatives. Remaining open to continuous learning is an essential characteristic of self-directed, continual, lifelong learners and should be nurtured both at home and in school. People who are inquisitive, thoughtful, and confident are open to searching for new or better ways to solve problems, understand ideas, and resolve tensions and uncertainties. That includes the humility of knowing what we don’t know, which is the highest form of thinking we will ever learn. Paradoxically, unless we start with humility, we will find it difficult to move forward. Self-directed, continuous learners actively gather and interpret feedback through self-observation by consciously monitoring their own feelings, attitudes and skills; inviting feedback from teachers, parents, and peers, and through interviews with others; and collecting evidence showing the effects of their own efforts. Data is then analyzed, interpreted and internalized as students modify actions in relation to their goals (Kallick & Zmuda, 2017). Encourage your students to: ●

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Have humility and pride when admitting you don’t know. Reframe this as a launch for exploration, curiosity and mystery rather than a limitation. Ask questions and seek connections. Deep learning is fueled by an inquisitive mind, developing capabilities for effective and thoughtful action. Continue to discover who you are and how you see the world. Ask questions, such as: What motivates me to keep learning? What do I still wonder about? How will I remain open to new ideas? Or new learning?

Habits of Mind Builds a School Culture Habits of Mind develops students’ capacities to recognize and apply their dispositional thinking when they are confronted with problematic situations by asking themselves, “What is the most intelligent thing I can do right now?” This question is not only valid for students, but also for the culture of entire school community Key factors that contribute to enhancing the school culture with the Habits of Mind include: ●

Common vocabulary: Whether talking to students, parents, administrators, members of the community or each other, the Habits of Mind provide a

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common language that anchors the school as a community of continuous learners. As students hear the language and link the terms with the observed behaviors of the adults, they soon realize that “it’s how we do things around here.” Social norming: The principal opens the faculty meeting with an agenda listing the problems and decisions that the staff must resolve and asks, “If these are the problems we face today, which Habits of Mind will serve us as we work together to respond to them?” Staff members generates a list, and, after the meeting, they reflect on which Habits of Mind served them as they approached each problem: ● “We will need to listen with understanding and empathy to each other’s points of view.” ● “We will need to persist with some of these problems that have been plaguing us for years.” ● “We will need to think flexibly and generate many ways of solving these problems.” ● “Let’s remember to find a little humor and laugh together!” Social norms are informal understandings that govern the behavior of members and can be thought of as rules that prescribe what people should and should not do given their social surroundings (Donohoo et al., 2018). De-privatized practice: Some school staffs work and silos. They seldom interact with staff from other departments. The foreign language teachers seldom talk with the technology teachers, and literature teachers rarely interact with the soccer coaches. In Habits of Mind schools however, staff members make their practice public because they share a common vision of their graduates. Each department finds ways of building the Habits of Mind into their curriculum. The dispositions are what is taught, valued and assessed on the journey towards mastery of their content (Louis et al., 1996). Attention density: As students progress through the elementary grades, or as they visit the various subject matters at the high school, there is a recurring focus on the Habits of Mind. Whether it be in social studies class, science lab or football field, the kindergarten or any other gradelevel classroom, their teachers invite them to focus on one or more of the Habits of Mind. Signals and posters throughout the school and classroom remind the staff and students about the importance of the Habits of Mind. Students are recognized with awards, badges or bracelets for their performance of one or more of the habits (Costa & Kallick, 2016, 51). These constant reminders, encounters and applications are what is known as attention density. The more students focus on and grapple with the Habits, the more those habits become internalized into their hearts, minds and behaviors.

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Shared indicators of growth: Because staff members share a common commitment to producing student growth in the Habits of Mind over time, they dialogue about indicators that they observe students performing as they progress through the school. They report to each other such indicators as: ● The student body president advocated for the use of listening with understanding and empathy to each other’s point of view as he conducted the student council meeting. ● At Kittredge School in San Francisco, eighth graders adopt learning buddies from the kindergarten and first grade. They read stories together and find examples of the Habits of Mind. ● John, a third grader, complained there was too much emphasis on the Habits of Mind. No matter where he turned, he said, they reappeared. Now John, as a sixth grader, advocated for responding with empathy in their school project of visiting homeless people to better understand their plight. Parents and community as partners. It is more likely that students will learn and practice the habits of mind if they witness their parents exhibiting of Habits of Mind themselves. In one secondary school, the students invited business leaders from the community to share and  to determine their necessity for the Habits of Mind in the jobs. Parents have reported that they employ the Habits of Mind in their work and share them with their colleagues. They describe how they need to listen, to pose questions, to think creatively and to persist in their jobs. Some examples of parent’s anecdotes include (Costa & Kallick, 2019, 186–187): ● “We talk about the Habits of Mind as a family, and I share them with friends because I find them to be pertinent/applicable in many situations. I love how the Habits of Mind reinforce and emphasize all of the characteristics that we value so highly.” ● “When the Giants won the World Series, Shane proudly said, ‘The Giants were definitely striving for accuracy when they won the World Series.’ And, after I made some cookies, I told Zachary he had to wait until after dinner to have one, to which he replied, ‘I am managing my impulsivity for sure!’”

In Summary The dispositions described in this chapter may well serve as the most essential, enduring, powerful and desirable attributes for the graduates of our educational system and workers in our business organizations. The dispositions we need to acquire or strengthen are learned primarily from

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being around people who exhibit them. They will need dispositions and learning skills far beyond current society requirements. Most of the jobs have, in fact, not been created yet. Indeed, our survival may depend on it.

References Costa, A. & Kallick, B. (2008) Learning and Leading with Habits of Mind: Sixteen characteristics of success. Alexandria, VA: ASCD. Costa, A. & Kallick, B. (2016) Dispositions: Reframing teaching and learning. Thousand Oaks, CA: Corwin. Costa, A. & Kallick, B. (2019) Nurturing Habits of Mind in Early Childhood: Success Stories from Classrooms around the World. Alexandria, VA: ASCD. Donohoo, J, Hattie, J. & Eells, R. (March 2018) The power of collective efficacy. Educational Leadership, 75(6), 40–44 Kallick, B. & Zmuda, A. (2017) Students at the Center: Personalizing Learning and Habits of Mind. Alexandria, VA: ASCD. Lieberman, M. (March 2, 2021) Top U.S. companies: These are the skills students need in a post-pandemic world.  Education Week. top-u​-s-co​mpani​es-th​ese-a​re-th​e-ski​l ls-s​ tuden​ts-ne​ed-in​-a-po​st-pa​ndemi​c-wor​ld/20​21/03​ Louis, K., Marks, H., and Kruse, S. (1996). Teacher’s professional community in restructuring schools. American Educational Research Journal, 33(4), 757–798. Perkins, D.N., Jay, E., & Tishman, S. (1993). Beyond abilities: A dispositional theory of thinking. Merrill-Palmer Quarterly: Journal of Developmental Psycholog y, 39(1), 1–21.

Chapter 7 BEYOND THE REACH OF TEACHING —DIFFERENTIATING THE ROLE OF PHENOMENOLOGICALLY ORIENTED VIGNETTES IN LEARNING AND TEACHING FROM PHENOMENON-BASED LEARNING Evi Agostini and Vasileios Symeonidis

Introduction: Discourses and the Nature of Learning What is learning? How does learning occur? How can we study learning? These are some fundamental questions about the nature of learning, a phenomenon that appears to be on everybody’s mind and on every agenda these days, even though little is known about the experience of learning itself. Education policymakers are increasingly talking about predefined “learning outcomes” and “flexible lifelong learners”, but the problem with the wider learnification of educational discourse is that questions about the content, purpose, and relationships of education are no longer asked, or they are taken for granted (Biesta, 2017). Gert Biesta makes the criticism that “the language of learning has eroded a meaningful understanding of teaching and the teacher” (Biesta, 2012, 36). The emergence of new learning theories and especially constructivism has also resulted in a shift from teaching to learning, placing students at the center of educational discourse and teachers on the outside, primarily in the role of mediators, facilitators or advisors.1 Unfortunately, concepts of learning that take into account the interrelationship between teaching and learning as well as between teachers and learners, and the responsiveness of those relationships, are less popular. To question learning, according to MeyerDrawe (2012) is to cast an alien perspective on an apparently familiar issue and to experience it as fragile. This fragility is inherent in the phenomenon of

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learning (and teaching). Many valuable disciplines make a study of learning, from psychology to sociology to the neurosciences to biogenetics. However, the perspective of pedagogy, an independent scholarly discipline that emerged in Continental Europe in the nineteenth century, is essential to the consideration of questions about the nature of learning (Schratz and Westfall-Greiter, 2015) and their interconnection with teaching. From a pedagogical perspective, the point of education is never to determine whether students are learning, but to ensure that they are learning something, and that they are learning for particular purposes and from someone (Biesta, 2012, 2017). In this pedagogical context, where a central role is attributed to the world and to the other, learning emerges not only from experience, but also as an experience in itself. Conceptualizing learning as an experience means that words used to describe that experience transform our perception of it. This is where phenomenology, as the philosophy of experience, can help pedagogy, researchers, and teachers to understand the experiential dimensions of learning (Meyer-Drawe, 2017). Even if our own learning experiences have retreated into the darkness (Meyer-Drawe, 2003), we can understand the learning processes of other learners. However, there remains an inevitable distance between concrete, situated experiences and our attempts to revisit those through discussion or reflection. In addition, it is important to note that we cannot ourselves initiate new learning experiences or control the initiation process, because during such an awakening, “one is present, but one cannot cause the act, an act which is, however, not possible without oneself” (Meyer-Drawe, 2017, 15). As living entities who want to learn (or to teach, as we will see later on), the only thing we can do is to pay attention to the unexpected, and in particular to our experience of failure, which has the potential to help us to return to the “things themselves”2 (Husserl, 1965, 81). Hence, learning as experience occurs when expectations based on habits of seeing and acting are thwarted. Confronted with the questionability of previous knowledge, learners become aware of their preconceptions. Furthermore, reflection enables a nd permits learners to confront not only their questionable historical knowledge but also their own identity as learners (Agostini, 2016). Teaching is no guarantee of learning, and this fundamental disconnect allows us to claim that learning is “beyond the reach of teaching.” (Schratz et al., 2014) What occurs in the classroom is a very personal event for each individual, the teacher, and the students. Teachers cannot directly influence the learning of their students. However, they can attend to the experiences of their students, consciously focusing on what emerges from students’ experiences and carefully responding to it, a process that Schratz (2009) has characterized in the German language as lernseits (from the point of view of learning)

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and that Agostini’s term pedagogical ethos (2020) suggests must be the fundamental basis of teachers’ professional approach. In our chapter we argue that a phenomenological concept of learning and the corresponding approach to teaching takes into account the interrelationship between teaching and learning and between teachers and learners, and the responsiveness of those relationships. Although teaching does not produce learning, teaching and learning are co-determining processes in the classroom. According to Horst Rumpf and Käte Meyer-Drawe (as cited in Schratz and Westfall-Greiter, 2015): “My teaching culminates in the learning of others.” As two sides of the same coin, teaching and learning are experiences that are intertwined in any pedagogical relationship. This chapter aims to showcase the theoretical and empirical foundations of this new “beyond the reach of teaching” approach in the field of education, drawing on a project developed in Austria. In 2008 and 2012, the Austrian Science Fund (FWF) financed two studies to examine the learning of pupils in all nine provinces of Austria. In order to investigate learning in heterogeneous groups and to derive proposals for teaching methods, an innovative approach had to be developed: the data collected were condensed into concise descriptions of scenes from school experience, known as phenomenologically oriented vignettes (e.g., Schratz et al., 2012; Baur and Peterlini, 2016). Contrary to studies of learning that focus on the result and not on the process and its origins, a phenomenological approach to pedagogy—and thus a vignette—focuses on how the learning experience occurs, that is the meaningful or irritating phenomena to which learners are exposed during their experience. This chapter first provides an overview of the history and foundations of the “beyond the reach of teaching” approach. It then describes the premises of learning and teaching from a phenomenological perspective and explains phenomenologically oriented vignettes, highlighting their role not only as research tools but also as tools for professional development, and their current use and development in an EU project on which the authors are collaborating with seven European partner institutions. Vignettes can be used to practice the beyond the reach of teaching approach. However, exactly what a personalized phenomenological approach to learning and teaching means compared with a constructivist approach focused on the individual, is not easy to explain. The chapter therefore goes on to outline key differences between the phenomenological and constructivist approaches, taking the Finnish “phenomenon-based learning” approach, introduced by a national reform in 2016, as an example. It concludes with some thoughts on the reciprocal relationship between learning and teaching and on the responsibilities of teachers and policy makers when it comes to school reforms.

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History and Foundations of the “Beyond the Reach of Teaching” Approach In 2008, a new Secondary School Reform (NMS-Reform) was launched as a pilot project, becoming a mandatory set of national reforms in 2012 and aiming to avoid separating cohorts into ability groupings at the age of ten. This was initially intended to bring together Neue Mittelschule (NMS—middle schools/lower secondary schools) and Allgemeinbildende höhere Schule (AHS— grammar schools with or without lower secondary classes). Full implementation of the reform would have meant that all Austrian students at lower secondary level would attend the same type of school until the age of 14. The NMS reform aimed to improve opportunities for students by applying new pedagogical approaches to enhance learning and by reinforcing teacher collaboration (Nusche et al., 2016). It was based on the premise that all students should receive the best possible education, regardless of their sociocultural background. Although a wholesale move towards comprehensive schooling at lower secondary level turned out not to be feasible politically, at the start of the reform the Austrian Science Fund (FWF) financed a large study of the policy. In order to investigate learning in heterogeneous groups in all nine federal states of Austria, an innovative research approach had to be developed, focusing on learning from a pedagogical perspective and as experienced by individual students—and no longer on the performance of those individuals. The data collected were condensed into concise descriptions of scenes from school experience, known as phenomenologically oriented vignettes. The phenomenological approach was applied to the study of diverse classroom communities across 24 NMS sites in Austria, and personalized data on learning were collected at three time points over a one-year period. To provide new insights into learning from a phenomenological–pedagogical perspective and to identify implications for teaching, the research was therefore designed to provide a model that would enable the international research community to observe universal phenomena of learning (and teaching) through countryspecific d ata; a m odel t hat w ould b ridge i nternational d ifferences be tween systems. The results of the study were expected to have direct relevance for Austria and to drive developments in educational reform, teacher education, teaching techniques, and school development in other countries. As countries seek to adapt their educational and “Industrial Plus” learning systems (see introduction to this anthology) to the needs of contemporary society, expectations of schools and teachers have changed profoundly. In the course of recent decades, education systems have in particular tried to reconcile the three imperatives of equality, efficiency, and excellence. In this context, learners adapt to and are formed by the school system; schools do

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not tend to adapt to the diverse individuals they serve and the social conditions in which they find themselves in order to provide greater opportunities and broaden access to higher education (cf. Schratz, 2003). The NMS reform project responded to these issues and resulted in a massive intervention for teachers, who suddenly had to nurture as well as challenge all children, irrespective of their social, cultural and linguistic background or their individual performance in primary school, with the clear policy goal of raising academic achievement and thereby increasing access to higher education. The project thus necessitated a fundamental reorientation of the instructional and organizational system of teaching and learning for 10- to 14-year olds, assigning them to heterogeneous groups to reflect the society in which the schools were embedded. To make this possible, teachers needed to adopt a new mindset, looking at their activities lernseits (Schratz, 2009) or develop a pedagogical ethos (Agostini, 2020) that went “beyond the reach of teaching”. Both mindsets presuppose an understanding of learning that comes from a genuinely phenomenological perspective. Teachers working from this perspective must themselves become learners in order to be able to teach responsively. Educational science and society require research to pay attention to learning and personal development processes. However, the question of the most effective teaching methods cannot be answered without understanding how learners learn (cf. Meyer-Drawe, 2012) and without becoming a learner oneself (Agostini et al., 2018). From a phenomenological perspective, teaching and learning are interactive, responsive processes that are in constant interplay with the social, cultural, organizational, and political contexts in which they are embedded. Consequently, the present approach has a theoretical foundation that goes beyond individual cognitive, constructivist or competence-oriented views of learning, or discussions of alternative modes of instruction, teaching methods, and adequate learning environments (cf. e.g., Vygotsky, 1978; Helmke, 2012; Müller et al., 2013), and includes the confrontation of individual learners with challenging content as an equally essential component of teaching. The focus must be on the day-to-day lived experiences of learners and their personal learning and development in and through educational processes. Capturing the lived experience of learners in diverse classroom communities and taking a phenomenological approach to data analysis can reveal universal phenomena related to learning (and teaching).

Personalized Learning and Teaching from a Phenomenological Perspective The focus on the personal in the present research is not only aligned with the current paradigm shift taking place internationally with regard to

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personalized learning, education for the twenty-first century and teaching in superdiverse classrooms, it is also an inevitable consequence of the phenomenological approach to research into learning. Recent theoretical work on the phenomenology of learning conducted by Meyer-Drawe (2012) defines learning from a pedagogical perspective strictly as experience, which is “an idiosyncratic entanglement in a world to which we respond by taking on its articulations” (p. 16). She goes on to argue that learning “in this sense is not solely instruction. It is an event. This does not, however, make teachers superfluous. The more they know about the contingent and discontinuous nature of learning, the more they will be able to exploit the opportunity of the moment”3 (2012, 16). It is precisely research into the phenomena of learning as experience, and the qualitative collection of data on the opportunities provided in and through educational processes leading to moments of learning, that will have an impact on issues related to teaching. The classroom per se is seen as a space where learning opportunities or “pedagogical moments” (Van Manen, 1991, 187f.) occur. Nevertheless, can teachers initiate, assist, and improve or conclude complete cycles of learning if they have scarcely any grasp of the phenomena of learning (cf. Meyer-Drawe, 2012)? Focusing on vignettes of the lived experiences of learners in the classroom can provide data on teachable moments. Vignettes initially reveal how learners take on the articulations of their lifeworld (Lebenswelt) and subsequently enable the identification of implications for teaching. However, we need to think “beyond the reach of teaching” to achieve this, considering the interconnectedness of teaching and learning alongside the idea that teachers themselves have to become learners. This reinforces the idea that teaching is central to the learning of all. When teachers teach, they too can be surprised by their own expectations and experiences and can themselves become learners (Agostini et al., 2018).

Phenomenologically Oriented Vignettes: An Example A vignette entitled “Patrick, Peter and Miss Piccali” is presented below in order to explore the potential of vignettes for teaching “beyond the reach of teaching”. Miss Piccali, the Italian teacher, asks the students to resume their work from last time. Paul shuffles sl owly in to th e ne xt ro om an d si ts wi th Ph ilipp an d Patrick at two tables that have been pushed together. A big book is open in the middle, showing a text entitled “L’antica India” (Ancient India) on the left, and a large drawing of several Indian warriors on the right. The boys begin to copy the text. They repeatedly break off from their work, put their heads

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together and whisper. Scraps of German language are heard. Suddenly Paul, who is facing the door, warns: “Teacher.” They fall silent. Miss Piccali sternly asks, “Come parliamo?” (What language do we speak in?) Patrick replies “In italiano!” (In Italian). Miss Piccali reads the brief text slowly and repeats her explanation. Paul’s eyes move from the teacher to a point in the book and back again. As Miss Piccali leaves the room, the boys continue to transfer the text from the book to their sheets. “This can’t be right”, Paul shouts and suddenly looking at the picture he exclaims, “Look there, in the picture! They’ve got real spears. They’ve slung their bows and can shoot really strongly.” Suddenly, he jumps on his chair and mimics an archer. He stands up straight, looking into the distance. Then he bends the bow, shoots, and raises his arms in triumph. With a beaming face he descends from the chair and sits. All three now bend low over the drawing in the book, their fingers moving over the picture pointing out the different details. They laugh loudly while discussing the differences, mostly in German. Suddenly Miss Piccali is standing next to the table. “Vi interessa? O lo state facendo, perché avete iniziato?” (Are you interested in this? Or are you just trying to get it finished?), she asks sternly. (Agostini, 2016, 207f.)

Phenomenologically oriented vignettes are aesthetically condensed and situational narratives that exemplify learning experiences and make them physically perceptible. Vignettes have been used in the context of Austrian schools since 2008. However, the “Innsbruck vignette research” approach has been further developed and vignettes have also been deployed for social spaces outside of school (e.g., Peterlini, 2016; Agostini and Bube, 2021). Sometimes vignettes focus only on one person or a few people, such as students, sometimes there are interactions between different groups of people, for example students and teachers as in our sample vignette. Research on vignettes in (in)formal educational contexts and social learning spaces centers on methodologies that are suitable for exploring dimensions of experience, for example the phenomena of learning and teaching or pedagogical activities, and for getting as close as possible to the participants (Symeonidis and Papadopoulou, 2020). Understanding learning as experience rather than as a product of experience means that the challenge for researchers is how to capture learning experiences in the field. In the fi eld, re searchers at tempt to st ay open and particularly attend to elements such as atmosphere, facial and bodily expressions, and tone of voice while co-experiencing. These details are noted by researchers in protocols, which then form the basis for writing the vignettes. The vignette stems from researchers’ co-experience of the lived experience of learners during the pedagogic action,in medias res. It is a rich

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description of the experiential qualities of a tangible moment perceived by the researcher. To ensure that the researcher communicates the essence of the learners’ experience as completely as possible, as it is co-experienced, without interpretation, the individual genesis of a vignette is documented, then validated in the field through communication with subject teachers and through a workshop within the research team. The data collected from the school experience is triangulated as necessary with other methodological approaches such as photo evaluation, document analysis, focus groups, and interviews. Vignettes, however, are not about reconstructing experiences, but rather about bringing them to mind. Reenacting the experiences involved makes it possible to identify specific, concrete learning and teaching opportunities that might have been overlooked in the situation itself. Hence, the first function of phenomenologically oriented vignettes is to act as qualitative research instruments that capture moments of experience and condense them into concise scenes. The following research questions are central to grasping such scenes and condensing them into vignettes: What kind of experience—in different pedagogical situations or subjects, such as Italian as in above vignette—does learning involve? What are the physical, spatial, temporal and/or relational characteristics of such experiences? The answers to these questions provide differentiated insights into learning, as well as into teaching processes. The ways in which vignettes deviate from our expectations are productive, because they allow us to see what we no longer know—for example, as in the sample vignette, which allows readers to experience how content can be so appealing that it makes students jump onto the table. Instead, the teacher, Ms. Piccali, is oblivious to the experience and seems certain that the students just want to finish the exercise as quickly as possible. Instead of being productively unsettled by her students, she sticks to the familiar path, and to the teaching methods she already knows. Vignettes aim to intensify and expand experience for readers, addressing their physical responsiveness (Waldenfels, 2000). The empirical approach of vignette research seeks to differentiate the multiplicity of meaning inherent in experiences without rashly categorizing or establishing it conclusively, drawing invisible learning out of the shadows. By focusing on the experience of learning, vignettes also have the potential to illustrate the shift from practical to academic knowledge, a transition that teachers often have to force in schools.

Taking Teaching Through Vignettes Seriously Vignettes aim to provide examples, showing rather than telling, and as such can also be used to educate, train and develop (prospective) teachers as well

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as schools. The ProLernen4 project uses vignettes as a professionalization tool in the training of teachers and school leaders at tertiary education institutions (universities, teacher training colleges) and for school development (incl. kindergarten and other formal educational institutions) and quality management. Günther Buck (1989) has highlighted the educational power of examples, and in the same way, vignettes also refer to intersubjective and therefore relational experiences, which can be intuitively understood and therefore recognized by teachers reading the vignette. In the same way as an example, a vignette always illustrates analogous phenomena and experiences, which can be understood and learned from (Buck, 1989). Where a vignette depicts a specific enforcement experience, for instance, reading about the experience shown in the vignette will make us aware of our previous knowledge. Further reflection on our preconceptions enables us to confront what might now be questionable knowledge. Therefore, in reading the vignette, readers become aware of the unexpressed, vague and naive practical knowledge that forms the basis for their own expectations and acquire explicit academic knowledge. The transformative experiences of the students enables the reader to re-enact at first-hand the transition from practical knowledge to academic knowledge (Agostini, 2016). As vignettes already contain universal experiences that are relevant to other situations, a phenomenological theory of learning and teaching does not need to be transferred into practice. Consequently, vignettes, which anticipate practice and thus reach (Meyer-Drawe, 2009, 14) into theory and practice as well as learning and teaching, are inextricably intertwined with each other. The so-called application problem of converting theory into practice on a one-to-one basis does not therefore arise. The exemplification provided by the vignette enables the experience of learning in which one transitions from one form of knowledge to another. The use of vignettes in teacher education provides students with an educational practice based on concrete experience and a pedagogical theory that derives from (pre)reflexive understanding. Consequently, vignettes grant (trainee) teachers access to both educational practice and theory, with no issues of transition. Academic debates and various international projects looking at the use of vignettes show that on the one hand their formal structure makes them easily accessible, but that on the other hand, there is a risk that prospective teachers will read them in particular with regard to their own prior learning, and that teachers will read them only on the basis of their work experience. However, vignettes represent an attempt to practice a phenomenological approach to pedagogy that goes “beyond the reach of teaching” and thus to examine learning without overlooking the important role played by teachers.

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Differences between Phenomenological and Constructivist Approaches: The Example of Phenomenon-Based Learning A pedagogical and phenomenological approach to learning and teaching differs from the dominant constructivist approach, which is grounded in psychology. Perhaps we can better understand those differences by comparing the phenomenological approach with phenomenon-based learning, which became popular in Finland following the reform of the national curriculum in 2016. Drawing on the study of Symeonidis and Schwarz (2016), this chapter highlights the main differences between the two approaches. Phenomenon-based learning is grounded in constructivism and includes elements of sociocultural learning and progressive inquiry, aiming to prepare learners to solve problems in real life. While traditional instruction is focused on isolated subjects that appear separate from each other, the study of phenomena helps learners to make connections across different disciplines. According to Silander (2015a, 16), holistic real-world phenomena provide the starting point for learning: “The phenomena are studied as complete entities, in their real context, and the information and skills related to them are studied by crossing the boundaries between subjects.” Examples of phenomena can include topics, such as climate change, the European Union, media and technology, water and energy. Such topics can bridge the divide between different subjects, creating opportunities to integrate a range of pedagogical methods and learning environments. Phenomenon-based learning starts with the observation of a real-world phenomenon that is studied from different points of view, thus not limiting the observation to a single perspective. Silander (2015b) argues that phenomenon-based learning consists of five dimensions: holisticity, authenticity, contextuality, problem-based inquiry and learning process. Depending on how the specific approach is implemented in the classroom, the results can range from a superficial study of the phenomena based on limited evidence to more advanced learning. It thus becomes clear that all the approaches that inform phenomenonbased teaching and learning are grounded in and derive from constructivist epistemology, including multiple individual perspectives, authentic problemsolving activities, real-world environments, inquiry learning and scaffolding. The phenomenon-based learning and teaching approach implies that it is the learner, in a process facilitated by the teacher, who mediates learning. The role of the teacher is to guide and organize the learning process rather than to provide knowledge. Hence, whereas a constructivist perspective views students as active participants who engage in the social construction of knowledge, a phenomenological perspective pays particular attention to the idea

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that learning cannot be self-initiated but starts with another person (e.g., the teacher) or a thing (e.g., the book or the drawing in the book in our sample vignette). Examination of phenomenon-based learning through the theoretical lenses of phenomenology reveals important implications for teaching and learning. A constructivist context seems to absolve teachers from their responsibility to teach, because the meanings of phenomena emerge in the heads of students. As already outlined, a phenomenological approach requires teachers to give way to the experiences of the students on the one hand and on the other hand to recognize moments of learning as they arise. But they must also assume a share of responsibility for the educational process. According to Biesta (2012, 44), teachers need to make “concrete situated judgements about what is educationally desirable, both with regard to the aims of education and with regard to its means”. Competences, while important, cannot be considered a sufficient condition for good teaching, since each particular teaching situation requires teachers to judge which competences students need to use (ibid.). In this respect, the advantages of the Finnish approach to teaching and quality teacher education must, as Sahlberg (2011) points out, remain high on the policy agenda when educational reforms are planned or implemented. With regard to the role of students, the idea of self-regulated learners might lead to the unintended outcome of students being blamed for the failure of educational endeavors. As already outlined above, learning is always about something, for a particular purpose and delivered by someone. From a phenomenological perspective, learning as an experience implies that students have to undergo experiences; they cannot construct them. And this gives rise to an element of uncertainty and ambivalence, which educators need to be prepared to encounter.

Concluding Remarks: Responsivity as a Virtue Phenomenological conceptions of learning emphasize that learners undergo experiences, which is different from constructing knowledge. They place value on an element of uncertainty and on the belief that neither teaching nor learning is fully predicated on instruction, and that the former does not necessarily result in the latter. Accordingly, it is difficult to derive guidelines for teaching or specific proposals for teacher education based on a phenomenological theory of learning. Nevertheless, vignettes are a tool that promotes a mindset going “beyond the reach of teaching” and a perception of the relationship between learning and teaching as being interwoven yet not directly interdependent. Pedagogical and phenomenological approaches to learning and teaching characterize this relation as reciprocal (Meyer-Drawe, 2012; Waldenfels, 2009; Westphal, 2015; Agostini, 2016). Neither teachers

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nor students are the sole generators of successful learning outcomes, but their reciprocal relationship gives rise to a transformation that is the work of both. Approaches that neglect the reciprocity of this relationship and the responsibility of teachers risk turning teaching and learning into neoliberal practices in which the students are not only blamed if their endeavors fail but also ascribe blame to themselves. Pedagogical and phenomenological approaches to reciprocal teaching and learning regard both sides of the same coin as essentially social activities that reflect both the contemporary and the historical dimensions of society. In this, it is important to beware of educational reforms and policies that shift the responsibility for learning outcomes onto students and reduce the role of the teacher to that of facilitator, mediator and organizer of multidisciplinary learning modules.

References Agostini, E. (2016). Lernen im Spannungsfeld von Finden und Erfinden. Zur schöpferischen Genese von Sinn im Vollzug der Erfahrung. Paderborn [et al]: Schöningh. Agostini, E. (2020). Aisthesis – Pathos –Ethos. Zur Heranbildung einer pädagogischen Achtsamkeit und Zuwendung im professionellen Lehrer/-innenhandeln. Erfahrungsorientierte Bildungsforschung, Vol. 6. Innsbruck and Wien: StudienVerlag. Agostini, E. & Bube, A. (2021). Anders wahrnehmen und anderes verstehen am Beispiel der Vignettenforschung ‘Nah am Werk’. In: Erfahrungen verstehen – (Nicht-)Verstehen erfahren, edited by V. Symeonidis & J. F. Schwarz. Erfahrungsorientierte Bildungsforschung, Vol. 8 (pp. 67–89). Innsbruck and Wien: StudienVerlag. Agostini, E., Schratz, M., & Risse, E. (2018). Lernseits denken – erfolgreich unterrichten. Personalisiertes Lehren und Lernen in der Schule. Hamburg: AOL. Baur, S. & Peterlini, H. K. (Eds.). (2016). An der Seite des Lernens. Erfahrungsprotokolle aus dem Unterricht an Südtiroler Schulen – ein Forschungsbericht. Erfahrungsorientierte Bildungsforschung, Vol. 2. Innsbruck, Wien, and Bozen: StudienVerlag. Biesta, G. (2012). Giving Teaching Back to Education: Responding to the Disappearance of the Teacher. Phenomenolog y & Practice, 6(2), 35–49. Biesta, G. (2017). The Rediscovery of Teaching. New York and London: Routledge. Buck, G. (1989). Lernen und Erfahrung – Epagogik: zum Begriff der didaktischen Induktion (3rd expanded ed.). Darmstadt: Wissenschaftliche Buchgesellschaft. Helmke, A. (2012). Unterrichtsqualität und Lehrerprofessionalität. Seelze: Klett Kallmeyer. Husserl, E. (1965). Philosophie als strenge Wissenschaft. Edited by W. Szilasi. Frankfurt a. M.: Vittorio Klostermann. Meyer-Drawe, K. (2003). Lernen als Erfahrung. Zeitschrift für Erziehungswissenschaft, 6(4), 505–514. Meyer-Drawe, K. (2009). Theorie als Vorgriff auf die Praxis. Zur Bedeutung des Studium für pädagogisches Handeln. In Schulpraktische Studien in gestuften Studiengängen, edited by R. Bolle & M. Rotermund (pp. 11–29). Leipzig: Leipziger Universitätsverlag. Meyer-Drawe, K. (2012). Diskurse des Lernens (2nd revised and corrected ed.). Munich: Wilhelm Fink. Meyer-Drawe, K. (2013). Lernen braucht Lehren. In Pädagogische Reform: Anspruch – Geschichte – Aktualität, edited by P. Fauser, W. Beutel, & J. John (pp. 89–97). Jena: Klett Kallmeyer.

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Meyer-Drawe, K. (2017). Phenomenology as a Philosophy of Experience – Implications for Pedagogy. In Erfahrungen deuten - Deutungen erfahren. Vignettes and Anecdotes as Research, Evaluation and Mentoring Tool, edited by M. Ammann, T. Westfall-Greiter & M. Schratz. Erfahrungsorientierte Bildungsforschung, Vol. 3 (pp. 13–21). Innsbruck: StudienVerlag. Müller, K., Gartmeier, M., & Prenzel, M. (2013). Kompetenzorientierter Unterricht im Kontext nationaler Bildungsstandards. Bildung und Erziehung, 66(2), 127–144. Nusche, D., Radinger, T., Busemeyer, M. R., & Theisens, H. (2016). OECD Reviews of School Resources: Austria. Paris, France: Pubilishing Paris. Peterlini, H. K. (2016). Lernen und Macht. Prozesse der Bildung zwischen Autonomie und Abhängigkeit. Erfahrungsorientierte Bildungsforschung, Vol. 1. Innsbruck, Wien, and Bozen: StudienVerlag. Sahlberg, P. (2011). Paradoxes of educational improvement: The Finnish experience. Scottish Educational Review, 43(1), 3–23. Schratz, M. (2003). Heterogenität als Organisations- und Arbeitsprinzip. Erkundungen in die Zukunft. Journal für Schulentwicklung, 7(4), 42–50. Schratz, M. (2009). “Lernseits” von Unterricht. Alte Muster, neue Lebenswelten – was für Schulen? Lernende Schule, 12(46, 47), 16–21. Schratz, M., & Westfall-Greiter, T. (2015). Lernen als Erfahrung: Ein pädagogischer Blick auf Phänomene des Lernens [Learning as Experience: A Continental European Perspective on the Nature of Learning]. In The Nature of Learning – Die Natur des Lernens. Forschungsergebnisse für die Praxis, edited by H. Dumont, D. Istance & F. Benavides (pp. 14–33). Weinheim: Beltz. Schratz, M., Westfall-Greiter, T., & Schwarz, J. F. (2012). Lernen als bildende Erfahrung. Vignetten in der Praxisforschung. Innsbruck, Wien, and Bozen: StudienVerlag. Schratz, M., Westfall-Greiter, T., & Schwarz, J. F. (2014). Beyond the Reach of Teaching and Measurement: Methodology and Initial Findings of the Innsbruck Vignette Research. Journal for Latin American Education Research, 51(1), 123–134. Silander, P. (2015a). Digital Pedagogy. In How to create the school of the future: Revolutionary thinking and design from Finland, edited by P. Mattila & P. Silander (pp. 9–26). Oulu: University of Oulu, Center for Internet Excellence. Silander, P. (2015b). Rubric for Phenomenon Based Learning. Retrieved October 5, 2016, from http://www​.phenomenaleducation​.info​/phenomenon​-based​-learning​.html Symeonidis, V. & Papadopoulou, V. (2020). I mathisi os empeiria: Mia fainomenologiki proseggisi stin ekpaideutiki ereuna me ti xrisi vignettwn [Learning as experience: A phenomenological approach to educational research trough vignettes]. Educational Review, 37(70), 143–158. Symeonidis, V. & Schwarz, J. F. (2016). Phenomenon-based Teaching and learning through the Pedagogical Lenses of Phenomenology: The Recent Curriculum Reform in Finland. Forum Oswiatowe, 28(2), 31–47. Van Manen, M. (1991) The Tact of Teaching. The Meaning of Pedagogical Thoughtfulness. Ontario: Althouse. Vygotsky, L. (1978). Mind in Society. Cambridge, MA: Harvard University Press. Waldenfels, B. (2000). Das leibliche Selbst. Frankfurt a. M.: Suhrkamp. Waldenfels, B. (2009). Lehren und Lernen im Wirkungsfeld der Aufmerksamkeit. In Umlernen. Festschrift für Käte Meyer-Drawe, edited by N. Ricken, H. Röhr, J. Ruhloff, & K. Schaller (pp. 23–33). Munich: Wilhelm Fink. Westphal, K. (2015). Kulturelle Bildung als Antwortgeschehen. Zum Stellenwert der Phänomenologie für die kulturelle und ästhetische Bildung. In Pädagogische Erfahrung. Theoretische und empirische Perspektiven, edited by M. Brinkmann, R. Kubac & S. Rödel (pp. 89–106). Wiesbaden: Springer VS.

Chapter 8 WHERE IMMERSION, EXPERIMENTATION, GAMING AND LEARNING MEET—LEARNING IN VIRTUAL REALITIES Carla Aerts

Introduction The acceleration of today’s technological and scientific evolution is unprecedented. Devices and the lure of the screen have not only become a fact of life; they drive scientific progress and the Internet revolution. The development of smart technologies has created a new paradigm for machine–human relationships in which the machine is not only becoming the repository, but also the curator and orchestrator of knowledge. Knowledge acquisition in itself no longer suffices to succeed and thrive in the world. Acquiring and applying knowledge in and through experience is becoming ever more important to tackle the challenges and uncertainties of our world. The reality of experience in, and of the physical world, is increasingly moulded in digital interaction, enabled by high-end computers, graphics, smartphones and progress in machine learning and artificial intelligence. Add the evolution of VR and immersive technologies, it is clear that our experience of reality and relationship with the world is impacted profoundly. This has become most obvious in gaming. Reflecting on Third Generation Learning and Teaching, however, the uptake of VR and immersive technologies is rather sluggish and disparate, as education typically remains a late adopter of technologies. Generation Z or Generation Zoomers (Gen Z or Generation Zoomers, born after the Millennials between 1990 and 2010) engage passionately in the fast-evolving new realities and make-belief worlds these technologies evoke. It is the world Generation Z grew up in and their successors Gen Alpha, born

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after 20101, have lived in since birth. The question begs whether the temptation of the screen obfuscates their experience of the world and understanding of what knowledge-acquisition in a digital world means, refers to or should be. What do Gen Z and Gen A learn today, and more precisely what should they learn today and how? We know that knowledge alone is no longer an asset in a world in which The Fourth Industrial Revolution, a term coined by Klaus Schwab, has shifted the paradigm of intelligence and knowledge. The importance of what we learn may be declining, the importance of how we learn and how to apply knowledge in and through experience is evermore critical to thrive. Is education responding to the world these youngsters grow up in? A world in which they appear to move seamlessly between immersive Virtual Realities of gaming and the reality of education, often stuck in outdated models of learning and instruction? How should education respond to a world in which knowledge is ubiquitous on the Internet, in which youngsters increasingly learn, engaged in informal learning and skills-development through YouTube videos, gaming and play in virtual make-belief realities? Despite the impact of this digital technological (r)evolution on their lives, learners still find themselves far removed from their reality at school, college or university. A dissonance the COVID-19 pandemic has amplified; highlighting our reliance on the Internet and digital technologies, the need for new learning models and digitally augmented modes of learning and teaching; yet woefully demonstrating how adopting them effectively and equitably too often remains maladjusted and ignored. What can immersive Virtual Reality and EdTech2 bring to education to address this disconnect and respond to the challenges of a society that will increasingly rely on twenty-first century skills and lifelong learning?

Immersive Virtual Reality Experiences The term virtual reality evokes images of headsets and jerky movements of users teleported into make-belief worlds that are invisible to the onlooker. Yet, VR does not solely rely on teleporting or sophisticated equipment and includes a set of different technologies or the fully-fledged Metaverse. Augmented reality (AR) produces a digitally augmented view of the world. Pokémon Go and Snapchat Lenses are examples of AR, most often accessed through mobile devices or AR glasses. AR can reach the sophisticated augmentation of sports analysis as an overlay on TV replay; simulate the look of furniture in your home; or bring historical sites to life through content augmentation. In the classroom, AR tends to support the augmentation of textbooks.

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VR refers to computer-generated simulations that teleport the user into an interactive three-dimensional (3D) multi-sensory make-belief world. VR is typically associated with headsets, touch controllers or gloves, perceived as high-end tech, but can also be accessed on computers or mobile devices for non-teleported immersive experiences. VR is used in a growing list of applications. Lifelong learning, vocational education, life coaching and professional development are increasingly augmented by VR technologies. To date VR has mainly been driven by Facebook’s Oculus brand, launched in 20163. The Oculus Rift and the Oculus Quest remain the predominant enablers of high-end VR simulations. Keen to democratize VR and bringing VR to the classroom, Google launched Google Cardboard in 2015, a cheap cardboard stereoscope, supported by Google Expeditions its Virtual Reality App. However, the company is discontinuing its VR endeavours. An indicator perhaps, that Google cardboard didn’t get the anticipated traction, nor the sophistication expected from VR teleporting and immersion. As costs keep coming down, more headset manufacturers are entering the market. Technology improvements and sophistication will enable support for collaboration and AI is likely to influence V R p rofoundly. A s h igh-end V R technologies can prove prohibitively expensive, how they will fare in education is harder to gauge. Their growth—driven by the Asian market—is likely to be slower as education remains somewhat of laggard in the digital world. VR headsets don’t suit everyone. They can bring on motion or cybersickness, which can make teleported experiences extremely unpleasant. Many users also find headsets cumbersome or worry about feeling isolated from the world around them. Combined with the cost and challenges of introducing new technologies to the classroom, these perceived shortcomings are likely to remain an obstacle for adoption of VR in the classroom for some time to come. However, non-teleported VR accessed through computers, still allows for immersive experiences, interaction and even collaboration with others. These VR experiences are cheaper and less challenging to implement in the classroom, making their affordances more accessible. Mixed realities (MR), the third VR technology, enables the merging of real and virtual worlds. In MR, real and digital objects co-exist in a hybrid of the real and the virtual. Unlike AR that superimposes additional information on reality, MR enables interaction between the physical and the virtual world in real time. MR requires bespoke headset technology, championed by Microsoft’s HoloLens. Described by some as a mix of AR and VR, HoloLens, the Mixed Reality feature in Windows 10 and Microsoft’s gaming console, Xbox, have been the key drivers for MR. Unlike Oculus, which immerses the user in a virtual world, HoloLens leaves the user in the real world, whilst interacting with 3D virtual elements in it4.

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VR Technologies in Education: A Story of Growth The VR market is forecast to reach US$57.55 billion by 2027, growing 44.3 percent on average annually, driven by the COVID-19 pandemic, cloud computing, 5G5 networks, gaming and technology developments. A growing demand for training, designing and prototyping, R&D simulations or teaching and learning at reduced costs, makes these technologies compelling. Analysts have forecast VR in education growth of 42.5 percent annually, between 2020 and 2027, driven by the COVID-19 pandemic6. Despite these optimistic growth forecasts and enthusiasm for VR headsets in East Asian classrooms—as the region adopts digital technologies by stealth—imminent uptake is less likely for the majority of the world’s classrooms.

Gaming in Make-belief Worlds Online video-gaming cannot be ignored when considering VR. Immersive video games and multi-player online gaming are firmly embedded in childhood, teen and adult lives7. The games market is flourishing and forecast to grow by 32.75 percent annually, from US$152 billion in 2019 to US$275 billion by 2025, once more driven by East Asia. From app-based games, such as Brawl Stars, car chasing games to multi-device games, including the highly popular ROBLOX or the sophisticated make-belief worlds of Fortnite, video gaming has become ubiquitous. This booming business is driven by aggressive competition, merchandising and eSports championships with lucrative winnings by celebrity role models for millions of enthusiasts. Gamers, including kids and teens, spend an average of 7-hours weekly playing games, lured by a diversified and highly competitive offer. De spite parental worries about screen-time and excessive gaming, the world of gaming and eSports is progressively gaining educator interest as many games provide informal as well as formal learning opportunity. One game, Minecraft, stands out as its learning potential has been recognized by educators worldwide. Tuning into the power of Minecraft the tech giant, Microsoft purchased this open-ended, multiplayer immersive PC-based game from its developer, Mojang in 2014 for US$2.5 billion. Understanding the potential of this sandbox game8 for education, Microsoft also acquired MinecraftEDU from Teacher Gaming LLC, for an undisclosed sum. This led to the creation of a dedicated division driving Minecraft for education. Despite the reluctance of many countries’ education policy makers for classroom adoption, Minecraft EDU is estimated to in use in over 115 countries. Minecraft is no longer the sole contender for the classroom. Other game developers are realising the potential of gaming for learning. More recently the highly popular game ROBLOX has attracted educator attention, witnessing

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growing adoption in informal coding and game design clubs as well as the classroom. Recognizing the significance for education, ROBLOX Education or ROBLOX Edu offers free resources, curricula, and lesson plans to teachers. Using ROBLOX Studio learners can engage in peer learning and collaborative immersive learning projects in game design, coding, entrepreneurship and citizenship programs, without the need for high-end equipment.

Immerse, Discover, Experience and Play: We Learn Children and teens are extremely adept and versed in immerse technologies and embrace their culture. Many educators have started to recognize the relevance of these technologies and culture engagement for positive learning experiences. Entering a pyramid and descending towards the pharaoh’s tomb where his burial is taking place, discovering the sarcophagi and a mummy. Taking part in the process of mumification not only brings understanding of the process but can heighten cultural understanding and historical insight. Being teleported into a tribal village in the Amazon Forest, subject to encroaching deforestation, can support perspective-taking and critical thinking, or provide problemsolving exercises on the relationship climate change with eco-systems. These experiences can be repeated, and if online, can be accessed anywhere at any time. The science lab’s VR cousin can provide safe, accessible and cheaper learning environments. Medical students can immerse themselves in the study of anatomy, practice surgical procedures or train in bedside manner in a makebelief world immersed in VR simulations. These examples demonstrate how experiential immersion can augment Third Generation Learning opportunity. Virtual Reality and immersive learning experiences are reported to be suited to engaging and motivating many learners. Research into the use of VR in learning contexts reveals that VR can promote empathy and support perspective-taking, through immersion in different c ontexts. Engaging in refugee camp or conflict resolution simulations, teleport real-life scenarios into a virtual world, its real-life counterpart too remote or abstract for learners to engage with. Examples of the positive impact of VR and the benefits of immersive learning experiences vary from the classroom to higher education, professional and lifelong learning, highlighted in some case studies below.

Culture-Relevant Make-Belief Worlds for Positive Classroom Behavior: Classcraft Committed to the growth and potential of learners and promote human connection using playful learning approaches, two entrepreneurial Canadian brothers and their father founded Classcraft in 2013.

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Aiming to foster positive classroom behavior, Classcraft deploys modern game-play driven pedagogical approaches and role-play to create culture-relevant learning experiences, supported by its virtual immersive and collaborative learning platform. Classcraft has gained international reputation and its platform, available in 11 languages, is used in over 160 countries. Convinced motivation is a fundamental factor for learning, Classcraft galvanizes the potency of story and engagement in video games to augment learning. It promotes learner agency by putting learners in control of their learning process. Adhering to a strong belief in the “power of positivity” and the importance of social engagement for learning, Classcraft nurtures positive classroom behavior by reinforcing collaboration and accountability to one’s peers, autonomy and individual competency development. Backed by research on the importance of positive behavior for fostering cognitive and metacognitive skills, the company’s platform augments positive behavior through Socio-Emotional Learning (SEL) supported by culture reference. The importance of cultural relevance for learner engagement sets Classcraft apart from most EdTech companies. Learners create their avatar to engage in immersive, personalized and collaborative learning experiences. Recognizing learner variability, the platform enables differentiated instruction and academic support, never losing sight of fostering peer-empathy, communication and collaborative skills development. Although learner-centric in its approach, Classcraft recognizes the undisputable importance of teachers. Fostering a pedagogical approach and cultural relevance, the platform enables teachers to leverage a symbiosis of learner-driven, collective, and collaborative endeavors. Not only to these bring positive engagement, they strengthen teacher–learner interaction. Responding to the COVID-19 pandemic, Classcraft developed a set of standards for remote learning, in collaboration with thousands of schools, aiming to encourage positive learning behavior in remote learning settings. These standards focus on developing three core learner attributes or skills, including: Being an Empowered Learner, Being an Engaged Learner and Contributing to the Learning Community.

Leveling the Playing Field through VR Schools in disadvantaged communities often lack access to technology, particularly VR. Yet their benefits for disadvantaged learners from low socioeconomic backgrounds can be very compelling. Fascinated by the potential of VR for curriculum and pedagogy, the Australian researcher Erica Southgate

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embarked on a research program in 2016. This led to a collaboration with visionary school leaders and teachers in two Australian schools, committed to developing innovative pedagogies and curriculum approaches focused on nurturing learner agency. Nearly 50 percent of learners at Callaghan College in Newcastle come from a poor socio-economic background; 10 percent of them from indigenous communities, whereas Dungog High School learners live in remote timber and agricultural country. An engaged school-researcher collaboration explored iVR’s9 potential for pedagogical and curriculum innovation for STEM education at Callaghan, and STEAM at Dungog.

Voyage to the Inner Workings of the Brain Teachers at Callaghan College developed a unit of work aligned to the Australian Sciences curriculum and introduced MinecraftVR to a subset of 13–16-year old learners, split into groups of three. Learners engaged in a collaborative, teleported immersive experience wearing Oculus VR headsets and using touch controllers. Tasked to perform online research and build a 3D prototype, representing a cross-section, or diorama of a body organ learners undertook research and created a Minecraft VR prototype. To avoid cyber-sickness the equipment was used 57 percent of lesson-time over a period of 6–7 weeks, during 21 1-hour long lessons. One group in particular stunned the teachers as a 3D-interactive skyscraper model emerged. This represented an abstraction of the brain, on top of a spinal column encased in spinal fluid, and representations of ribs and nerve endings. A transparent brain hemisphere exposed firing neurons. Lights revealed the brain’s workings at night and represented released thoughts. Learning ignited, MinecraftVR’s gaming capabilities were galvanized in an avatar flight. Circumnavigating the brain and landing on a platform allowed the avatar to engage in close-up inspection and activate neurons, using a flipflop switch.

A Tale of Mixed Engagement Some thrived and excelled; others worried about their appearance or feeling isolated from their environment and peers. They continued their project on a desktop computer. The teachers’ inability to observe and intervene in the teleported world brought challenges for classroom management or learnerintervention. Yet even the struggling learners collaborated, created and destroyed the structures they built. Not quite on task, but still developing learning, agency, and collaborative skills.

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The Virtual Drama Director Dungog High School chose a CAPA10 -aligned active learning experience for nine Senior Drama students. They built a virtual set using Google’s VR drawing program, Tilt Brush. This single-user VR software allows for onand offline, design and drawing experimentation in a virtual world. Working in groups of three, learners created a 3D immersive director’s vision for the Australian play Ruby Moon. Translating their vision into an audience experience, they evoked the play’s mood, atmosphere and symbols, in set, costumes and prop design. Tilt Brush could only be used by a single learner, but peer-advice and discussion informed the design process, as learners swapped headsets to move in- and out of the virtual world. Cooperative engagement developed into a collaborative self-directed process, facilitated by teachers turning pedagogical practice from unidirectional instruction into a collective constructive learning experience. The results surprised. The most reserved learners astounded their teachers. Unearthing the nature of the play, they produced an annotated 2D design and created a 3D immersive evocation of neighborhood disintegration, as a parallel to the breakdown of the characters’ reality and the audience’s ambiguity about truth. They thrived.

Experiencing Growth Overwhelmed by the challenge at first, learners revealed that they started thinking as directors and engaged in critical reflection as the project progressed. Trepidation morphed into a readiness to experiment. The ability to undo, change designs and using more elaborate sets of drawing tools than available in real life, gave them a heightened sense of experimentation, discovery and accomplishment.

Teacher Reflection Teachers reported that introducing VR to the classroom requires careful preparation, appropriate settings, and time to get acquainted with the technologies’ affordances a nd c onsider e thical i mplications. T hey h ighlighted the benefits o f t echnology-augmented s hared a nd c ollaborative immersive learning experiences and celebrated the learners’ hidden talents and developing autonomy. A demonstration that iVR can level the playing field, e mpowering d isadvantaged a nd r eserved l earners t o e xcel and shine.

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Addressing Disengagement in Science Education Science teaching has proved a dominant driver for VR in education. A key player in this field is the Danish EdTech company Labster, founded by science teachers and scientists who experienced disengagement in science lessons first-hand. Convinced better methods are needed to nurture the curiosity of learners, Labster is on a mission to promote enthusiasm for science and support the democratization of the Science Lab, transforming science learning into an inspiration rather than the dreary discipline learners feel subjected to. Having explored EdTech’s offering for science learning, nothing seemed to move the dial, nor achieve the outcomes the founders aspired to. They looked beyond the realm of education and found their AHA moment, discovering flight simulators. Inspired by the benefits these brought to pilot training, Labster’s simulation-based teaching methods started taking shape. Collaborating with MIT, Stanford, and the Technical University in Denmark, Labster developed science lab simulations and researched their efficacy. Committed to measuring learning progress and assessing efficacy, f ormative quiz-based assessments were integrated into virtual lab experiences. These enabled progress measuring and reporting in teacher and learner dashboards. A collaborative research-study with Stanford and Danish Technology University, revealed a 76 percent increase in learning effectiveness using Labster’s browser-based non-teleported virtual lab simulation, compared to traditional science lessons at highly reduced cost. An additional 10 percent increase was achieved, when simulations were facilitated, coached, and led by teachers who are at the heart of Labster’s approach. Labster didn’t stop there and turned to the use of story, strong narrative and learning design to bring meaning to science knowledge and aid learning transfer. Game designers and learning psychologists joined the team. With an entrepreneurial zest for pushing the envelope and reimagining education Labster turned a phone into a fully equipped science lab. Using cheap VR headsets individual students are teleported into a lab, guided by a virtual assistant and are able to access supporting content on a virtual tablet. Like Alice in Wonderland, learners can shrink into a molecule inside a PCR machine11, teleported into the workings of DNA, travel through the world of blood cells, discover and experiment. Understanding that a reliance on VR headsets would prevent the potential for democratization and collaboration, the start-up exploits the ubiquity of laptops, tablets and mobile devices using browser-based virtual labs to engage students and young learners in accessible collaborative virtual lab experiments.

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Labster’s leap into VR science lab simulations is set to continue, reach increasing numbers of learners and teachers, engaging them in immersive experiences in which technology and humans co-exist, co-create and learn, inspired by science.

Innovating and Disrupting Education at Erasmus University Rotterdam Before the COVID-19 pandemic, many universities had started using gaming and immersive platforms in lectures, courses or for research. However, not core to research or teaching activity, these initiatives tended to peter out. Cognizant of the changing role of university to prepare students for the challenges and complexities of twenty-first century professional l ife a nd society, Erasmus University Rotterdam embarked on a program of academic innovation. Aspiring to become a leading, relevant higher education institution, embedded in its strong tradition of research and education partnership, the university seeks to respond to complex societal challenges and a technology-driven world. Focused on student engagement and success, Erasmus University sought to explore new modes of education, co-created with students. Several new education innovation initiatives emerged, the most radical of which ErasmusX, named after the space in which the program was to take shape. Determined to lead education innovation, embrace change, even disrupt the nature of education itself, students are invited to shape or create their own education path. Harnessing new technologies and addressing societal issues, students are encouraged to tinker and experiment in a multi- or interdisciplinary context. Positioned outside of the regular academic curriculum, ErasmusX can engage in radical, transformative innovations, encouraged to develop and research new pedagogical concepts and practices. As ErasmusX got out of the starting blocks, COVID-19 struck the Netherlands. A lockdown left the innovation space deserted as lectures moved online and co-creation projects could not continue in person. Yet, a set-back turned into an opportunity. The team opted for a metamorphosis of the deserted campus. Utilizing virtual, immersive 3D technologies, ErasmusX invited students to co-create the first Virtual Minecraft University Campus in the Netherlands. Initiated with the help of the student-led Erasmus eSports community, students designed and built a replica of each campus location. Expert and professional Minecraft expertise was drafted in for the final touches and to develop a custom-made scavenger hunt game for the onboarding days of the upcoming academic year. Built to a 1:1 scale, a tour of the virtual campus takes as long as a touring the real campus. Inspired by ErasmusX, a number of Dutch universities embarked on virtual campus co-creation projects.

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The story doesn’t end there. Minecraft Campus needed a purpose beyond a meeting place and visitor center. Realizing that social distancing and social interaction can go hand-in-hand and open up new opportunities, ErasmusX pivoted Minecraft Campus into an education technology project. The campus became a co-creation innovation space, for students to design their own learning journeys. The aspiration to develop, trial and implement new pedagogical approaches, setting assignments and engaging students in problem-solving and creative gameplay became reality. Students did a poster presentation and developed solutions for the United Nations’ SDGs12. They engaged in a course on creative problem-solving using Minecraft and an assignment on the sense of belonging and its relation to physical space. A gamified legal technology module for over 1,000 is in development. Sharing their initiative with others, ErasmusX Minecraft Campus hosted TU Delft students immersing students into a virtual inter-university hackathon13 at the end of May 2021. Inspiring other Dutch universities who started their innovation programs, ErasmusX immersive virtual learning space undoubtedly represents a launchpad for continued reimagining of higher education. Pedagogical innovation, research, disruptive and entrepreneurial interdisciplinary programs continue to develop and invite students to create their own educational journeys. A first for the Netherlands that inspired others.

Summary Today’s youngsters are ever more exposed to and immersed in VRs and technologies that mold their life, culture and engagement with the world and its challenges. VR may be in its infancy in supporting third generation teaching and learning, yet the impact of these technologies on learning and the futures of learning will continue to evolve as their affordances become increasingly recognized. Ignoring the importance of VR and gamified immersive technologies for education, neglects the culture and the world of today’s learners, rather than putting them at the heart of education and learning. Having demonstrated how these immersive learning experiences can be a catalyst for learning engagement, the development of agency and twenty-first century skills, their importance for nurturing learning potential can no longer be ignored.

Bibliography Allcoat, D. and von Mühlenen, A. 2018. Learning in Virtual Reality: Effects on Performance, Emotion and Engagement. Research in Learning Technolog y 26, 1–5. Babich, N. 2019. How VR in Education Will Change How We Learn and Teach. September. 19, 2019. https://xd​.adobe​.com​/ideas​/principles​/emerging​-technology​/virtual​-reality​ -will​-change​-learn​-teach/

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Bavelier, D. & Saunders, S. 2021. Could Playing Video Games Impact Learning Ability. A Bold Podcast. April 16, 2021. https://bold​.expert​/the​-bold​-podcast​-episode​-ten/​?utm​ _source​=BOLD+-​+Blog​+on​+Learning​+and​+Development+-​+Newsletter​& utm​ _campaign​=ceaa514941- ​& utm ​_ medium​=email​& utm ​_ t​​erm​= 0​​_ b809​​47e32​​a​- cea​​ a5149​​41​-24​​51204​​48 Bodekaer, M. 2016. This Virtual Lab Will Revolutionize Science Class. June 1, 2016, TEDTalk. https://www​.youtube​.com​/watch​?v​=iF5​-aDJOr6U​&t​=686s Bonasio, A. 2019. Immersive Experiences in Education, New Places and Spaces for Learning. Whitepaper, Microsoft. Brand, J. and Kinash, S. 2013. Crafting Minds in Minecraft. Education Technolog y Solutions 55, 56–58. Class VR. 2019. A Guide To VR & AR In Education, AR & VR Whitepaper. Class VR.2019. 50 Creative Ways to Use ClassVR, Sharing Best Practice, Virtual Reality for Schools. Class VR. 2020. Research in The Use of Virtual Reality Learning, Virtual Reality for Schools. Edwin. 2021. Virtual Reality (VR) in Education. Robots​.net​. October, 2019. https://robots​ .net​/it​/vr​-in​-education/ ErasmusX. 2020. The Campus You Know, Is Now Virtual. Introducing the Virtual Campus. Erasmus University Rotterdam. July 7. https://virtualerasmus​.com Global Virtual Reality Market in Education Sector 2020–2024. BusinessWire. March, 2019. https://www​.businesswire​.com​/news​/home​/20200319005519​/en​/Globa... Herrera, F., Bailenson, J., Weisz, E., Ogle, E. and Zaki, J. 2018. Building Long-Term Empathy: A Large-Scale Comparison of Traditional and Virtual Reality PerspectiveTaking. PloS One 13(10), e0204494. Hu-Au, E. and Lee, J.J. 2017. Virtual Reality in Education: A Tool for Learning in the Experience Age. International Journal of Innovation in Education 4(4), 215–226. Johnson, J., 2021, Using Roblox in the Classroom: Teachers Share Advice. January 23, 2021. https://blog​.roblox​.com​/2021​/01​/using​-roblox​-classroom​-teachers​-share​-advice/ Kuhn, J. 2017. Minecraft: Education Edition. Calico Journal 35(2), 214–223. Labster. 2021. How Instructors Are Planning To Use Labster. March, 2021. www​.labster​.com Markowitz, D. M., Laha, R., Perone, B. P., Pea, R. D., & Bailenson, J. N. 2018. Immersive Virtual Reality Field Trips Facilitate Learning about Climate Change.  Frontiers in Psycholog y 9, 2364. MindCET. 2016. Virtual Reality Promise: A Contemporary Version of the “Emperor’s New Clothes”? Ed. Waismann, C. https://www​.mindcet​.org​/uploads​/2016​/09​/VREducation​.pdf Montgomery, B. 2016. Stanford Experiments with Virtual Reality, Social-Emotional Learning and Oculus Rift. Edsurge, August 16, 2016. https://www​.edsurge​.com​/news​ /2016​- 08​-16​-stanford​- experiments​-with​-virtual​-reality​-social​- emotional​-learning​ -and​-oculus​-rift Mordor Intelligence. 2020. Gaming Market – Growth, Trends, COVID-19 Impact and Forecasts (2021–2026). https://www​.mordorintelligence​.com​/industry​-reports​/global​-games​ -market Mordor Intelligence. 2020. Global Virtual Reality in Gaming Market – Growth, Trends, COVID19 Impact and Forecasts (2021–2026). https://www​.mordorintelligence​.com​/industry​ -reports​/virtual​-reality​-in​-gaming​-market Noonoo, S. 2019. Can Virtual Simulations Teach a Human Skill Like Empathy?. EdSurge, August 5, 2019. https://www​.edsurge​.com​/news​/2019​- 08​- 05​-can​-virtual​-simulations​ -teach​-a​-human​-skill​-like​-empathy

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Petrov, A. 2014. Using Minecraft in Education: A Qualitative Study on Benefits and Challenges of Game-based Education. Pottle, J. 2019. Virtual Reality and the Transformation of Medical Education. Future Healthcare Journal 6(3), 181. Reynard, R. 2017. The IMPACT of Virtual Reality on Learning. Campus Technology. April 26, 2017. https://campustechnology​.com​/articles​/2017​/04​/26​/the​-impact​-of​-virtua... Roblox Wiki, Roblox Education. https://roblox​.fandom​.com​/wiki​/Roblox ​_ Education, https://roblox​.fandom​.com​/wiki​/Roblox ​_ Education, accessed May 2021 Sanchez, E., Young, S. and Jouneau-Sion, C. 2017. Classcraft: From Gamification to Ludicization of Classroom Management. Education and Information Technologies 22(2), 497–513. Short, D. 2012. Teaching Scientific Concepts Using a Virtual World: Minecraft. Teaching Science 58(3), 55–58. Slovak, P., Salen, K., Ta, S. and Fitzpatrick, G. 2018. Mediating Conflicts in Minecraft: Empowering Learning in Online Multiplayer Games’. Proceedings of the CHI Conference on Human Factors in Computing Systems, pp. 1–13. Soffel, J. 2016. What Are the 21st Century Skills Every Student Needs?. World Economic Forum. March 16, 2010. https://www​.weforum​.org​/agenda​/2016​/03​/21st​-century​ -skills​-future​-jobs​-students/ Southgate, E. 2020. Virtual Reality in Curriculum and Pedagog y: Evidence from Secondary Classrooms. Routledge: Taylor and Francis Group. Vlasova, H. 2020. The Future of VR & AR in Education. Getting Smart. September, 2020. https://www​.gettingsmart​.com​/2020​/09​/the​-future​-of​-vr​-ar​-in​-education/

Chapter 9 EXPERIENCING DIGITAL STORYTELLING Khaldoun Dia-Eddine

Introduction The combination of globalization, demographic changes and technological advances together with environmental changes is impacting all what man intends to do; it influences the societal environment and creates new demands and needs. That requires specific skills and good preparation. Education and innovation in education are cornerstones to respond to the changes and redeem the skills’ gaps created through these changes. The changes in education require new pedagogical approaches. This chapter will focus on one such approach: digital storytelling (DST). After giving some backgrounds (Part I) and a discussion on DST (Part II), four different examples of digital story telling are presented (Part III) followed by some conclusions. The experiences have demonstrated a strong interest of students in DST-methods as an alternative to traditional lecturing. The students were better motivated to question, comment, search and to interact. The evaluation of the experiences opens the door for some recommendations and possible improvements as well as the evaluation of the efforts linked to their implementations.

PART I. BACKGROUNDS

Methodology and Limitations The paper is based on an extensive literature review which will explain the theoretical bases of the study and the logic behind it.

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The experiences were developed and conducted by the author in his classes. Some of them—at earlier stages—were in presence form, the others were conducted later in online form (due to the Covid-19 pandemic). The classes concerned different topics in bachelor a nd master classes. The students’ feedback and the author’s self-reflection about this experience are given in the final recommendations of this chapter. Within the frame of this chapter, it is not possible to present all the details supporting the research; they are part of other publications. Please note that the experience is not representative in term of the numbers of participants and the number of repetitions and hence, the results are not definitive. Other important limitations were the lack of time, available material and technical skills to develop the stories and make the video clips. Three experiences (stories I, II and IV) were conducted on the base of existing recorded material. The story III is based on a commercial film.

The Demand for New Educational Approaches As technology, political, demographical, social and environmental changes are impacting us and continue to permeate not only our personal but also our professional lives, societies, companies and individuals are struggling to adapt in an increasingly moving digital economy. These changes have created a new environment, new expectations and new demands. They create volatility, uncertainty, complexity and ambiguity known as VUCA (Benette, & Lemoine, 2014). Staying at the top of performance requires wide efforts to support the development of skills, knowledge, and experiences. We must all be learning every day, and this learning must be integrated in societal and organizations’ cultures. Education today offers new approaches to the societies and to the economy. New tendencies like technological innovation, social innovation, risk analysis and evaluation, project management, environmental integration, entrepreneurship and social entrepreneurship are new fields o r re-invented fields. The lack of skills is mentioned by many researchers as an essential barrier to respond to the demands of these changing factors ( Jónasson, 2016). A model showing that was created and presented (DiaEddine, 2020).

Drivers for Change Without going into the details, we may summarize the drivers for change in the education system in the following aspects:

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(a) Accelerated technological development

The fast-technological development in the last decades took several forms (among others): the miniaturization translated in the huge increase of the number of operations carried out per second (FLOPS), the access and use of always more complex mobile devices in general, (Manyika, 2017) the huge development and the spillover of ICT into other domains, the improvement of the efficiency of the used systems in term of energy consumption or development speed. The decrease of the price of most public goods (consumables, appliances, clothes, toys, etc.) pushed for more consumption and more demands for raw materials, while it increased the prices of goods and services such as education, childcare, medical care and housing. This difference between prices and the other factors created new and faster demands for education in all domains. (b) Demographic changes

The major challenges due to the demographic changes are (Vandemoortele, 2012): ●

●

●

●

●

The change in the absolute number of new jobs and working places with additional skills. The disproportionate size of segments of population (youth, aging), leading to demands for extended working life and hence perpetual education. The new spatial distribution of the population (international and internal migration, concentration in urban centers vs. depletion in the rural areas), this requires an adaptation of the education to respond to new skills and working conditions. The inequality and gap between the richest and the poorest on the earth is widening (World Inequality Report, 2018). This will push for more demand for better education to redeem the differences. The growing educational gap in completing primary school and other forms of education between countries (WIDE, 2019). Closing the gap in global education is the key to global prosperity, safety and stability.

(c) Challenges of globalization

Globalization is characterized by the opening of international borders to increasingly fast flows of goods, services, finance, people and ideas; and the changes in institutions and policies at national and international levels that facilitate or promote such flows (WHO, 2019).

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Parts of the challenges posed by the globalization can be contained through better and continuous education ( John, 1998).

The Drivers, the Consumers and the Entrepreneurs At the heart of these changes, we have—in a simple form—several players: the entrepreneur, the developer and the consumer. Entrepreneurship needs to be prepared through the introduction of entrepreneurial education parallel to the technical education (Sulphey and Alkahtani, 2017). The consumer is influenced by the changes mentioned above. This consumer should be prepared to use these technologies and be able to benefit from (consume) them. This requires an adapted education. The scale of the problem of finding the right employee and developer varies from country to country according to recent data from the OECD but all studies indicate that skill shortages are a major issue for CEOs (PWC, 2014).

Education for Transformation The above-mentioned challenges demand changes in the aims of education and in its content (PWC, 2014). Education’s development evolves at a slow pace, and this applies to their form, operation and content (PWC, 2014). The demand of the learners changed too. The Joint Information Systems Committee ( JISC) reports that digital learners rarely describe e-learning as a separate or special activity and indicates that technology plays a big role in life and learning (El Abaikan Reem, 2012). In fact, most of the students are confident with the use of the computers and other technologies (Littlejohn and Pegler, 2007). This is a challenge to the designers and lecturers: “due to the constantly changing nature of technology, finding an appropriate balance between innovation and production will be a constant challenge for those designing blended learning systems” (Bonk and Graham, 2012). Looking at social reality and learning from the individual’s perspective, an interesting challenge to education is the distraction due to the intensive use of digital technologies. (Kusnekoff, Munz and Titsworth, 2015). A phenomenological study concluded that the use of social media had become a prominent aspect of university students’ academic experiences (Flanigan and Babchuk, 2015) both inside and outside of the classroom setting. Such new practices and learning contexts would not be possible without using adequate digital devices for learning-by-doing, tailored to the intended learning effects (Schröder and Krüger, 2019).

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Digital technology can facilitate (OECD, 2016). Innovative pedagogic models: Gaming, Simulations, Real-time formative assessment, Skills-based assessments and E-learning aimed at autonomous learners. PART II INTRODUCTION TO DIGITAL STORYTELLING

Digital Storytelling Storytelling is culturally universal, it is likely the oldest form of teaching, allowing generations of humans to share cultural knowledge to be remembered over time. Stories typically recount a sequence of events in which one or more protagonists interact with their world, often confronting and attempting to resolve problems along the way. The human capacity for intersubjectivity allows the audience of a story to build shared meaning even from distal events and others’ experiences (Landrum, Brakke and McCarthy, 2019). Storytelling works well as a pedagogical approach due to its concreteness, specificity and narrative organization; this is also due to engaging the receiver in the process of sensemaking (Finkel, 2000). The use of stories has many advantages like the use of other types of memories (Willingham, 2009) and the capacity of processing lived experiences sequentially in scripts (Hazel, 2008). Storytelling is also motivating interest of learning, controlling students’ behavioral problems, resistance and anxiety and it is building strong relationship between students and teachers (Hytti and O’Gorman, 2004). Because storytelling is contextual (Fina and Georgakopoulou, 2019), students will be aware of what they have learned and the benefits that they can gain (Dewi et al., 2018). Storytelling allows an easier comprehension of the material presented (Willingham, 2009). Studies show better results when stories are used in the education: better memory for words (Bower and Clark, 1969) and clear enhancement of the recall of expository text (Graesser et al., 1980) significantly increase students’ exam performance (Gunther, 2011) while improving students’ ethical behavior (Swanson, 2016). From a teaching perspective, the purpose of stories is to (a) create interest, (b) provide a structure for remembering course material, (c) share information in a familiar and accessible form and (d) create a more personal student– teacher connection (Green and Brock, 2000). Stories can be used in many ways to support teaching and learning. They may be generated by the instructor or the students, or they may be adopted as texts written by other authors. The narratives may take many forms, from traditional oral or written narratives to those using relatively new technologies of digital storytelling or data visualizations (Landrum et al., 2019).

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With the advancement of technology, storytelling has become more effective in terms of functionality (Nazuk et al., 2015). Porter (2004) defined digital storytelling as combining authentic stories with image, music, graphs and voice-over, while Dupain and Maguire described it as creating a story by integrating multimedia elements such as visuals, audio, video and animation. According to them, there are many genres embedded in digital storytelling: the video game, interactive cinema, virtual reality, web-based narratives and interactive TV (Dupain and Maguire, 2005). DST can create a separate subgenre that lies somewhere between a TV documentary, a report or personal videos and the more traditional modes of oral and written narratives (Handler-Miller, 2020). Like the traditional stories, digital stories can have the objective of being informative, being instructive, or give personal narration (Robin, 2015). DST can recount historical events from many different fields ranging from social science to physical science (Coutinho, 2010; Robin, 2006). Several studies have linked education by digital storytelling to today’s students. Some defined characteristics of the “NET-generation” and addressed how educators can tailor their teaching strategies to match the characteristics of these learners (Berk, 2009; Robin, 2016). There are endless principles and approaches to crafting stories, depending on purpose and audience. The principles of Porter used for our framework are (Porter, 2019): ● ● ●

● ● ●

Living Inside Your Story (identification). Unfolding Lessons Learned (outcomes). Developing Creative Tension (emotionalization) including unusual or memorable content (Einstein et al., 1989). Economizing the Story Told (shortest path and setup). Showing Not Telling (visualization). Developing Craftsmanship (scenography).

Other researchers defined steps and elements for creating a DST along similar lines (Gregori-Signes, 2008). The e-Learning Digital Storytelling (eLDiSt) framework comprises several Digital Storytelling Aspects (DSAs) divided into four categories: story, learning, digital creation and combined aspects (Smeda et al., 2010, 2012). In our applications we are going to use the definitions, principles, elements and objectives mentioned above and create our framework based on the eLDiST. The evaluation of the stories and their impacts are extracted from the different elements mentioned above, they are going to be used later.

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For the evaluation of the experiences, we are going to use a spider chart to summarize the results. This type of chart is used for representing multidimensional data, in a two-dimensional chart.

PART III STORIES

Introduction To start with: do the examples applied here represent exactly the digital storytelling format described earlier? There are certainly differences between the forms of DST described by Robin or Dupain and Maguire due to the uniqueness of each story, of the storyteller style, the different audiences, and channels.​ We present four stories with different approaches as follows:​

Story I Material received by the students: the used slides, pre-reading material, short resumé of the hero of the story in addition to instruction for using the sequences and Q&A session. The structure of the lecture: sequence I, quiz, sequence II, quiz, sequence N, quiz and class discussion.

Story II Material received by the students: the used slides, pre-reading material, short resumé of the hero of the story and Q&A session. The structure of the lecture: automated sequence of slides and then class discussion. The form of each slides used (figure 9.1).

Story III Material received by the students: The above-mentioned PPs, pre-reading material in the form of press articles and journal papers, class discussion about each clip and Q&A session. The structure of the lecture: Presentation of the whole negotiation, possibility of watching the whole movie, then the clips were used when adequate as application to theoretical explanations. Each clip was followed by class discussion and analysis based on learned models.

lecturer as author, invented details based on historical facts Recorded and prepared in studio with PP included within

Hero’s/narrator’s self experience

Story II

Recorded in class (guest lecturer) and treated using Movie Maker and embedded in PP presentation Actual form of 2 video clips of 17 video clips of ca. 3 minutes DST and 33 minutes each. each embedded within PP length Used in: 4 X 50 min presentation. Used in 2X 50 min Narrator Lecturer Hero Hero’s presence Not present Directly present Required Read short introduction Followed the theoretical part(s) of Preparations about hero’s life the course Introduction to the conflict zone and timeline Post-storytelling Quiz + Q&A with Quiz + Discussions + evaluation interactions lecturer + evaluation Educational aim Explain Arab cultural Explain intermediation in a identity and conflict introduction to Islam International negotiations, Used in the Cross-cultural course management, Doing Conflict and intermediation, War, economics and business business in ME, International businessregional focus

Origin of the visual production

Base of the story and plot

Story I

Table 9.1 Overview of the DST conducted experiences.

International negotiations

Explain complex negotiations

Discussions + evaluation

Recorded in class (guest lecturer) treated using Movie Maker and embedded in PP presentation

Commercial movie treated using Movie Maker and embedded in PP presentation 10 video clips of ca. 4.5 minutes each within PP presentation, each slide introducing one clip Actors Indirectly present Introduction of the crisis, the major players and the negotiation timeline

Doing business in ME, War, economics and business

Hero /author Directly present Followed the theoretical part(s) of the course Introduction to the conflict zone and timeline Quiz + Q&A + Alternative scenarios + evaluation Explain doing business in zone of conflict

4 video clips of 12 minutes each starting in row automatically. Used in 2X 50 min

Hero’s/narrator’s book of self-experience

Story IV

Hero’s book

Story III

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Figure 9.1 Slide’s division for Story II experience.

Story IV Material received by the students: Pre-reading and introductory material about the case in the form of reports and press articles, short resumé of the hero of the story and Q&A session. The structure of the lecture: Introduction to the case, projection of the prepared movie and class discussion.

Results After conducting the experience few times, the weighted and standardized consolidated evaluation (grading scale from 1–5) the results were:​ The analysis of the spider chart indicates: 1. Certain weakness does exist in the narration form, this is due to historical, geographical and cultural distances. This may be improved through better introduction through a dedicated activity and adding some music or special sound effects. A better recording could also improve the impact. 2. Large differences of feedbacks among the stories: The plot, the dramatic question, the emotional content and the digital elements used. These are

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Figure 9.2 Results of conducting the four DST experiences.

due to the unique nature of each topic and the differences in the raw material used, since they were recorded before the idea of using them for educational purpose. 3. Positive and united feedbacks related to the language used, the level of response to the expectations and the learning effect of the experiences, the retention effect and the impact of ethical issues. This is due -in our opinion- to the type of the activity, that is storytelling, it is a confirmation of the earlier theoretical explanations. In our opinion the good level of expectation is due to the right introduction and preparation before the presentation of the stories. Some other points mentioned during the feedbacks are grouped here below and they were related to: ● ● ● ●

Audio quality (noise and absence of special effects). Video sharpness and stabilization. Framing and zooming effects. The switch between narrator and slides.

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Finally, the script in term of relation between the narrator and the students and the participation of the students. It may be wise to link some pre-lecture activities to the total script of the story, like searching for old caravan routes through Arabic peninsula, economic situation of Syria before the civil war. Some enhancements were proposed: to improve the learning effect. Reduce the quantity of information on the slides (especially in story II) to reduce the fragmentation of the students’ attention. Also, to let students make some introductory works as part of the story, this should be combined with showing the benefit of the sequence of the lecture (i.e., the use of a quiz). PART IV CONCLUSIONS

The feedback showed that students benefited from the experience; they expressed that in different ways and they are asking for more such experiences. The experience shows that there is a good interest in this way of teaching and a certain degree of freedom in using it. It offered the possibility of having it in presence as well as in distance lecturing, it offered the possibility of watching later. These are two elements which are part of the requirements for an adapted education. The other one is the use of digital means as platform and the possibility to have very short sequences with an electronic interaction. As said earlier, the discussions after watching the stories were very appreciated and permitted a better and more focused questions. That said, it would be possible to increase the level of attention and of interaction with the students through motivating students to participate to the construction of the stories and through testing the learning results. One negative aspect is the heavy engagement of resources (time, tools and equipment as well as people) related to preparation, testing and implementing. This experience should be made for topics which can be re-used in the same course or other courses too. Further trials are needed to continue the analysis of the results and effects of using these experiences. This should be linked with a track record of the improvements and their effect to repeat them in further material. Some further research should look at the long-term impact of this method in term of retention of information or as impact on the adoption of new values as well as the motivation for further and deeper research about the stories presented.

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John, W. S. 1998. Carnegie Council for Ethics in International Affairs, 1998, Challenges of Globalization Human Rights Dialogue 1.11 (Summer 1998) ‘Toward a “Social Foreign Policy” With Asia’. Jónasson, J. T. 2016. ‘Educational change, inertia and potential futures’. European Journal of Futures Research 4, No. 1: 1–14. https://doi​.org​/10​.1007​/s40309​- 016​- 0087​-z. Kusnekoff, J., Munz, S., & Titsworth, S. 2015. Mobile phones in the classroom: Examining the effects of texting, twitter, and message content on student learning. Communication Education 64, No. 3: 344–365. https://doi​.org​/10​.1080​/03634523​.2015​.1038727. Landrum, R. E., Brakke, K., & McCarthy, M. A. 2019. ‘The pedagogical power of storytelling’. Scholarship of Teaching and Learning in Psycholog y 5, No. 3: 247–253. https:// doi​.org​/10​.1037​/stl0000152. Littlejohn, A., & Pegler, C. 2007. Preparing for Blended e-Learning. Abingdon, Oxfordshire: Routledge. Manyika, J. 2017. ‘Technology, jobs and the future of work’. Boston, USA: McKinsey Global Institute. Nazuk, A., Khan, F., Munir, J., Anwar, S., Raza, S. M., & Cheema, U. A. 2015. ‘Use of digital storytelling as a teaching tool at national university of science and technology’. Bulletin of Education and Research 37, No. 1: 1–26. OECD. 2016. Innovating Education and Educating for Innovation: The Power of Digital Technologies and Skills. Educational Research and Innovation. Paris: OECD Publishing. https://doi​.org​ /10​.1787​/9789264265097​-en. Porter, B. 2004. Digitales: The Art of Telling Digital Stories. Bernajean Porter. Porter, B. 2019. ‘The art of digital storytelling, part I: Becoming 21st-century story keepers’. www​.tech4learning​.com. Retrieved October 2019. PWC. 2014. ‘17th annual global CEO survey: The talent challenge’. https://www​ .pwc​.com​/gx ​/en​/ hr​-management​- services​/publications​/assets​/ceosurvey​-talent​ -challenge​.pdf. Retrieved November 2019. Robin, B. 2006. ‘The educational uses of digital storytelling’. In: Society for Information Technolog y & Teacher Education International Conference, pp. 709–716. Barcelona, Spain: Association for the Advancement of Computing in Education (AACE). Robin, B. R. 2015. ‘The effective uses of digital storytelling as a teaching and learning tool’. In: Handbook of Research on Teaching Literacy Through the Communicative and Visual Arts 2, pp. 457–468. Barcelona, Spain: Routledge. Robin, B. R. 2016. ‘The power of digital storytelling to support teaching and learning’. Digital Education Review 30: 17–29. Schröder, A., & Krüger, D. 2019. ‘Social Innovation as a Driver for New Educational Practices: Modernising, Repairing and Transforming the Education System’. Sustainability Journal 2019, 11, 1070; doi:10.3390/su11041070. Basel, Switzerland, mdpi. Sulphey, M. M., & Alkahtani, N. 2017. ‘Economic security and sustainability through social entrepreneurship: The current Saudi scenario’. Journal of Security and Sustainability Issues 6, No. 3: 479–490. Smeda, N., Dakich, E., & Sharda, N. 2012. ‘Digital storytelling with Web 2.0 tools for collaborative learning’. In: Collaborative Learning 2.0: Open Educational Resources, pp. 145–163. Hershey, Pennsylvania, USA: IGI Global. Sulphey, M. M., & Alkahtani, N. 2017. ‘Economic security and sustainability through social entrepreneurship: The current Saudi scenario’. Journal of Security and Sustainability Issues 6, No. 3: 479–490. Swanson, D. 2016. ‘Fictional stories with ethical content: Guidelines for using stories to improve ethical behavior’. Ethics & Behavior 26, No. 7: 545–561. http://dx​.doi​.org​/10​ .1080​/10508422​.2015​.1081095.

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Vandemoortele, J. 2012. Advancing the Global Development Agenda Post-2015: Some Thoughts, Ideas and Practical Suggestions. New York: UN System Task Team on the Post-2015, UN Development Agenda. WHO. ‘Definition of globalization’. https://www​.who​.int​/topics​/globalization​/en/. Retrieved November 2019. Willingham, D. T. 2009. Why Don’t Students Like School? San Francisco, CA: Jossey-Bass. World Inequality Lab. 2018. World Inequality Report, 2018. World Wealth and Income Database, www. WID.world, downloaded Nov. 2019.

Chapter 10 LEARNING BY GAMING IN MANAGEMENT Evgeniya Kaz and Evgeniya Nekhoda

Gamification: Pro and Contra In one of the episodes of the novel “The Adventures of Tom Sawyer”, Twain (1876) describes how Tom, the main character of the book is forced to whitewash a fence thirty yards long, as punishment for pranks and deceit. Naturally, he is very upset about it. “He surveyed the fence, and all gladness left him and a deep melancholy settled down upon his spirit. Thirty yards of board fence nine feet high. Life to him seemed hollow, and existence but a burden (Twain, 1876, 26).” Thus, Tom decides to tell his friends that whitewashing the fence is not a punishment, but a high privilege. “Tom swept his brush daintily back and forth — stepped back to note the effect — added a touch here and there — criticized the effect again (Twain, 1876, 30).” When one of his friends (Ben Rogers) saw how enthusiastically Tom was whitewashing the fence, he asked to let him participate. Tom, rejoicing in his soul, refused, declaring that this high responsibility is his. Then Ben gave Tom an apple, if only he would allow him to help whitewashing the fence. The same thing happened to other boys, who came up later and who also took part. Thus, Tom Sawyer was able to turn a monotonous workflow into an enthusiastic task, a game. According to modern jargon, he applied gamification. The term Gamification was coined as a term only a few years ago. Nick Pelling, a British-born computer programmer and inventor, defined the term as: “Apply game-like accelerated user interface design to make electronic transactions more enjoyable and faster (Pelling, 2011, vol.9).” Yu-Kai Chou, a leading gamification expert and author of “ Actionable Gamification”, defines gamification as: “The craft of deriving all the fun and addicting elements found in games and applying them to real-world or productive activities (Chou, 2012).” Subsequently, the concept of gamification began to be

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actively used in educational practice. For example, Kevin Werbach, professor at the Wharton School, University of Pennsylvania, defines gamification as: “An application of game elements and digital game design techniques to non-game problems, such as business and social impact challenges (Werbach and Hunter, 2012).” In short, on may say that gamification means: making a game of it. It should be emphasized that the episode “Tom Sawyer and the Whitewashing of the Fence” is about gamification, not about a business game. While business games imitate the decision-making process by managers in management situations, gamification is non-imitative; it keeps the content of the activity unchanged (e.g., whitewashing a fence), but changes the way in which this activity is organized. In other words, gamification, by contrast with a business game, takes place in the real world, not in a game situation. As noted by O.V. Orlova, V.N. Titova: “Reality remains reality, without turning it into a game, and play attitudes are introduced into the system of the subject’s operations with this reality (Orlova and Titova, 2015, 60–64).” The main goal of entertaining games is to have fun. In contrast, the goal of gamification is to encourage people to carry out certain behaviors by applying game mechanics to non-game entities or bringing game elements into the current business process. Another concept close to gamification is a computer simulation, which creates the illusion of reality in a computer environment and serves educational and training purposes (learning to drive, working with complex equipment, etc.). Gamification, in contrast to simulation, creates t he i llusion of a game using the mechanics of the computer environment in the real world.

Game Over? Even though the concept of gamification was developed only recently, it is becoming increasingly popular. At the same time, interest in classic business games is on the decline. Thus, if we compare the interest for the search queries “business games” and gamification in the Google search network, we come to the following conclusions: interest in the concept of gamification was just emerging in 2013 (the specified search query is indicated by a rising curve and does not exceed 25 points, Fig. 10.1). Then, the interest in both concepts was approximately the same starting from the end of 2013. In the next four years (at the level of 50 points), however, since 2018, there has been a sharp increase in gamification with a simultaneous decrease in business games (the dynamics of popularity for the first concept was 93 points and 22 for the second, respectively in 2020, Fig. 10.1). It’s worth noting that Google Trends presents the ratio of the total number of search queries for a

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Figure 10.1 Comparison of the dynamics of the popularity of the interest to “business games” and gamification around the world for the period from June 2009 to April 2021.

certain word or phrase on each day of the selected period to the maximum number of search queries for the same word or phrase (in a certain region for a selected period). The results are then converted to a 100-point scale. Thus, the largest indicator is assigned 100 points. The rest of the values are compared with the largest indicator, and they are taken equal to a number from zero to 99. We observe similar dynamics in Russia. From 2018 to the present time, the frequency for the query gamification is twice higher than that for the query business games (queries were analyzed in the Russian language). Do the identified trends indicate that business games are becoming a less popular tool in the educational process?

Business Game Model What is the reason for the decreased trend in the number of queries for the keyword business game and, on the contrary, high search activity results in relation to the concept of gamification presented above? Perhaps this is due to the lack of understanding by many Internet users of the differences between the two concepts, and therefore the incorrect appeal to the trendy term, although in fact, they may have meant a business game. This assumption seems feasible, because these concepts are often confused by the authors of numerous publications on the topic of gamification. Thus, for example, Varenina (2014, vol.6), Akchelov and Galanina (2019, 117–132), Elagina and Pisklakov (2014, 22–27) and many others erroneously classify business games with of the imitation kind to the concept of gamification. This is explained by the fact that in the literature concerning business games, there is still no agreement on both the structure of a business game, its design principles, and the methods of conducting them. In most cases the

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authors proceed from their empirical experience, common sense, or adopt the corresponding components from other authors. We believe that the reliance in the construction of business games on the principles of structuralism, which has spread far beyond the boundaries of philosophical constructions, will make it possible to change the situation that has developed in treatment of business games. In an attempt to resolve the issue mentioned above, we have referred to V. Propp’s Morphology of the Folk Tale (Propp, 1928), where a Russian philologist, based on the analysis of 2,500 plots of Russian fairy tales, revealed that they were constructed in a similar way as a combination of 32 actions-functions (disappearance, wedding, violation of interdiction, etc.) and seven characters (the villain, the dispatcher, the helper, the princess or prize; the donor, the hero and the false hero). V. Propp’s monograph was ahead of the development of structuralism as an independent trend in scientific thought for many decades. His research and studies of numerous supporters of structuralism made it possible not only to clarify the mechanisms of folklore, but also the general mechanisms of the work of human consciousness, how it constructs the objects and texts. C. Levi-Strauss, social anthropologist and leading exponent of structuralism, revealed that in the formation of the plots of myths, the technique denoted by the term bricolage is often used. In his book The Savage Mind, he notes that bricolage is the imparting of new meaning to material objects and ideas through new, unusual use of them or a combination of them with each other that has not been encountered before (Lévi-Strauss, 1966). The famous anthropologist emphasizes that objects taken out of one context and moved to another can take on a completely different meaning. Both principles of structuralism, such as identifying repetitive structures and placing an object in an unusual context, are applicable in business games. This, in our opinion, allows for expanding their prospects. In this chapter, we will also rely on gamification research, the authors of which, as will be explained below, explicitly or implicitly rely on the principles of structuralism. According to Kevin Werbach, a professor at the University of Pennsylvania and an expert in the application of game mechanics in business and educational environment, there are three groups of structures used in gamification: dynamics, mechanics and components (Werbach and Hunter, 2015). “Dynamics are the big-picture aspects of the gamified system that you have to consider and manage” (Werbach and Hunter, 2015). Werbach refers to the most important game dynamics such as constraints, emotions, narrative, progression and relationships. “Mechanics are the basic processes that drive the action forward and generate player engagement” (Werbach and Hunter, 2015). Werbach identifies ten important game mechanics such as challenges, chance, competition,

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cooperation, feedback, resource acquisition, rewards, transactions, turns and win states. Each mechanic is a way to achieve one or more of the dynamics described above. For example, a random event, such as an unexpected reward, can stimulate certain emotions. “Components are the specific instantiations of mechanics and dynamics” (Werbsch and Hunter, 2015, 11). The author identifies fifteen important game components such as achievements, avatars, badges, boss fights, collections, combat, content unlocking, gifting, leaderboards, levels, points, quests, social graph, teams and virtual goods. Other authors, following the same line of thinking, argue that game mechanics can work effectively if they consist of only four elements: reward; competition; a sense of achievement and feedback (Iraidina, 2020). We have identified several analogies that can be observed in the structures of a folk tale and, for example, in the classification of K. Werbach (Table 10.1). We have followed this useful analogy and placed the “elements of the gamified space” as identified by Werbach in the context of business games (bricolage). This will allow us to propose a classification of the main elements of a business game (arranged in the order of their participation in the development of a business game): Dynamics: 1. Identify the audience and the context. At this stage, it is important to determine which course topics can be placed in the game context. At the same time, the game context should contribute to a deeper understanding of the studied material (narrative and progression). Hereinafter, italics denote the name of the corresponding element in the terminology of K. Werbach. 2. Prepare a list of recommended reading, video materials on the studied topic, which students will study themselves in the process of completing the assignment (narrative). 3. Define the roles of the players (relationships). 4. Identify competing and cooperating players (relationships).

Table 10.1 Correspondence of some elements of a folk tale and gamified space.

Elements of the folk tale by V. Propp

Elements gamification space by K. Werbach

Interdiction Struggle Difficult task Victory

Constraints Competition, boss fights and combat Challenges Win states and leaderboards

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Mechanics: 1. Describe the instructions for each participant (challenges). 2. Develop the individual and team reward systems (rewards). 3. Choose the technologies for the interaction of students in the process of completing the assignment (cooperation). Components: 1. Choose game components that will encourage the student to complete the assignment (achievements, points, etc.). In K. Werbach’s typology, an element such as progress is present in all groups of game elements (it is represented by the progression concept at the level of dynamics, by the feedback concept at the level of mechanics, and by the achievements, levels and points concepts at the level of components). In our opinion, in the educational process at the level of mechanics and components, this category should include not only the form of an individual assessment of knowledge and skills acquired in the field of the studied subject, but also the quality of the completed assignment, as well as an assessment of the student satisfaction’s degree with their participation in the business game. As will be shown below, the implementation of Werbach’s typology into the field of business games simplifies the process of creating business games, increases the variety and, ultimately, breathes a second life into this, of course, a useful educational product, the interest in which, unfortunately, gradually decreasing.

Business Games in Management: When an Academic Course Becomes a Quest The period of the pandemic, which began in 2019, forced universities to organize classes in online format. The management course for first year bachelor students of the Institute of Economics and Management at Tomsk State University was no exception. The main goal of the course is to make students understand the role of a manager, his main functions, and to develop the decision-making skills in the field of management. In the situation described above, in which students were not able to visit real companies, the interaction between students was limited, we decided to develop and introduce a business game that promotes the development of students’ managerial skills in an artificially created situation that compensates all the main features of real team management. Thus, we developed the business game “Learning by Managing” to study the following topics: “Functions of Management”, “Managing Remote Teams” and “Analysis of Organizational Culture”.

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When developing a business game, we relied on the general principles of structuralism and the practice of its use in gamification. In particular, we used three important elements of gamification (dynamics, mechanics and components). As for the dynamics (relationships element), the class was divided into 2 competing teams. In each of the teams the students were offered the following roles: 1. 2. 3. 4.

Manager (one person); Technical specialist (two persons); HR specialist (one person) and Ordinary employees (the other members of the team).

Each team member, in accordance with his role, was offered an individual assignment which they had to complete in the process of working on a joint project (mechanics). In addition, the students were sent a list of recommended reading, video materials and so on, on the relevant topic. Since gamification is the application of game elements to real-world or productive activities, then in the “Learning by Managing” business game the real activity was an analysis of organizational culture. Thus, students playing the role of “ordinary employees” had to analyze the organizational culture of their study group (according to the methodology of Cameron and Quinn (2000), in which they are studying at the Institute of Economics and Management (the composition of the groups is constant throughout all the years of undergraduate studies in the Russian Federation). Students who played the role of manager had to: 1. Plan the work of students who played the role of “ordinary employees”; 2. Control the work of “ordinary employees”; 3. Approve the motivation system for “ordinary employees” (together with the “HR specialist”) and 4. Approve software products (for example, Trello, Bitrix24, Google docs, etc.) for organizing the work of “ordinary employees” (together with the students playing the role of “technical specialist”). At the same time, the managers were forbidden to help the students, who played the role of “ordinary employee”, in the completing their assignment. The “technical specialist” had to: 1. Offer the manager software products for organizing the work of “ordinary employees”;

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2. Make the instructions for working with software products for “ordinary employees” and 3. Give advises the team members on software products on regular bases. The “HR specialist” had to: 1. Offer the manager a system of employee motivation and 2. Implement a motivation system in the team. Thus, the main work of the team had to be performed by “ordinary employees”. However, the main work of “ordinary employees” would be difficult to co mplete wi thout “m anagers”, “t echnical sp ecialists” an d th e “HR-specialist.” For two weeks students of each team were working online together on the assignment. Therefore, in the process of working on a joint project, students were able not only to try out one of the proposed organizational roles, master teamwork skills and learn how to make research reports, but they also learned the management functions in practice: the concept of hierarchy, organizational structure and culture, understood how the motivation system works in the company, and also mastered the skill of remote work.

The Results and Feedback When designing a business game, as mentioned above, it is important not only to evaluate the knowledge and skills gained in the field of the subject being studied, but also the quality of the completed assignment, the degree of student satisfaction with their participation in a business game (components). As for the first item, the knowledge gained can be assessed by an interview or test assignments on relevant topics. It is also not difficult to evaluate the quality of the group performance, since the assignment is evaluated based on the “research report” prepared by the students. However, a problem for us, as teachers, arises in respect of an individual assessment of each group of students. First, we were not members of student teams, and, secondly, the work takes place remotely, so we could not observe the process of completing the assignment in teams. Therefore, we used the 360-degree method for the individual assessment of team members. Each student was asked to assess all members of his team and to fill in a detailed report, the form of which was adapted to role they played in the team. A questionnaire survey of students conducted after the end of the business game revealed the following. When we asked: “What did you like/dislike about the business game?,” the students indicated the following: “I liked

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the opportunity to be in the role of a ‘real manager’, to be responsible for the entire work, understand that instructions and feedback are expected from you, plan the teamwork, organize the workflow, set deadlines, motivate, control and reward. Thus, I studied management and this assignment made it clear that I was not mistaken with the choice of a professional path (Alexander).” Another student said: “In the process of completing the group assignment, I liked the idea of working in a team. In some way it resembled a real workflow in one of the companies, and I was really immersed in this corporate atmosphere (Veronica).” It is worth noting, when we asked about the degree of student’s satisfaction with the business game “Learning by Managing”, the average mark was 8.9 points (on a scale from zero to ten points. 136 students took part in the survey). The feedback presented above indicates that correctly structured business games can help students not only develop the skills necessary for successful adaptation in a rapidly changing world, but also achieve comparability of the quality of relationships arising in the process of a business game to those that arise in a real company environment (the most important condition for gamification), while retaining an imitative character (an important feature of a business game). Thus, in our opinion, games that are structured on the principles of gamification, while retaining the imitative character typical for a business game, get close in terms of their nature to gamification (Fig. 10.2). Therefore, we propose to introduce a concept “G-business game” (G means gamified). It is worth noting that the computer simulation does not deal with the real subjects. It is recommended when studying some academic subjects. However, business games have more advantages for Management course. The last one

Figure 10.2 “G-business games” in the coordinate system “the main goal—the nature of game activity.”

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includes the real interaction of individuals. In our opinion, this is the very reality that interests the management of organizations.

Conclusion The development of a G-business game can significantly improve the quality of knowledge and competencies of students. However, it requires professionalism and additional efforts on the part of the teacher. The practice of conducting a G-business game “Learning by Managing” allows us to indicate several factors that a teacher, who intends to develop a “G-business game”, should pay attention to: 1. Don’t be afraid to experiment. Test different formats and game mechanics. It can be challenging to anticipate all the game elements: dynamics, mechanics and components, when developing a “G-business game.” Therefore, use more often the feedback assessment. As a result, the students will suggest what should be improved in the proposed game. 2. From play to knowledge. Don’t forget about educational goals. When creating an assignment for students, answer the question: “How can students apply their knowledge in a real practice?” 3. Fun. The pleasure of the “G-business game” can be not only in-game, but also out-of-game. The first type includes all the ways to get pleasure from playing actions during the game (cooperation, competition, creativity, etc.). The second type includes the ways to get pleasure from the game outside its framework (accumulation of experience, learning, etc.). Therefore, the teacher may try to build a “G-business game” with rewards and risks (components in the terminology of K. Werbach) that make sense and motivate the student to pass the game, level by level, approaching the ultimate goal, which is mastering the skills and knowledge of the studied discipline. In conclusion, we would like to highlight several advantages in the process of introducing a G-business game in Management academic course: 1. “G-business game” stimulates students to work independently. They tend to study much more materials to solve the assigned task than in an ordinary business game, which may not require any preliminary preparation of students. 2. “G-business game” encourages students to think nontrivially. Correctly organized problem statement stimulates students to search for nonstandard solutions. The ability to think creatively and outside the box is

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often exactly the quality that employers are looking for when hiring new specialists. 3. “G-business game” heightens students’ interest in science. It is important that the educational process is interesting and exciting. The more actively the student participates in, the more interesting it is for them to study. 4. “G-business game” prepares students for real life. This type of game makes it possible to connect theory and practice, thereby the students understand the practical aspects of their future profession. Therefore, before saying “game over”, remember the words of Albert Einstein: “Play is the highest form of research.”

References Akchelov E. O., Galanina E. V. 2019. ‘New approach to gamification in education’. Journal of Wellbeing Technologies 32, No. 1: 117–132. http://earchive​.tpu​.ru​/bitstream​ /11683​/53253​/1​/jwt​-957​.pdf. Cameron K., Quinn R. 2000. Diagnosing and Changing Organizational Culture. Pearson Education, Inc., Upper Saddle River, New Jersey. http://my​ .ilstu​ .edu/​ ~llipper​ / com435​/survey​_ocai​_culture​.pdf. Chou, Y. K. 2012. ‘What is gamification. Yukai Chou: Gamification and behavioral design’. https://yukaichou​.com​/gamification​-examples​/what​-is​-gamification/. Elagina O. B., Pisklakov P. V. 2014. ‘Gamification of distance learning’. Institute of Open and Distance Education 56, No. 4: 22–27. http://journals​.tsu​.ru​/uploads​/import​/1121​/ files​/56 ​_ 022​.pdf. Iraidina M. 2020. ‘“Skillbox media”. The game is a serious matter. Explaining what gamification is’. https://skillbox​.ru​/media​/management​/igra​_delo​_ seryeznoe​_ rasskazyvaem​_ pro​_ geymifikatsiyu/. Lévi-Strauss C. 1966. The Savage Mind. University of Chicago Press, Chicago, Illinois, p. 219. Orlova, O. V., & Titova, V. N. 2015. ‘Gamification as a way of learning organization’. Tomsk State Pedagogical University Bulletin 9: 60–64. Pelling, N. 2011. ‘The (short) prehistory of gamification’. Funding Startups (& Other Impossibilities). https://nanodome​.wordpress​.com​/2011​/08​/09​/the​-short​-prehistory​-of​ -gamification. Propp V. 1928. “Morphology of the folk tale”. State Institute of Art History. Academia Leningrad. https://web​.mit​.edu​/allanmc​/www​/propp​.pdf. Twain, M. 1876. The Adventures of Tom Sawyer. American Publishing Co, Hartford, Connecticut. https://etc.usf.edu/lit2go/34/the-adventures-of-tom-sawyer/5429/ preface/. Varenina L. P. 2014. ‘Gamification in education’. Historical and Socio-Educational Idea’s 6, No. 6, Part 2. https://cyberleninka​.ru​/article​/n​/geymifikatsiya​-v​-obrazovanii​/viewer. Werbach, K., & Hunter, D. 2012. For the Win: How Game Thinking Can Revolutionize Your Business. Wharton Digital Press, Philadelphia, p. 148. Werbach, K., & Hunter, D. 2015. The Gamification Toolkit: Dynamics, Mechanics, and Components for the Win. Wharton School Press, Philadelphia, p. 50. https://books​ .google​.ru​/books​?id​=RDAMCAAAQBAJ​&printsec​=frontcover​& hl​=ru​#v​=onepage​ &q​& f​=false.

Chapter 11 THE CHALLENGE OF ARTIFICIAL INTELLIGENCE Iaroslava Kharkova and Wayne Holmes Artificial Intelligence (AI) appears to be advancing at an ever-accelerating pace and affecting much of human life. The power of AI has already been demonstrated in various areas – from smartphone personal assistants and customer support chatbots to medical diagnoses and driverless cars. At the same time, these applications bring multiple challenges and much hyperbole. Nonetheless, of particular importance here, AI systems have also entered the classroom. However, while promising to enhance education, the design and deployment of these tools again raise particular concerns and challenges. We begin this chapter with a brief history and definition of AI outlining the evolution of AI techniques aiming to imitate or outperform human cognitive capacities. We continue by exploring what AI systems promise to deliver in educational contexts and their impact on learners, examining the interaction through the lens of three analytical categories: learning with AI, learning about AI and preparing for AI. We also explore the risks related to the introduction of AI into education and investigate transversal issues related to all three categories, noting that currently little attention has been paid to what is ethically acceptable for AI and education. Finally, we conclude by trying to answer two questions: how can we make better AI tools for education and how can education help address the challenges created by AI?

Introduction Artificial intelligence is constantly in the headlines. Almost every day, we read about another dramatic although often overhyped breakthrough, such as the use of AI to identify and counter COVID-19,1 software agents that appear capable of fluid conversations,2 or the creation of deep fake videos3. However, we know less about how AI has infiltrated our daily lives. AI helps unlock your smartphone with face ID,4 provides personalized feeds in your 6 social media,5 and monitors your whereabouts as you walk about town.

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Increasingly, while it rarely makes the headlines, AI is also being used in educational contexts, for example to automatically generate timetables,7 to adapt tutoring technologies to individual competencies,8 and to monitor whether students are concentrating in class.9 Advocates, such as developers and some researchers and policymakers, argue that the introduction of AI into classrooms enhances learning and thus de facto benefits students. Others respond that AI in classrooms de-professionalizes teachers, ignores the social dimension of teaching and learning, and automates poor pedagogic practices. Whichever view is more accurate, because education plays such a key role in developing and empowering the world’s citizens, understanding the impact of AI on education is increasingly important. Accordingly, in this chapter, we begin that task by exploring the various connections between AI and education. We start with the context: what exactly is AI?

A Brief History and Definition of AI Reflections o n i ntelligence h ave b een p art o f t he p hilosophical p roject t o understand the world since ancient times and has long been accompanied by the ambition to create an artificial intelligence (history is littered with myths about master craftsmen creating artificially i ntelligent creatures). One more recent example is the Mechanical Turk, a machine created in 1779 that was capable of independently playing a game of chess, long thought to be the pinnacle of human intelligence—although it later turned out that the machine was a fake. A man, concealed inside the machine, was the real chess player. However, the official beginning of today’s A I project is dated to 1956, when the term was coined at a conference at Dartmouth College by the mathematician John McCarthy who was interested in developing computer programs capable of thinking intelligently (McCarthy et al., 1956). Subsequent AI researchers mainly focused their efforts on two contrasting goals. While some were interested in exploring how biological or natural intelligence works, in order to replicate those mechanisms in computer programs, others were interested in creating programs that perform tasks better than humans without worrying about whether these programs actually think as humans do. These alternative goals, together with the interdisciplinary nature of AI research and the overlaps with ordinary computer programming, mean that it is difficult to give a universally accepted definition of AI. Nonetheless, it is the case that the history of AI is associated with the development of increasingly sophisticated algorithms and models that are applied to areas that require human cognition—such as visual perception, speech recognition and generation, decision-making or the general capacity to learn and perform cognitive tasks (Holmes et al., 2019).

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Since its creation, the field of AI has experienced periods of fast progress supported by abundant funding, interspersed with so-called AI winters when progress slowed and the funding dried up, because another AI technique had failed to deliver its promise to create a machine with human-level intelligence (Marcus and Ernest, 2019). The recent dramatic developments in AI began around 2010, when a sub-field of AI, machine learning, succeeded in the automatic recognition of objects (firstly, cats!) in images and videos at closer-tohuman levels. By that time, machine learning, which requires huge amounts of data to work effectively, had been researched for decades. The rapid advances were made possible thanks to significant hardware developments and the easy availability of ever-increasing amounts of data from the Internet. Inevitably, AI is complex (most AI engineers have a PhD in a related field), making its details d ifficult to ful ly understand. Nonetheless, it is wor th summarizing some of the key terminology. Until recent times, most AI research adopted a rule- or knowledge-based approach (known as classical AI). The engineers encoded knowledge in models and wrote software programs to process that knowledge. This approach led to so-called expert systems, computer software that can for example automatically diagnose failures in mechanical systems, but never achieved anything resembling human-level intelligence. On the other hand, an approach known as machine learning (which is often incorrectly assumed to be synonymous with AI), involves software, usually known as algorithms but again mostly written by humans, that typically identify patterns in huge amounts of data that can then be applied to new data. For example, a method known as supervised learning uses huge amounts of labelled data (such as photographs labelled with the names of the persons depicted), that can then automatically label new photographs (of the same people). Other machine learning techniques include unsupervised learning, self-supervised learning and reinforcement learning. The most successful machine learning approach, known as “deep learning,” is inspired by the way that neurons in animal brains work. It is one variant or another of deep learning10 that have led to the current boom in AI technologies, including radical advances in natural language processing (NLP), translation and generation, speech recognition and generation (the application of NLP to spoken words as used by AI personal assistants such as Siri11 or Alexa12), image recognition (such as facial and handwriting recognition), autonomous agents (such as game avatars and malicious software bots), affect detection (to analyze sentiment in text, behavior and faces), and artificial creativity (AI to create images, music or stories). Despite the dramatic advances, behind the hyperbole there remain multiple limitations. For example, it has been argued that the machine learning approach is fast reaching its ceiling (doubling the data tends only to improve the outcomes by a small amount),13 such that a new paradigm is now needed

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(perhaps involving a synthesis of classical AI and machine learning) (Marcus and Ernest, 2019). Another alternative is known as “augmented intelligence” in which, rather than trying to create machines that imitate human thinking, focuses on machine–human collaboration, computational thinking combined with human creativity that might help solve more complex problems (Miao and Holmes, 2021). In any case, what is known as Moravec’s paradox remains true: while AI can do many things that humans cannot do easily (such as calculate and identify patterns), it cannot do many things that humans can do easily (such as understand meaning and apply human values; Moravec, 1988). Finally, as illustrated by the Mechanical Turk, it is important not to conflate something that appears intelligent with something that is intelligent.

The Connections between AI and Education While we might imagine that the introduction of AI in education is a recent phenomenon, research about how computers could provide one-to-one tutoring first emerged in the 1970s (Guan et al., 2020). Today, the picture is more complex and the discourse often muddled, as AI is connected to education in at least three ways: ●

● ●

Learning with AI: the application of AI tools in classrooms to support teaching and learning. Learning about AI: the teaching of AI, how it works and how to create it. Preparing for AI: the teaching of what it means to live in a world increasingly impacted by AI.

Before continuing, it is important to recognize the limitations of this categorization: the differences between the three connections are neither unambiguous nor rigid, indeed they overlap in various ways. In particular, the last two may alternatively be conceived as two aspects of the same task: preparing all citizens to understand to varying degrees the technical and human aspects of AI and their implications for our future. Nonetheless, the “three connections” approach is still useful, if only because it helps ensure that certain subtleties are not forgotten.

Learning with AI Learning with AI is the connection most often mentioned in the media and by policymakers. It can usefully be further divided into system-facing AI, student-facing AI, teacher-facing AI and using AI to learn about learning. All these approaches have benefits, but many also raise serious concerns, such as those centered on data: data privacy, data security and data ownership (Holmes et al., 2021).

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System-facing AI

System-facing AI does not directly support teaching or learning but is designed to administrate education processes. These include admissions, timetabling, identifying students at risk of dropping out, and attendance, behavior and homework monitoring (much of which all too often can be indistinguishable from surveillance). The data collected by AI-powered learning management systems (e.g., when a student has logged on and logged off, what activities they have accessed and what objects they have downloaded) can be automatically analyzed to inform teachers and administrators.

Student-facing AI

Student-facing AI has been the focus of research for more than 50 years and has received by far the most funding14. More recently, from about 2010, this type of AI “escaped” from the research lab and is now offered by innumerable private companies around the world (at least 30 of which are multi-million dollar funded). Student-facing AI typically involves using machine learning algorithms to process large amounts of student data to recommend learning pathways adapted to the individual student. Student-facing AI includes the so-called “intelligent tutoring systems”15 which provide step-by-step instructions adapted to individual student accomplishments, “dialogue-based tutoring systems”16 which are underpinned by the Socratic method and use natural language processing to guide students in solving learning problems; “exploratory learning environments”17 which adopt a constructivist model and facilitate discovery learning guided by the AI, educational chatbots which provide students with anytime support18, and automated writing evaluation which might provide formative or summative assessment (Holmes et al., 2019). The most common, most researched, and most funded of these applications are the so-called intelligent tutoring systems (ITS). These work by providing step-by-step one-to-one tutoring (instructions and activities), detecting student behavior patterns, and providing continuous assessment and feedback. Although sophisticated and the result of years of research, ITS all too often only automate existing poor pedagogic practices, usually naïve instructionism, rather than bringing anything new to pedagogy (Holmes et al., 2021). In any case, ITS tend to be limited to well-defined and structured subjects such as mathematics, physics and computer science. They are far less effective in complex, dynamic and uncertain learning environments, and cannot address ill-defined problems that do not have a clear solution path, such as in the humanities and language artsolmes (H

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et al., 2019). They also inevitably reduce student agency, because of the way in which they provide their step-by-step instructions towards predetermined outcomes, nudge student decisions and actions, and reduce the opportunity to learn from failure (Selwyn, 2019). Despite being limited and not as effective as human teachers, they also replace teacher functions and thus effectively de-professionalize teachers. For example, while probably every teacher would like an AI system to do their marking for them, delegating marking to an automated system takes away key opportunities for teachers to learn about their students’ skills and challenges. In any case, there is little evidence that automated marking systems actually work. Teacher-facing AI

Teacher-facing AI has received far less attention. Usually, teachers have to make do with the ubiquitous data dashboards incorporated in most studentfacing AI that allow them to monitor “data-field” progress (Williamson et al., 2020). But there exist few AI tools that are expressly designed to support teachers—to provide, what you might think of as, an AI-powered virtual exoskeleton that helps teachers to become super teachers. In fact, the only true teacher-facing AI examples of which we are aware are designed to help teachers easily identify resources to support their teaching.19 Perhaps the reason is that student-facing AI is the low-hanging fruit which has received decades of research attention, while true teacher-facing AI is more challenging. AI to learn about learning

This final “learning with AI” topic uses a loose interpretation of AI. Nonetheless, it involves using machine learning techniques and statistics to analyze data collected during students’ online learning. It is because AI is an extremely interdisciplinary area, enriched by psychology, neuroscience, linguistics and cognitive science, that it has potential to provide insights into the process of learning (Holmes et al., 2019). In any case, over recent years, due to the increased availability of data from distance and hybrid learning modes, two closely related fields (what distinguishes them is increasingly unclear) have emerged: educational data mining (EDM) and learning analytics (LA). Both involve the collection and processing of large amounts of student interaction and other data to discern patterns, often to predict which students are at risk of failure so that they might be given additional support.20 On the other hand, although there is relatively little research in this direction, EDM and LA do have potential to help us better understand how learning happens, and which of the learning theories taught to pre-service teachers are closer to the truth.

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Learning about AI As noted earlier, by “learning about AI” we mean learning how it works and how to create it. In other words, in this connection we are talking only about AI from a technical perspective. However, although for analysis we have separated “preparing for AI” from “learning about AI,” in practice it is critical that any teaching about the technical aspects of AI should be interwoven with teaching about the human aspects of AI (rather than tagging a brief consideration of ethics onto the end of an otherwise wholly technical course, as is usually the case). That said, learning about AI involves teaching about the AI techniques and technologies that we mentioned earlier (classical AI, machine learning, deep learning, NLP, facial recognition, autonomous agents and so on).

AI curricula

Many countries have integrated ICT and digital skills curricula into formal education systems over recent years, and a few are also now introducing AI curricula into schools. At the same time, in the United States, the AI4K1221 curriculum is being independently developed for use across all educational settings, while most of the world’s largest technology companies have also developed their own AI curricula. Nonetheless, whatever the mechanism, across the world—albeit currently in limited ways—students as young as five years’ old are being introduced to AI, how it works and how to create it, helping them to understand what it can do, even if they have no intention of ever becoming an AI engineer. This integration of AI curricula in school education should over time help develop a more diverse pool of AI experts, a better gender balance and a mix of cultural values, which hopefully will filter down in positive ways to the AI tools that are developed.22 At the other end of the education journey, most Higher Education institutions around the world offer one or more courses teaching the AI techniques and technologies mentioned earlier, all designed to train the AI engineers and developers of tomorrow (while helping countries establish their AI competitive edge). The role of teachers

Finally, the successful implementation of AI curricula will not be possible without teachers—which means that teachers need to be trained. This training should include content knowledge for teachers who will teach about AI (including teachers who might usefully teach about the use of AI in music,

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writing and the other arts), as well as appropriate pedagogies for those who will use student-facing AI tools in the classroom. In particular, teachers and schools administrators, who might not possess specific technical knowledge, need to be able to evaluate properly the AI tools that they are considering using (Holmes et al., 2018), so that they might decide if and how they might be used to augment the learning process, or if they might have any negative consequences.

Preparing for AI As noted earlier, by “preparing for AI” we mean the teaching of what it means to live in a world increasingly impacted by AI. In other words, in this connection we are talking about AI from a human perspective—which as we have mentioned should be interwoven with learning about AI. To begin with, preparing for AI means focusing school curricula on the essentially human skills that AI is unlikely to be any good at for many years to come (such as creativity, collaboration, critical thinking, communication, value judgments and social and emotional learning), rather than continuing to teach what AI can already do better than humans. It also means addressing the fact that learners are increasingly interacting not only with other learners but also with non-human intelligence or technologies, a new dimension of relationships that will have profound social and ethical implications (Facer, 2021). And it also means a focus on the human issues, such as the fairness, accountability and trustworthiness of AI. For example, when teaching how facial recognition works and how it can be created, students should also consider at the same time, or at least in the same session, the ethical consequences of facial recognition (e.g., the inability of current facial recognition systems to recognize women of color as accurately as they recognize white men, and the impact of facial recognition when used in CCTV systems in public spaces). The reality is that this rarely happens, and when it does the ethics tends to be taught by computer scientists who, although experts in their own field, usually do not have any ethics training. In short, while university courses in AI are beginning to look seriously at what it means for their AI to be ethical, it is key that the AI engineers and developers of tomorrow are at least capable of considering the complex implications of their work for wider society. The ethics of AI in general has been addressed by innumerable governments and institutes around the world, leading to a vast array of principles and regulations ( Jobin et al., 2019). However, the ethics of AI for education (which is likely to be different from, for example, the ethics of AI for health or transportation) is yet to be fully worked out (Holmes et al., 2021). As we have noted, almost all contemporary AI tools require the collection and analysis of

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data, which is why most ethical principles focus heavily on that data (addressing issues such as privacy, security and ownership). Most AI for education also involves data, but, while addressing the ethics of that data is necessary, it is not sufficient: for AI and education, it is important also to consider the ethics of education. Issues such as the ethics of teacher expectations, teacher roles and relations between teachers and students, and particular approaches to pedagogy, all also need to be considered (Holmes et al., 2021). In fact, it might be more challenging to determine what is ethically acceptable for AI and education than in any other area, as the consequences might be witnessed only in the long term (Selwyn, 2019). What else does it mean to prepare for AI? What are the other transversal issues that need to be properly considered by students, indeed all citizens, around the world? Here we mention briefly just a few, fi rstly so me da tarelated issues: bias, privacy, consent and ownership; then some educationrelated issues: value alignment, autonomy, agency, empowerment, pedagogy and the future of work. Bias

Technology is never neutral; instead, it reflects the cultural norms and values of the humans who create and process it. For example, machine learning models can all too easily be biased if the data that informs it is biased (which is often the case for data scraped from the Internet), or because the developers encode (albeit unintentionally) their own cultural values in the algorithms. In other words, while AI developers position AI tools as less biased than humans, biases in AI do still exist—which may lead to the colonization of knowledge and learning with AI tools that are not culturally appropriate. For example, if the model used by a student-facing AI tool is trained on data generated by young people in Europe or the US, this might have negative consequences for young people in Asia or Africa. Privacy, consent and ownership

Every citizen should be able to understand the implications of data sharing and how AI algorithms can affect privacy, equity and sustainability. When young people in schools are asked to engage with a student-facing AI tool, they will generate large amounts of personal interaction data that is aggregated by the system. This raises multiple questions. To begin with, have the young people genuinely given their informed consent for this data to be collected, processed and re-used? Indeed, are they legally competent to give such consent, or have their legal guardians given consent on their behalf? Developers might reply:

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if the teacher is happy to use the system, why do we need the consent of the students? We do not ask for student consent when a teacher decides to use a particular textbook or non-AI classroom tool. However, introducing the AI changes things. In particular, the data generated by the student almost always leaves the classroom, to be appropriated by the company who developed the tool and re-used to enhance that company’s algorithms. We also need to consider how the generated data is processed, whether it is stored securely, and who holds the ownership (Renz et al., 2020). If the student were to write an essay, draw a design, or compose some music, they would own what they have produced. Why then does not the student (or, indeed, people in general) own the data that they create by means of their interaction with an AI system? Data is valuable—indeed, it is data that drives most AI companies’ business models. The collection of such a wide range of personal data also raises issues of privacy and surveillance. Where is the moral case for external companies to know what a student clicked and when, for how long they watched a video, or where they are on campus? Many student-facing AI systems even aim to infer the student’s affective state, with the laudable intention of helping move them from a negative to a positive affective state in order to enhance their learning but doing so represents an unprecedented at-scale invasion of personal space. Value alignment

One of the main concerns related to the design and deployment of AI, especially in education, is that while trying to imitate human cognition it cannot understand human goals—if only because humans do not all share universal goals. If we cannot define human objectives completely and correctly, it means that we cannot code them in AI (Russell, 2019). This leads to the core problem of value alignment. If we allow AI tools to make informed decisions on our behalf, how do we ensure that the values incorporated in these decisions are the ones that we want? This is specifically important in education, which is an extremely complex environment that does not always include clear definitions and rules. Autonomy, agency and empowerment

Quality education is not only about cognitive knowledge, but also collaboration, empathy and respect for the diversity of human beings and ideas. Teacher–student relations can empower and motivate: a single glance or word of encouragement at the right moment can have a big impact on a student. But can student-facing AI do the same, or do those tools risk undermining

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learner’s agency, autonomy and curiosity? For example, ITS, while adapting pathways for students, promote only a certain right type of action and aim to prevent students failing, which prevents them learning from such an experience. This might cause learners and teachers to over-rely on AI decision-making, leading to an inability to act independently (Selwyn, 2019), much like how GPS technologies have impacted our ability to self-navigate around town (Dahmani and Bohbot, 2020). In particular, the personalization of learning, which is entirely based on statistical averages, effectively deprives learners of their own individualities (Koenig, 2019) and the opportunity to self-actualize. Pedagogy

As noted earlier, most student-facing AI can be accused of automating poor pedagogic practices (essentially didactic instructionism with no opportunities to collaborate or learn from failure). This reflects many developers’ naïve understanding of what actually constitutes good quality education (after all, they went to school, so they understand education). Spoiler alert: education is not just about memorization and recall, and it is particularly not just about memorizing and recalling facts and procedures. Nonetheless, where are the AI tools that challenge existing pedagogy or that add to the lexicon? For example, it has been argued that for too long education systems have depended on end-of-course no-talking examinations to assess and accredit student achievements. However, while there is a great deal of research in e-proctoring (i.e., automating a practice that is questionable with techniques indistinguishable from surveillance), where is the research into AI-powered alternative approaches to assessment and accreditation? There are many possibilities, some that de-professionalize and some that empower teachers, but so far there are few funding opportunities. The future of work

Given that AI-powered tools can perform many routine tasks, potentially allowing humans to focus on complex work that also requires empathy or common sense, the introduction of AI is likely to affect most professions in all sectors, especially in middle-skills jobs. On the other hand, there is likely to be a growing demand for high-skilled jobs that will require both technical digital and transversal socio-emotional and communication skills. However, a closer look shows that AI and related technologies can automate not only manual tasks but also some cognitive tasks, which might drastically change the nature of human involvement in, and the quality of, workuchanan (B

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et al., 2020). Consequently, education will have an increasingly important role to play, preparing citizens for a world in which job roles are constantly shifting, and helping to re-skill people as needed throughout their lives. In addition, education and regulations are key to ensure that AI applications do not negatively affect workers’ well-being, but instead empower and augment them. They are also key to ensure that the developers (those who drive AI research, design and deployment) promote sustainable development, advance universal human rights and pursue social equity and economic prosperity for all (Shiohira, 2021).

What’s Next? Disruptive technologies, climate change, widening inequalities and global crises such as the COVID-19 pandemic have all emphasized the need to re-think the consequences of human actions and the role of education in shaping our future societies (Appadurai, 2020)—if only because, besides being one of the key factors for social and economic development, education is also a vehicle for transmitting and building shared purposes and values. This is why we need a collective effort to re-imagine both education that empowers teachers and students, and how AI can open up avenues for innovation, creativity and self-actualization. Here, this raises two questions: How can we make better AI tools, and how can education help address the challenges created by AI?

How Can We Make Better AI Tools for Education? The design and development of future student-facing, teacher-facing and system-facing AI tools should adopt a human-centered approach (Mitchell, 2019) and be problem oriented. They should start with the definition of challenges and root causes. Although the dominant narrative suggests otherwise, AI technologies cannot provide solutions to all education problems (and just because an AI system can do something does not mean that we should). For example, putting an ITS into a rural context because there are insufficient qualified teachers may be of benefit to one cohort of learners but this Silicon Valley-inspired techno-solutionism does not solve the actual problem— instead, we probably need to focus on increasing the numbers of qualified teachers by means of professional development. In short, AI technologies for use in education should by design be underpinned by educational values, should address real educational big problems, should not be driven by the private sector, but should be developed in cooperation with all actors (from learners and teachers to policymakers and civil society).

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Any learning with AI tool should also by its very nature be designed to augment a teacher’s work and not de-professionalize them (replace or degrade their status or dignity). Instead, developers need to recognize that the pedagogical process is always under construction and can be unpredictable, while AI tools (at least for the foreseeable future) lack common sense and nuanced understanding of educational contexts. For example, even if the learning content is adapted to the individual, it does not necessarily mean that it is delivered by means of the most effective pedagogical approach or towards helping the student to self-actualize (rather than just pass the same superficial examinations as everyone else). A human-centered approach should be at the heart of all AI tools and initiatives. AI might be able to identify learning patterns in the huge amounts of data that it collects that otherwise would have never been discovered and thus provide some useful insights, but it cannot create embodied social interactions between students or between students and teachers, nor can it express the whole richness of human experiences. In short, the reality is that not all that is worth knowing and learning can be incorporated into digital devices and reduced to algorithms—which is why AI classroom tools should be designed to support not replace teachers.

How Can Education Help Address the Challenges Created by AI? Finally, education as a site for critical study (i.e., both learning about AI and preparing for AI) can enable people to handle the challenges arising from AI from both a technical and humanistic perspective. It can provide the underpinning for new professions, but also raise awareness of the emerging impact of AI on humanity and inform philosophical debates on potential human– machine relationships. Informed citizens should be able to establish regulatory and monitoring mechanisms to ensure that AI technologies—especially those used in educational contexts—do not amplify existing inequalities, but instead benefit the most vulnerable or marginalized and contribute to the common good. Indeed, AI and its impact on education create challenges and opportunities, but ultimately it is social and political choices—not just the technologies or the technologists—that will determine how AI contributes to education and the outcomes for all.

References Appadurai, A. (2020) Gradual Learning, the Urgency of Knowledge and the Connectivity of Humanity. Paris: UNESCO. https://en​.unesco​.org​/futuresofeducation​/appadurai​ -gradual​-learning​-urgency​-knowledge (accessed 4 July 2021).

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Buchanan, J., Allais, S., Anderson, M., Calvo, R. A., Peter, S. and Pietsch, T. (2020) The Futures of Work: What Education Can and Can’t Do. Paris, France: UNESCO. https:// unesdoc​.unesco​.org​/ark:​/48223​/pf0000374435 (accessed 4 July 2021). Dahmani, L. and Bohbot, V. D. (2020) ‘Habitual use of GPS negatively impacts spatial memory during self-guided navigation’. Scientific Reports, vol. 10, no. 1, p. 6310. DOI: 10.1038/s41598-020-62877-0. Facer, K. (2021) ‘Rethinking the ‘human’ at the heart of humanist education’. https:// en​.unesco​.org​/futuresofeducation​/ideas​-lab​/facer​-rethinking​-humanist​- education (accessed 4 July 2021). Guan, C., Mou, J. and Jiang, Z. (2020) ‘Artificial intelligence innovation in education: A twenty-year data-driven historical analysis’. International Journal of Innovation Studies, vol. 4, no. 4, pp. 134–147. DOI: 10.1016/j.ijis.2020.09.001. Holmes, W., Anastopoulou, S., Schaumburg, H. and Mavrikis, M. (2018) Technolog yEnhanced Personalised Learning: Untangling the Evidence. Stuttgart: Robert Bosch Stftung. https://www​.bosch​- stiftung​.de​/sites​/default​/files​/publications​/pdf​/2018​ -08​/Study​_Technology​- enhanced​%20Personalised​%20Learning​.pdf (accessed 4 July 2021). Holmes, W., Bialik, M. and Fadel, C. (2019) Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston, MA: Center for Curriculum Redesign. Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I. and Koedinger, K. R. (2021) ‘Ethics of AI in education: Towards a community-wide framework’. International Journal of Artificial Intelligence in Education. DOI: 10.1007/s40593-021-00239-1 (accessed 4 July 2021). Jobin, A., Ienca, M. and Vayena, E. (2019) ‘Artificial intelligence: The global landscape of ethics guidelines’. Nature Machine Intelligence, vol. 1, no. 9, pp. 389–399. DOI: 10.1038/ s42256-019-0088-2. Marcus, G. and Ernest, D. (2019) Rebooting AI: Building Artificial Intelligence We Can Trust. New York, NY: Pantheon Books. McCarthy, J., Minsky, M. L., Rochester, N. and Shannon, C. E. (1956) A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Dartmouth College. http:// www​-formal​.stanford​.edu​/jmc​/history​/dartmouth​/dartmouth​.html (accessed 4 July 2021). Miao, F. and Holmes, W. (2021) AI and Education: Guidance for Policy-Makers. Paris, France: UNESCO. https://unesdoc​.unesco​.org​/ark:​/48223​/pf0000376709 (accessed 4 July 2021). Mitchell, M. (2019) Artificial Intelligence: A Guide for Thinking Humans. London: Pelican Books. Moravec, H. (1988) Mind Children: The Future of Robot and Human Intelligence. Cambridge, MA: Harvard University Press. Renz, A., Krishnaraja, S. and Gronau, E. (2020) ‘Demystification of artificial intelligence in education – How much AI is really in the educational technology?’. International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI), vol. 2, no. 1, p. 14. DOI: 10.3991/ijai.v2i1.12675. Russell, S. (2019) Human Compatible: Artificial Intelligence and the Problem of Control. New York, NY: Viking. Selwyn, N. (2019) Should Robots Replace Teachers?: AI and the Future of Education, Digital Futures. Cambridge, UK: Polity Press.

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Shiohira, K. (2021) Understanding the Impact of AI on Skills Development. Bonn, Germany: UNESCO-UNEVOC. https://unevoc​.unesco​.org​/home​/UNEVOC​+Publications​/ lang​=en​/akt​=detail​/qs​=6448 (accessed 4 July 2021). Williamson, B., Bayne, S. and Shay, S. (2020) ‘The datafication of teaching in higher education: Critical issues and perspectives’. Teaching in Higher Education, vol. 25, no. 4, pp. 351–365. DOI: 10.1080/13562517.2020.1748811.

Chapter 12 AGNOGENESIS1 BREEDS KAKISTOCRACY2 Bruno della Chiesa

Abstract This chapter seeks first to reflect on the role that “educational neuroscience” plays or does not play in policymaking. The challenges met by this new discipline during the two decades of its existence3 (2000–2020) range from skepticism and indifference t o f ashion p henomenon t hat s aw t he p roliferation o f neuromyths, and the mushrooming of neuro-traffickers and neuro-hijackers. Furthermore, the text below expands on these examples to reflect on the role that science, sound or not, does play or does not play in public opinion-building, decision-making processes, and the epistemological crisis the world has been undergoing over the last decade (that conspiracy theories of all shapes and sizes are a testament of). What is at stake here is nothing less than the future of our children, and hence of humankind.

How Uses and Misuses of Hard Science in (Education) Policymaking, and Beyond, Can Shed Light on the Epistemological Crisis We Are Going Through 1. (past) / Neuro-indifference: neuro-skeptics and neuro-agnostics

1.1 Two forces of inertia opposed the birth of what was to become educational neuroscience. 1.2 Around 2000 an understandable, more or less healthy skepticism was widespread at education policy level. This “neuro-skepticism” in OECD countries came mostly from France, New Zealand and Sweden: 1.2.1 France feared an innovation threatening what it perceived as its leadership (cf. note 5). 1.2.2 New Zealand feared questionable uses and abuses of such an arcane discipline (cf. 5.2.6. & 6.3.3).

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1.2.3 Sweden feared hard science could supplant social sciences as reference disciplines (cf. 6 ff. & 7 ff.). 1.3 In the education world, a relative indifference to neuroscience was perceptible: 1.3.1 Many people simply doubted that brain research could contribute to education. 1.3.2 Such an overt, careless and sometimes even condescending lack of curiosity came as a surprise among neuroscientists, and innovation-minded educators were outraged. 1.3.3 The first five education ministries to support the necessary (and huge) transdisciplinary maieutic effort back in 1999 were found in Finland, Japan, Spain, the UK and the USA4. 1.4 Things changed when teachers identified the potential of the discipline: 1.4.1 Active resistance immediately developed in policy-making circles, as expected … 1.4.2 … and, even more, within education research, albeit for completely different reasons5. 1.4.3 Overt reluctance and open resistance marked the end of “neuro-agnosticism.” 1.5 Then, some 15 years ago, the original (hardly informed) neuro-skepticism and the active (ill-informed) resistance almost disappeared overnight, giving way to a faddish enthusiasm: 1.5.1. Educational neuroscience was suddenly fashionable, in all fields (not only in academia). 1.5.2. Tidal waves of “neuro-zealots” (term coined by David Daniel) swept the coasts of education, ready to spread their new faith. 1.5.3 While such a fervor was in theory welcome, it has been challenging the field to this day. 2. (past   and present) / Neuro-craze: neuro-addicts and neuro-fashion victims

2.1 Within the education systems, it is no surprise that teachers were first to embrace the perspective of a potential “neuroscientific revolution” and to lead the way into the field for the discipline: 2.1.1 Teachers, as education practitioners and thus as adult end-users of educational neuroscience, were the most important group to have much to gain in a revolution6. 2.1.2 Often close to despair, teachers are constantly looking for new tools that would allow them to face the extremely difficult challenges they are

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confronted with on a daily basis. Generally, practitioners hoped so much from brain research that they invested a lot of time and energy to better understand the learning brain (of which, after all, they are also experts, albeit less theoretical than neuroscientists). 2.1.3 Especially when most felt (not without reason) that institutions were once more resisting change, and were hence potentially depriving them of the tools they trusted to solve some of their professional problems, their frantic overzealousness, so characteristic of proselytes, turned into an almost fanatic proselytism. Many became neuro-addicts. 2.2 Outside of the education field, the mass media quickly followed the new trend (cf. 3.6 ff. & 4.3 ff.). 2.2.1 As is often the case, even more quickly they started to promote the fashion: with this, they became both preys and predators in a rigged game. Same old song. 2.2.2 Journalists took the lead, of course: after all, given the plethora of insights emerging from brain research at the turn of the century, there was a lot of exciting information to disseminate. 2.2.3 Myriads of other media professionals followed suit (cf. 3.6; 4.4.): with this, neuroscience was reaching the general public, turning “consumers” into neuro-fashion victims. 3. (present)   – Neuro-business: neuro-charlatans and neuro-traffickers

3.1 Now that everything related to understanding the brain is fashionable, there are major risks of which citizens should be aware. These risks, unfortunately confirmed by facts, are illnesses which metastasize all over the place, thus more and more difficult to cure. 3.2. Since the early 2010s, new players have entered the field: self-proclaimed experts in search of publicity (and money) are flooding the market with halfbaked training programs, pseudo-scientific b ooks, a nd o ther a dulterated products of the kind, meant for neuro-addicts. These are neuro-charlatans. Their practices often border on dubious “personal development,” and their scientific b ackground i s at b est weak, a nd, i n f act, most of t he t ime inexistent. 3.3 Neuro-charlatans behave as snake-oil salesmen. 3.3.1 Among them, a minority is more or less able to distinguish between sound science and pseudoscientific garbage, but most neuro-charlatans are not even aware of this distinction. In any case, such considerations are irrelevant to their mercantile preoccupations.

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3.3.2 If something sells, its scientific validity is of little significance to them— in fact, it is of no significance at all: what matters to the marketing of neurocharlatans is not the snake-oil per se, but the benefits they pretend it brings, regardless of its effects on the consumer. 3.3.3 This explains why neuro-charlatans often work hand-in-hand with media personalities (especially in TV, and more and more on so-called “social networks”). Mediacracies are populated by mediocrats7. 3.3.4 While obvious frauds are easy to unmask, unfortunately it is practically impossible for the layperson to distinguish between a genuine specialist and a charlatan, provided the latter has developed good communication skills. 3.3.5 In fact, neuro-charlatans are often much better at selling their junk than scientists at sharing their ideas. Scientists are generally not well trained for such communication exercises, and moreover, when they refuse to betray the complexities of science, with all its uncertainties, popularizing their work becomes a close to impossible task. 3.4. So most neuro-charlatans are in fact neuro-traffickers. 3.4.1 Wheeling and dealing with tools they do not comprehend, neuro-traffickers do not worry at all about the potential consequences. 3.4.2 The logic they operate under, once more, is that of marketing, which is very different to the logic of science and research. 3.4.3 However, this marketing logic has a lot in common with politics: immediacy is key, and the time horizon is limited to a few years at best. 3.4.4 Thus, charlatanism and trafficking ca n have a very real in fluence on education policies and practices, often with disastrous consequences for the learners. 3.4.5 But this neuro-business produces mountains of neuro-dollars. 3.5 For the reasons explained above, education practitioners are easy prey for neuro-charlatans, neuro-traffickers—and for other con artists. 3.6 The mass media constitute easy prey as well, albeit for different reasons. 3.6.1 Some media personalities (especially star anchors and pundits) represent an attractive target for neuro-traffickers, given that, as so many other ignoramuses, they yearn to be considered as intellectuals—perhaps in order to try to justify their extravagant power. 3.6.2 Incapable to tell the difference between “incontestable values” and “incontestably contestable values” (to borrow Pierre Bourdieu’s words), unread hacks of all shapes and sizes actively contribute to the continuous selfpromotion of a broad spectrum of impostors. 3.6.3 With this, the mass media relay more and more nonsensical propaganda, often without even realizing it. 3.7. This problem is, of course, not specific to educational neuroscience: think climate change, the COVID-19 pandemic8, vaccinations… (cf. 7.3.5).

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4. (permanent) – Neuro-gullibility: neuromyths

4.1. Neuromyths, whose dissemination spares no region of the globe, dangerously proliferate: both their emergence and their propagation are spontaneous only in appearance. 4.2 Since the brain is, for many people, such a “sexy” topic, everything related to the brain sells. 4.3 Neuroscience is attractive, so mass media pick it up. 4.3.1 In their univocal logic (to be fast, be simple, be assertive, leave no room for doubt, etc.), mass media always simplify and even over-simplify scientific results. 4.3.2 In their immediacy and sensationalist logic, said media tend to unduly extrapolate and generalize ill-understood scientific results. 4.3.3 In their market logic, said media ignore precautions that scientists are supposed to observe. 4.4 The most popular neuromyths have been around for a long time, and they range from stupid (“I read somewhere that we only use 10%—or 20, 25, 30 …—of our brain capacity anyway”) to funny (“I’m a left-brain person, and she’s a right-brain person: that’s why we can’t get along”). 4.4.1 Some are dubious (“male brain and female brains are inherently different”—this one shady when linked to supposed “learning capacities”), others even dangerous (“There are critical periods when certain matters must be taught and learned”, and/or “There is no time to lose, for everything important about the brain is decided by the age of 3 or 5, 6, 7”, etc.). 4.4.2 Some of the most frequent ones are developed to serve commercial purposes (“Learn a new language while sleeping!”; “Improve your memory without effort!”; “Brain-based method!”). 4.4.3 But the most frequently heard of all myths, which also happens to be the most fallacious and the most horrifying one, by far, is the one according to which “some people are ‘intelligent’ (meaning: ‘good at learning’), and some others not; it is simply so, there is nothing we can do about it”. Nature (innate) is overplayed once again, at the expense of the role of culture (acquired)— better, in this case: the role of socially determined subcultures. 4.4.4 This ideology-informed near-neuromyth, which could be called “myth of gift” (at birth) will not disappear any time soon, given how deeply entrenched it is in the belief systems of so many people, and how consciously watered it is, on a daily basis, for the political, social and economic benefits of sparsely populated but extremely powerful groups (who are still pushing crap like IQ tests). 4.4.5 This myth, and some others, are centuries old (even if they were only recently identified as pertaining to the neuro-stuff, or as at least closely related to it). But in spite of having been debunked decades ago, they still prosper and flourish9.

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4.5 While not pertaining to the neuromyths stricto sensu, the predating “meritocracy myth” is directly linked to the previous one, and reinforced by the omnipresence of the neuro-babble; it can hence be analyzed as an additional collateral damage. 4.6 Given that, understandably, the layperson cannot differentiate science from pseudoscience, researchers upset by ridiculous superstitions (and by the subsequent deleterious practices derived from them) have been trying to urge to caution, but to no avail. 4.6.1 Despite the effort expended by responsible people, pseudoscientific aberrations based on neuromyths survive and even thrive. 4.6.2 Some are harmless, like “brain gym”, for instance. 4.6.3 Others are hazardous if not outright harmful to health, like “neurolinguistic programming” (NLP), and many others. 4.7 The main reason why neuromyths succeed so much is that myths are generally easier to understand than scientific insights. 4.7.1 Campaigning against any myth is not an easy task. In fact, it is everything but simple: a real promethean endeavor. 4.7.2 The complexity inherent to any genuinely scientific process (methodologies, research protocols, replications, etc.) makes it impossible to simplify without betraying the very essence of science. 4.7.3 Contrary to the initial neuro-skepticism that affected educational neuroscience around its birth, the proliferation of neuromyths, yet another childhood illness of a viral nature that affected the fledgling discipline, has not yet been eradicated. 5. (present) – Neuro-credibility: neuro-apologists

5.1. Among the risks that appeared more recently, there are two major ones: 5.1.1 The first one weighs on the discipline and could impede its credibility. 5.1.2 Neuro-charlatans and neuro-traffickers ca nnot sa tisfy th eir cl ients’ expectations. 5.1.3 As soon as end-users realize that the “tricks” they have been buying do not work, they tend to reject everything with the prefix “neuro”, if not the word “brain” altogether. 5.1.4 Unable to separate the good from the bad, potential (or already dependent) neuro-addicts throw the baby out with the bath water. 5.1.5 The higher the expectations, the deeper the disillusions—which are, sooner or later, inevitable. 5.1.6 Only after a long and painful rehabilitation process will educational neuroscience then regain its lost credibility, and its information potential in educational matters—policies, practices, approaches and so on.

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5.1.7 Avoiding this trap is a good enough reason for neuro-apologists to act, and to act swiftly. 5.2 The second risk is broader and more dangerous than the first, given that the deepest harm it is inflicting on people is related to how the use of brain research impacts education (cf. 6.3. ff.). 5.2.1 What is fashionable, be it in scientific matters or any other domain, is only too often readily adopted by consumers (partially or entirely deprived of critical thinking skills—not really encouraged by groupthink!). 5.2.2 Gutter pseudoscience is thus often embraced just as much as sound science. Educational neuroscience is not immune to this phenomenon, as the success of neuromyths shows. 5.2.3 If pseudo-neuroscience was simply harmless and could be considered as a placebo of sorts, there would be no reason to worry too much, even if the placebo effect should not be taken lightly. 5.2.4 The risk pointed at here has much more to do with the political and social consequences and the practical “applications” of neuroscience than with the seriousness of the research work done upstream. 5.2.5 Whether neuro-traffickers wh o ar e in vading th e fie ld are awa re of it or not, they are dealing products that are everything but inert. If only their junk was simply destined to make money for these crooks (which it is, although they would of course never admit to that), it would certainly be questionable but remain relatively benign (except for their effect on the buyers’ purses). 5.2.6 What these dealers sell, however, are tools able to justify any political agenda and any policy, as despicable or terrifying as they may be (cf. 1.2.2 & 6.3.3). 5.2.7 Haloed in the respectable aura of science (even if, as far as pseudoscience is concerned, it is obviously not justified, and even if science is less and less respected, for reasons developed above), these tools are, in educational settings, weapons of massive prescription. 6. (present and future) / “Neuro-education”?!: neuro-hijackers

6.1 Science should never dictate our conduct. 6.2 Scientific insights can shed light on understanding (creating knowledge, meaning: “connecting dots of information”) and thus usefully inform decision-making processes, but cannot supplant ethics when it comes to making decisions. 6.2.1. Educational policies should be the fruits of Politics (uppercase P) and hence, at least following Plato’s ideal, of Ethics. The twentieth century has shown where policies allegedly guided by science lead.

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6.2.2 Science (uppercase or lowercase S) is neither technically able, nor ethically allowed to say what is good or bad, what is desirable or not, especially when the future of our children, and hence of humankind, is at stake. 6.2.3 Confronted with an ill-informed public, anyone can pretend almost anything to be the logical, nay desirable, consequence of discoveries. 6.3 Those using either scientific insights or pseudo-scientific delirium (which often includes “sugar-daddy science”10) to support their own (predetermined, and, in any case, self-interested) agenda are neuro-hijackers. 6.3.1 In many countries today, neuro-hijackers are mushrooming, preferably recruited among cynical politicians or policymakers who do not shy away from any distortion or misappropriation of research, reliable or not, in order to justify and eventually implement their (often ideology-driven) agendas and manipulate public opinion to “manufacture consent” for their own benefit. 6.3.2. Policymakers claiming to base their educational decisions on “brain research” have now been observed for more than a decade; such maneuvers are growing and growing. 6.3.3 A frightening hypothesis: based on distorted research results or on so-called scientific i nsights, i t w ould f or e xample b e p ossible, a nd e ven quite easy, to advocate ethnic discrimination in school settings—just acting as if neuroscience would support the racialist theory according to which ethnic groups are unequal in terms of brain (learning) power! (cf. 1.2.2 & 5.2.6) 6.4 Any educator, any citizen, any human being should make it its duty to refuse prescriptions based on science only (solid or not) when it comes to making decisions of an ethical nature. 6.4.1. As soon as 2002, the book Understanding the Brain – towards a new science of learning?11, started to send warning signals to the citizens-readers confronted with such downwards spirals, when debates on the goals and potential uses of the discipline started to emerge. 6.4.2 Whatever happens, nobody will hinder the advances in science, of course. Such a goal would be as senseless as ludicrous. 6.4.3 Nevertheless, people pretending to be responsible have no right to keep silent and do nothing, as if these phenomena were none of their business. Such misuses are well and truly the business of everyone - any one of us. 6.5 Meanwhile, as neuro-charlatans, neuro-traffickers an d ne uro-hijackers prospered on the territories of neuro-addiction, a new expression became a hit: “neuro-education.” 6.5.1 There are many reasons to reject this neologism:

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6.5.2 The most obvious one of them being that it suggests that only neurons should be educated, which is ridiculously reducing the very notion of education. Human beings are not neurons. They are even much more than a huge collection of neurons. 6.5.3 Yet another reason is the catchy aspect of the phrase, which reeks more of marketing jingles than of research agendas or educational aims (moreover, it evokes notorious swindles that still survive and thrive in various regions of the world). Once more, greed seems to trump it all. 6.6. I would like to suggest that from now on, before immersing an audience for the first t ime i n t he w aters o f n euroscience, t hose w hose d uty i t is to do so always start the initiatory journey indicating the limitations of the discipline, underlining the importance of sorting the wheat from the chaff. 6.6.1 Any conversation, any course, any training program, any educational product (online or not), any book dealing with educational neuroscience should clearly state, in its introduction or foreword, a clearly visible hazard sign: never even start to discuss the potential and the (real) benefits of the discipline without making sure, before anything else, that the end-users understand all the whys and wherefores of the whole endeavor. 6.6.2 Similarly, it is indispensable to warn against potential pharming, abuse and pitfalls. 6.6.3 A negative, pejorative connotation should henceforth be attached to the despicable label “neuro-education”, which will then specifically designate the activities of neuro-charlatans/traffickers (who often love to use the label “neuro-educators” for themselves) and neuro-hijackers. 6.7. Then the label “educational neuroscience” will be reserved for serious and conscious work. This precise and differentiated terminology would attract the attention of both insiders and players external to the field alike on the crucial and necessary demarcation to be achieved not only between what is scientifically sound and what is not, but also between what is politically and socially responsible and what is not, thus discriminating between what is ethically acceptable and what is not. 7. (future) / Neuro-Enlightenment: neuro-humanists

7.1 On top of a health crisis, on top of social, political, geopolitical/military crises, and on top of a looming financial/economic crisis, the world is undergoing an epistemological crisis (symptom of a moral crisis) that the World Health Organization (WHO) recently styled “infodemic”12. 7.1.1 As with neuromyths, the breathtaking multiplication of conspiracy theories (which have always existed in a form or another: consider for example

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the recurring manipulation of History—for political purposes, obviously) is a sad reality, epitomized by the expression “alternative facts”13. 7.1.2. Oddly, but interestingly enough, reaching such heights of bad faith was facilitated, if not rendered possible altogether, by the mingling of a growing marked taste for obscurantism and autocracy, as promoted by neo-medievalism (fantasy, pervasive in pop culture) and of the flood of information humankind is subjected to (and possibly drowning in). 7.1.3 Information (know-what) is not knowledge (know-why), but constitutes the elements (bits and pieces) knowledge can be constructed from: playing this kind of “connect-the-dots” game requires skills (know-how), that the human brain must first acquire. 7.1.4 Among the sets of skills required to construct knowledge, one of the most important ones, if not the most important one, is referred to as “critical thinking skills”14 (precisely those needed to protect oneself against neuromyths, conspiracy theories, bigotry/tribalism/nationalism and the like). 7.1.5 In other words, what humankind needs now is the emergence of a “neuro-Enlightenment.” 7.2 Conspiracy theories are often analyzed (not least by social psychology) as responses to an anxiety-inducing complexity of the world around: like neuromyths, they are simple, offering monocausal explanations for anything difficult to understand, to comprehend or to integrate. 7.2.1 As neuromyths (applied to the brain, as related to human behaviors), again, conspiracy theories (applied to the world, as related to Us and Them) are arguably developed and adhered to in order to simplify a reality just too complex to grasp. But there is more to this. 7.2.2 Historical perspectives suggest that the world is not necessarily more complex today than it ever was (but people are arguably more aware of this complexity now than they were before); anyway, even if this is accurate, the complexity around is not ipso facto less frightening. 7.2.3 One of the most complex questions human beings are confronted to is that of the universal and the particular: dealing with these antagonistic tendencies (far from being only theoretical) requires a form of dialectical reasoning between commonalities and diversities15—no wonder, in such conditions, that ready-made easy answers and so-called “solutions” are warmly welcomed. 7.2.4 Disinformation professionals (interested in money and power first, if not in money and power only) operate with models similar to the ones of either neuro-traffickers or neuro-hijackers, depending on whether they actually produce the nonsense themselves or if they merely benefit from it. Convinced that “if you tell a lie big enough and keep repeating it, people will eventually come to believe it”16, they indeed repeat ad nauseam any nonsense they think is beneficial—to them. Delusion rains, as bombs, keep falling down.

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7.2.5 Conspiracy theories are not only frequently related to specific worldviews, but moreover most of the time they are worldviews per se, easily turned into political weapons, as can be observed throughout History, up to this day. 7.2.6 In this sense, Manichean worldviews are particularly efficient: “Us and Them” quickly becomes “Us vs. Them”, which has the enormous advantage of reinforcing, out of fear, cohesiveness within a given group while scapegoating others17. 7.2.7 This explains the prevalence of the infamous “culture wars” (active ingredients in the abhorrent “identity politics”) we are witnessing. 7.3 These “culture wars” are featuring, among other elements, crazy conspiracy theories and execrable scapegoating, all of which paves the ground for monocratic authoritarianism, teleocratic tyranny, and outright fascism18. 7.3.1 Speak of fascism: looking at this planet of ours sixty years after Eichmann’s trial, should we not consider adding the “banality of ignorance” to Arendt’s groundbreaking diagnosis (and famous subtitle) on the “banality of evil”19? 7.3.2 Nescience is neither strength nor freedom nor peace: “Knowledge is freedom; ignorance is slavery”, as Miles Davis put it20, borrowing and playing with the famous words used for the Ingsoc slogan in George Orwell’s Nineteen Eighty-Four. 7.3.3 In this particular sense as well, agnogenesis (the manufacture of ignorance) is a crime against humanity: wasting brains is destroying human beings. It furthermore surely paves the way for populist narcissists and their sinister satraps, leading to despotism, dictatorship, and war. 7.3.4 Wide-spread ignorance elicited by obscurantist policies is a condition, if not the ultimate condition, for kakistocracy (government of the worst) to establish itself. Then, thanks to mass-produced unenlightenment, kakistocracy can only and constantly reinforce its power. 7.3.5 History, including the most recent one, sheds a crude light on this somber reality. Now, how to best use our “collective intelligence” when massive philistinism (as epitomized by the series of decisions made by the US Supreme Court end of June 2022”) seems to be taking over?21 7.3.6 It is thus the sacred duty (and the Sisyphean task) of educators, as neurohumanists,22 to make light shine in the darkness ( Jn. 1,5), to remedy this state of affairs—state of facts. 7.3.7 “Freedom is the freedom to say that two plus two equals four.”23

Part IV INDUSTRY–UNIVERSITY COLLABORATION

Chapter 13 PUBLIC EDUCATION AND THE UNIVERSITY Murat A. Yülek

Introduction The Second-Generation Universities (2GU; see Chapter 1) have had two functions: education, aiming at conveying existing knowledge and skills to new generations of students; and research, generating new knowledge for society. Through these traditional functions, the university has provided benefits to society since the Middle Ages. Now it is clear that there is a need for change toward the 3GU (Wissema 2009); a third function is warranted: a more direct contribution to economic development, and an active involvement of the university in addressing society’s developmental challenges. The university is part of the modern public education system, which had been initiated in response to economic and social dynamics that led to the Industrial Revolution in the eighteenth century. This could also be called the “Educational Revolution” akin to other revolutions such as the agricultural (around 10,000 BC), the European “Commercial Revolution” of the tenth century (Lopez and Lopez 1976, 950–1350) and the “Industrial Revolution” (Gordon and Schultz 2020). The university is also expected to engage in scientific r esearch, p otentially s purring t echnological a dvancement and innovation. Commercialized research is potentially one of the key drivers of economic growth and development in wide-ranging areas of the economy from agriculture to industrial high-technology areas. Thus, the university can continue to contribute to society through its second traditional function. Current higher educational practices clearly reveal that there is a significant gap to be filled between the university and the society. It is questionable that such nice bridges as the triple helix idea can close that rift. Rather, calls for a full reform of the structure of the public education including

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universities are mounting in the society (Buenstorf and Koenig 2020, Vol. 49; Baglieri, Baldi and Tucci 2018, 51–63; Liefner, Si and Schafer 2019, 3–14; Degl’Innocenti, Matousek and Tzeremes 2019, Vol. 48; Zhang, Chen and Fu 2019, 33–47; Rajalo and Vadi 2017, 42–54); and the design of new university sub-types is also necessary to address both education and research aspects.

The Origins of Pre-university Public and Technical Education Free compulsory public education developed in response to changing economic circumstances (Roberts 1957; Becker, Hornung and Woesmann 2011, 92–126; Carl 2009, 503–518). The world is now transitioning from the industrial revolution to the knowledge revolution (Chichilnisky 1998, 39–54). This section reviews the emergence and development of public education and its characteristics, shedding some light on the evolution and dynamics of technical education to ground the evolution of university education. The Prussian factory-based education system

Free compulsory public education is considered to have started in Prussia in the early eighteenth century. An edict by King Frederick William I encouraged primary education (Schleunes 1979, 315–342). Frederick II issued a law—Generallandschulreglement—forming an education system of eight years of primary education—the volksschule— that eventually became compulsory. Designed by Johan Julius Hecker, the early Prussian public education system was closely linked to the church. It involved grouping students into age groups, providing them with predetermined curricula mainly consisting of basic skills of reading and writing, as well as music (singing) and religious education. Hecker is also considered the architect of the first applied secondary education—the Realschule—which represented a significant break from the traditional secondary schools. The system included professional education of teachers with standard salary scales. The Prussian education system was state-funded although private agents were also entrusted to build and fund schools. The Prussian initiative was later dubbed the Factory-Based Model of Education (FBE). This naming maybe because the FBE involved orderly organized small desks which looked physically like an “educational factory.” However, this claim is not readily substantiated as Prussia at the time had not yet experienced the industrial revolution, although Frederick’s clearly implemented mercantilist—today called industrial—policies with a view to strengthen Prussia economically by encouraging net gold (capital) flows and

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developing certain sectors of the economy such as manufacturing and textiles (Yulek 2018). The possible second explanation is that FBE considered the student as an input to be shaped into an output through an educational system involving predesigned and repetitive procedures. Meshchaninov (2012) describes FBE as a “means of solidifying the fledgling Prussia into a uniform whole (Boli, Ramirez and Mayer 1985, Vol. 29) … explicitly designed for the purpose of consolidating imperial power.” FBE subsequently inspired France, and then the USA. Horace Mann (1796-1859)—“the Father of the Common School Movement”—succeeded in transferring the Prussian system to the USA in the nineteenth century: “A small number of passionate ideological leaders visited Prussia in the first half of the 19th century, fell in love with the order, obedience, and efficiency of its educational system and campaigned relentlessly thereafter to bring the Prussian vision to our shores” (Gatto 2000). The first such school in the United States, the Farm and Trade School in Boston, was founded in 1814 with the objective of providing orphans with academic as well as vocational education (Gordon and Schultz 2020). The Prussian FBE model or its derivatives have thus subsequently become the global educational norm. In every country, children (at ages six or seven years in general) are legally obligated to attend school until a certain age (12 years or more in developed economies). From education for adults to vocational and university education

In the USA, non-profit groups and technical societies established the fi rst adult schools to provide factory workers with educational opportunities rather than the prolonged track of the apprenticeship system (Theuerkauf and Weiner 1993; Walter 1993). In 1826, the American Lyceum movement enabled the spread of adult education through informal settings to assist social development. Roberts (1957) suggests that there were about 1,000 lyceums in the United States in the mid-1800s. In 1824, the Rensselaer School in Troy provided the adult population and teachers with technical and technological training at actual farms and production-oriented workshops. The Rensselaer School expanded its curriculum with Mathematical Arts in 1835 leading to the first school of engineering in the United States (Bennett 1926). In the Hampton Institute system, students worked to pay the tuition and studied at the same time. In 1868, the Worcester Polytechnic Institute in Worcester, Massachusetts, became one of the first schools designed to provide this type of education (Bennett 1926) and theoretical classes were combined with production work in labs so that students would not need an apprenticeship period when they completed the program (Walter 1993). In 1876, John

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Runkle, Massachusetts Institute of Technology (MIT) President, visited the Russian system of tool instruction exhibit in Philadelphia and considered it a solution for better training. This exhibition was based on the system of tool instruction, which promoted the construction of models from plans designed and drawn by students, a method to bridge the gap between theory and practice. Swedish and Russian systems similarly promoted manual labor and laboratory work in the form of exercises for a logical order for teaching certain tasks to develop pre-defined skills (Struck 1930).

University Universities have increased in number after medieval times—especially in the nineteenth and twentieth centuries (Schofer and Meyer 2005, 898–920; Valero and Van Reenen 2019, 53–67). In modern societies, universities constitute the highest level of formal public education. It combines and utilizes human, physical, and financial resources to achieve education and research functions. The traditional societal expectations from 2GUs are two-fold. Education is a transfer of the known set of tertiary level knowledge from the current generation of society to the next one. Training is a provision of known skills associated with professional fields such as medicine, engineering, agriculture, and forestry. To fulfill e ducation e xpectations, t he university has to address the cognitive skills of the learner. As versions of the Prussian FBE model universities around the world have concentrated predominantly on education at the expense of skills training, ignoring the balance between theoretical and practical aspects of curriculum and teaching. However, there is also a growing disenchantment with universities and calls for reform. First, second, and third generation universities

Universities first appeared in the Islamic world starting in the eighth century. Bait ul Hikmah, established in Baghdad in the seventh century as a library and translation center of Sanskrit and Greek scientific texts (Al-Khalili 2011; Kaviani et al. 2012), acted as an academic and research institution. Its experts also served as professionals in areas such as engineering and architecture (construction projects), medicine (providing medical services), astronomy (space maps and calculations, keeping the calendars), cartography (mapping the world), archeology (excavations of historical sites), and public administration. Caliph Al Ma’mun, a protector of the Bait is considered the first funder of “big science” (Al-Khalili 2011).

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Other academic institutions such as the University of Kairouan (est. 726); the University of Zaytuna (est. 732); the University of Cordoba (est. 786) of which Pope Silvester II was a graduate; Al Qarawiyyin University in Morocco (est. 859), which is the oldest existing, continually operating and first degreeawarding university in the world founded by a woman; Al-Azhar University in Egypt (est. 970) and Nizamiyah University (est. 1067) followed quickly. The Islamic universities were formal constructions capitalizing on the earlier informal practice of “learning circles;” a form in which there is a very close relationship between teacher and student, a master–apprentice model. The diplomas (“ijazat,” which were permissions to teach or practice) were granted by the professor on a course basis. The first European university was the University of Bologna (1088) which focused on education and memorization of pre-produced material. The 1GU was characterized by a strong affinity be tween th e te acher an d th e di sciple (Wissema 2009). Universities in Europe quickly mounted and included the University of Paris (1150) and the Universities of Cambridge (1209) and Oxford (1096). Economic history research has verified that universities were an important force in the commercial revolution (ninth to thirteenth centuries) through the development of legal institutions (Cantoni and Yuchtman 2014, 823–887; Valero and Van Reenen 2019, 53–67). The 2GU was pioneered by Humboldt University (est. 1810). It came into being after the industrial revolution in the eighteenth century leading to more division of labor. The 2GU has focused on research and “pure science and did not regard the application of their know-how as their task” (Wissema 2009). However, in practice, the important task of commercialization of the newly generated knowledge was left to public companies. The 2GU required a doctorate to teach. The Humboldt University introduced new requirements and meaning for a doctorate degree. The Award of a doctorate degree was subject to attendance of the student in seminars, submission of a thesis with an original contribution to science, and passing a comprehensive oral examination. Humboldt academicians were required to undertake research and publication in addition to teaching. Many British and American students got their doctorate education at Humboldt as the universities in their countries did not provide the degree. When they returned to their countries, they were employed at colleges and universities. American universities started to adopt the Humboldt system, starting with Yale University in 1861. The British universities followed in the early twentieth century (Park 2007). The French higher education system was reorganized in 1806–11, focusing on professionalization and a faculty system was established. The most important characteristic of the Napoleonic academic system was the formal

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establishment of Grandes Ecoles aimed at training elite cadres of administrators, engineers, and physicians. In today’s world, most of the existing universities can be classified as 2GUs. The Triple Helix Model (Lowe 1982, 239–246; Sabato and Mackenzie 1982; Etzkowitz 1993, 2008; Etzkowitz and Leydesdorff 1995, 14–19) can be considered an attempt to upgrade the 2GU. The model emphasizes possible societal value from a close relationship between three actors: university, government, and industry. In the basic model, the university is supposed to undertake early, basic research as well as train human capital. The industry, on the one hand, is supposed to receive the results of the basic research and commercialize it. The government, on the other hand, is supposed to provide R&D-friendly regulations, funds, and incentives as well as a market-friendly business environment to the industry (Figure 13.1). However, despite all these suggestions, the 2GU has not been able to close the great rift between university and society reducing the developmental contribution of the university. The 3GU is a form that aims at breaking the Chinese Wall between research and society (Table 13.1), pursuing “the exploitation or commercialization of the knowledge they create, making it their third objective, equal in importance to the objectives of scientific research and education” (Wissema 2009). Knowhow exploitation includes active involvement in stimulating start-ups. According to Wissema (2009), “technology is a tool for realizing profit because research is business; open innovation and external development are important steps for creating know-how and the establishment of new companies (technological start-ups) as tools for its exploitation are welcome.” Recently, discussion on the Fourth Generation University (4GU) is mounting. However, the concept of 4GU seems not to offer a significant difference from 3GU as it aims at a wider role in economic development (Pawłowski 2009, 51–64; Reichart 2019).

University Basic research results

Government

Industry

Figure 13.1 Ideal university, government, and industry ecosystem.

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Table 13.1 Characteristics of three generations of universities.

1GU

2GU

Objective

Education

Education and research

Role Method

Defending the truth Scholastic

Creating

Professionals

Orientation Universal Organization National faculties and colleges Management Chancellor

3GU

Education, research, know-how exploitation, and economic development Discovering nature Creating economic value for the society Modern science and Modern science, monodisciplinary interdisciplinary, applied, and practical Professionals and Professionals, scientists scientists and entrepreneurs National Global Faculties Other tertiary institutions and units Academics Professional management

Source: Wissema (2009) and the author.

Industrial revolution, knowledge revolution and university: The need for new university types

The First (British) Industrial Revolution (1IR) owes itself a great deal to the Scientific Revolution (Yulek 2018). In fact, technological knowledge and education have played important roles in the 1IR (Mokyr 2000, 2002, 2018). Following the 1IR, in the second half of the eighteenth century, a batch of countries including the USA, Germany, and Japan have undergone the second industrial revolution mostly in the second half of the nineteenth century. Yet, a third group of countries in East Asia experienced the third wave of industrial revolution in the second half of the twentieth century. Thus, successful industrialization, income generation, and economic development have gone hand-in-hand (Yulek 2018). General and vocational education continue to play a significant r ole i n industrial competitiveness (Peneder 2001) and industrialization as they have played in the different waves of t he industrial revolution. There is a direct positive correlation between educational outcomes and industrial competitiveness. In the knowledge revolution era, economic growth is thought to be driven by knowledge and by the technologies for processing and communicating it (Chichilnisky 1998, 39–54). However, technology is a product of human capital. That is, high-quality human capital generated by highly productive universities (and pre-university schools) is now a more important determinant of technological progress and economic development.

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The endogenous growth theory (Romer 1990, 71–102) has underlined that knowledge is also produced by the industry as a by-product of the production process, that it mostly accumulates in the employees, and is passed on from one generation to another. Moreover, spillovers of knowledge occur when employees move to other firms and institutions; thus, providing positive economic externalities to the society. Consequently, this technical knowledge is a public good in essence and can hardly be generated solely at academic institutions. The implication is that joint research between academic and non-academic actors including firms and public research centers may provide positive economic externalities. A related issue is whether public policies—especially industrial policies— can have a positive developmental impact by accelerating the industrialization process. A strand of research argues that industrial policy is warranted (Amsden 1992; Rodrik 2009; Wade 2004; Yulek 2018) while others oppose the idea (Krugman 1994). Nevertheless, the recent East Asian Miracle and now the success of Chinese industrial policies clearly demonstrate that welldesigned industrial policies may work. On the other hand, the fact is that many countries implement industrial policies while few succeed to bring to the fore the critical contribution of policy design which is a product of the “state capacity” (Yulek 2018; Yulek et al. 2020). An industrial university can support the formation of an “industrial layer” (this concept is due to Yulek 2018) true to the provision of both industrial employees and entrepreneurs. Moreover, it can complement the production, technological and R&D capabilities of the firm’s partners; thus, it can further ameliorate the industrial layer’s competitiveness. Consequently, a good tertiary sector would enhance the effectiveness of i ndustrial p olicy a nd c ontribute t o a healthy technological industrialization process in any given country. The knowledge revolution has necessitated—and helped develop—a new form of university, the so-called entrepreneurial university (EntU). Closely related to 3GU, the EntU has much closer and deeper links with the industry. The conclusion from the above discussion is that industry replicates the main two functions of university (conveying knowledge to the new generation and generating new knowledge). The industry has a wealth of knowledge and human capital resources that academia does not have (and vice-versa). However, if academia can collaborate effectively with the industry, it can be a catalyst for the society to make industrial knowledge resources more functional. Education and training and societal efficiency of the 2GU

There is evidence of a positive relationship between the existing university configuration, and regional and national economic growth and development

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(Kantor and Whalley 2014, 171–188) through education. Valero and Van Reenen (2019) report that a ten percent increase in the number of universities (roughly, adding one more university in the average region in their data set) increases that region’s income by 0.4 percent with additional positive externalities in the neighboring regions. Valero and Van Reenen’s (2019) results of the positive economic impact of a university on the region are based on skilled graduates who raise productivity in the firms in which they work and the innovation (as measured by an increase in patents) that university adds to the region. However, all this is the outcome of the existing university format and content. Many universities today have become theoretical educational institutions without adequately providing market-compatible knowledge and skills to students. This limits the benefits that the university provides to society. The 2GU has distanced itself from the realities of the job market. In particular, social science fields such as economics or sociology, or even others such as agriculture or engineering, broadly lack the skills component. The result is a significant mismatch between the demand for skills by the employers (private or public) in social sciences and the skills that the students can gain during university education. Most university graduates are under-skilled compared to the requirements of the labor market (Alba-Ramirez 1993, 259–278; Groot and van den Brink 2000; Ansell and Gingrich 2017, Vol. 50). And sometimes they are overqualified (CIPD 2018). Both are cases of labor market skill mismatch. A simple outcome of the skill (or, more generally the competence) mismatch is growing unemployment rates in the young (24–29 years) age group (Figure 13.2) and key challenges faced by the university in providing education are: 1. Skill and knowledge mismatch: A large majority of university graduates are employed by the non-academic labor market (firms and civil service). This mismatch is created by the fact that most universities are away from market realities; the university has become an ivory tower itself (The Economist 2015). 2. Alternative education institutions: The university faces a challenge from new educational actors: private sector professional education providers. As a university in most cases remains slow to adopt new teaching areas, the graduates or potential university students tend to enroll in professional training programs. 3. New technologies also provide a challenge to universities, especially to the ones that are too slow to adopt. Rapid technological development requires universities to change their curricula as well as teaching techniques. Faster competitors again risk the marginalization of universities (Van Vught 1999, 347–355).

204 30.0

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Youth Unemployment Rate (15-24 age)

25.0 20.0 15.0 10.0

5.0 0.0

Figure 13.2 Unemployment: 24–29 years (Source: World Bank).

Conclusions Public education is an inheritance from the eighteenth century and the 2GU is from the nineteenth century. Rapid changes in the world economy and sociology as well as the knowledge revolution have made clear that both systems need to be reformed to achieve better societal returns from education. A refocus is necessary for the improvement of university performance, especially in terms of their role in developing ecosystems and serving the region and the country. This is because the society grants resources to universities and expects a good social return on this investment. A university should thus perform efficiently in undertaking competence-based education and commercialized research activities with the resources granted to it. Accordingly, there is a sustained effort in reforming both the K-12 and tertiary education systems. At the university level, the emergence of 3GU and the EntU was a result of these efforts. Both are in the making without a definitive form and organization and new varieties are still needed to fill the gaps in different niches of societal requirements. A university can contribute to economic development at regional and national levels, especially in terms of technological progress and industrialization. Essentially, it takes force from close physical as well as mental proximity to the industry in establishing a very close collaboration with it. The principal societal benefits of university are (i) closing the competence gap in education and training and (ii) strengthening the research collaboration with the industry and thus better commercialization of knowledge. The university

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may have an even higher promise to develop industrial layer developing countries. The university may, thus, increase the effectiveness of industrial policies, accelerate technical progress and support successful technological gap closure with the developed nations. The university has a global perspective and at the same time, it has the premise that the university shall engage local capacity to manufacture and transform this for improved economic growth through education, training, and research in collaboration with private and public sectors. University may relate to the need of the immediate society and economy and be responsive to local communities through innovation and technological progress. The concept of a university may differ when relating to these different contexts depending on the resources available but the emphasis on innovation and technological advancement through scientific research may help with better performance to fulfill the functions of a university in general.

References Alba-Ramirez, A. 1993. ‘Mismatch in the Spanish labor market: Overeducation?’. Journal of Human Resources 28: 259–278. Al-Khalili, J. 2011. The house of wisdom: How Arabic science saved ancient knowledge and gave us the Renaissance. New York: Penguin. Amsden, A. H. 1992. Asia’s next giant: South Korea and late industrialization. New York: Oxford University Press. Ansell, B., & Gingrich, J. 2017. ‘Mismatch: University education and labor market institutions’. PS: Political Science and Politics 50, no. 2: 423–425. Baglieri, D., Baldi, F., & Tucci, C. L. 2018. ‘University technology transfer office business models: One size does not fit all’. Technovation 76: 51–63. Becker, S. O., Hornung, E., & Woessmann, L. 2011. ‘Education and catch-up in the industrial revolution’. American Economic Journal: Macroeconomics 3, no. 3: 92–126. Bennett, C. A. 1926. History of manual and industrial education up to 1870, Vol. 2. Peoria, IL: Manual Arts Press. Boli, J. Ramirez, F. Mayer, J. 1985. ‘Explaining the origins and expansion of mass education’. Comparative Education Review 29, no. 2: 145–170. Buenstorf, G., & Koenig, J. 2020. ‘Interrelated funding streams in a multi-funder university system: Evidence from the German Exzellenzinitiative’. Research Policy 49, no. 3: 103924. Cantoni, D., & Yuchtman, N. 2014. ‘Medieval universities, legal institutions, and the commercial revolution’. The Quarterly Journal of Economics 129: 823–887. Carl, J. 2009. ‘Industrialization and public education: Social cohesion and social stratification’. In: International handbook of comparative education, pp. 503–518. Dordrecht: Springer. Chichilnisky, G. 1998. ‘The knowledge revolution’. Journal of International Trade & Economic Development 7, no. 1: 39–54. CIPD. 2018. Over-skilled and underused: Investigating the untapped potential of UK skills. London: Chartered Institute of Personnel and Development.

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Degl’Innocenti, M., Matousek, R., & Tzeremes, N. G. 2019. ‘The interconnections of academic research and universities ‘third mission’: Evidence from the UK.’ Research Policy 48, no. 9: 103793. Etzkowitz, H. 1993. ‘Technology transfer: The second academic revolution’. Technolog y Access Report 6: 7–9. Etzkowitz, H. 2008. The triple helix: University-industry-government innovation in action. London: Routledge. Etzkowitz, H., & Leydesdorff, L. 1995. ‘The triple helix: University – industry – government relations: A laboratory for knowledge-based economic development’. EASST Review 14: 14–19. Gatto, J. T. 2000. The underground history of American education. New York, NY: Oxford Village Press. Gordon, H. R., & Schultz, D. 2020. The history and growth of career and technical education in America. Long Grove: Waveland Press. Groot, W., & van den Brink, H. M. 2000. ‘Skill mismatches in the Dutch labor market’. International Journal of Manpower 21, no. 8: 584–595. Kantor, S., & Whalley, A. 2014. ‘Knowledge spill overs from research universities: Evidence from endowment value shocks’. Review of Economics and Statistics 96, no. 1: 171–188. Kaviani, R., N. Salehi, A. Z. B. Ibrahim, M. R. M. Nor, F. A. F. A. Hamid, N. H. Hamzah, and A. Yusof. 2012. “The Significance of the Bayt Al-Hikma (House of Wisdom) in Early Abbasid Caliphate (132A. H-218A. H).” Middle East Journal of Scientific Research 11: 1272–1277. Krugman, P. 1994. ‘The myth of Asia’s miracle.’ Foreign Affairs 73: 62–78. Liefner, I., Si, Y. F., & Schäfer, K. 2019. ‘A latecomer firm’s R&D collaboration with advanced country universities and research institutes: The case of Huawei in Germany’. Technovation 86: 3–14. Lopez, R. S., & Lopez, R. S. 1976. The commercial revolution of the middle ages: 950–1350. Cambridge: Cambridge University Press. Lowe, C. U. 1982. ‘The triple helix – NIH, industry, and the academic world’. The Yale Journal of Biolog y and Medicine 55: 239–246. Meshchaninov, Y. 2012. The Prussian-industrial history of public schooling. Mineo: The New American Academy. Mokyr, J. 2000. ‘Knowledge, technology, and economic growth during the industrial revolution’. In Productivity, technolog y and economic growth, pp. 253–292. Boston, MA: Springer. Mokyr, J. 2002. The gifts of Athena: Historical origins of the knowledge economy. Princeton, NJ: Princeton University Press. Mokyr, J. 2018. The economics of the industrial revolution, edited by J. Mokyr. New York: Routledge. Park, C. 2007. ‘PhD quo vadis? Envisioning futures for the UK doctorate’. In Skills training in research degree programmes: Politics and practice, edited by Hinchcliffe, R., Bromley, T., & Hutchinson, S. London: Open University Press. Pawłowski, K. 2009. ‘The “fourth generation university” as a creator of the local and regional development’. Higher Education in Europe 34, no. 1: 51–64. Peneder, M. 2001. Entrepreneurial Competition and Industrial Location. Cheltenham: Edward Elgar Publishing.

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Rajalo, S., & Vadi, M. 2017. ‘University-industry innovation collaboration: Reconceptualization’. Technovation 62: 42–54. Reichert, S. 2019. The Role of Universities in Regional Innovation Ecosystems. EUA Study, European University Association, Brussels, Belgium. Roberts, R. W. 1957. Practical and vocational arts education. New York: Harper and Row. Rodrik, D. 2009. ‘Industrial policy: Don’t ask why, ask how’. Middle East Development Journal 1, no. 1: 1–29. Romer, P. M. 1990. ‘Endogenous technological change’. Journal of political Economy 98, no. 5: 71–102. Sabato, J., & Mackenzie, M. 1982. La producción de tecnología. Autónoma o transnacional. Mexico:Nueva imagen. Schleunes, K. A. 1979. ‘Enlightenment, reform, reaction: The schooling revolution in Prussia’. Central European History 12, no. 4: 315–342. Schofer, E., & Meyer, J. W. 2005. ‘The worldwide expansion of higher education in the twentieth century’. American Sociological Review 70, no. 6: 898–920. Struck, F. T. 1930. Foundations of industrial education. New York: John Wiley & Sons Incorporated. The Economist. 2015. ‘Excellence vs equity – Special report on universities’. 28 March 2015. https://www​.economist​.com​/sites​/default​/files​/20150328 ​_ sr​_univ2​.pdf. Theuerkauf, W. E., and A. Weiner. 1993. “The German Dual System of Vocational Education and Implications for Human Resource Development in America.” Paper presented at the American Vocational Association Convention (Nashville, TN, December 6, 1993). Valero, A., & Van Reenen, J. 2019. ‘The economic impact of universities: Evidence from across the globe’. Economics of Education Review 68: 53–67. Van Vught, F. 1999. ‘Innovative universities’. Tertiary Education and Management 5, no. 4: 347–355. Wade, R. 2004. Governing the market: Economic theory and the role of government in East Asian industrialization. Princeton, NJ: Princeton University Press. Walter, R. A. 1993. ‘Development of vocational education’. In Vocational education in the 1990s II: A sourcebook for strategies, methods, and materials, edited by Craig Anderson and Larry C. Rampp. Ann Arbor, MI: Prakken. Wissema, J. G. 2009. Towards the third generation university: Managing the university in transition. Cheltenham: Edward Elgar Publishing. Yulek, M. A. 2018. How nations succeed. Singapore: Palgrave Macmillan US. Yulek, M. A., Lee, K. H., Kim, J., & Park, D. 2020. ‘State capacity and the role of industrial policy in automobile industry: A comparative analysis of Turkey and South Korea’. Journal of Industry, Competition and Trade 20, no. 2: 1–25. Zhang, Y., Chen, K., & Fu, X. 2019. ‘Scientific effects of Triple Helix interactions among research institutes, industries and universities’. Technovation 86: 33–47.

Chapter 14 LEARNING IN THE INDUSTRIAL UNIVERSITY Murat A. Yülek and Ahmet Uludag

Introduction Manufacturing is the engine of economic growth and development (Kaldor 1966, 309–319; Szirmai 2013, 53–75; Yulek 2018), which critically supports development. The close association between manufacturing and growth is well documented. The university has a high potential to support industrial development. Research indicates a positive relationship between universities and growth (Valero and Van Reenen 2019, 53–67). Yet, whether the university functions as an efficient or ganization in co nverting pu blic an d pr ivate resources granted to it into satisfactory outcomes for society remains an important question. The university trains its students for the labor market. However, it is no longer the only social institution providing educational services, and university enrollment rates are weakening in some countries. Many competing formal and informal education services are provided by, among other things, on-the-job-learning (or, learning-by-doing) at industrial and non-industrial firms, banks, professional and vocational training institutions, research institutions or public administrations—all of which provide educational services covering the same or similar sets of knowledge. Recently, the exponential growth in university diplomas similar to the high-school diploma explosion in the 1950s and 1960s in the USA and 1980s in Turkey has degraded the value of university diploma. Online university diplomas have also been adding to diploma explosion. It is another reason why university enrollment rates fall in the USA (Nadvorny 2019) and slowing down in Europe (Teichler and Bürger 2015). The university cannot be indifferent t o h ow i t c an s erve s ociety b etter in education and development. The university ecosystem has been

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changing slowly from the so-called 1GU of the medieval times, to the 2GU and then to the 3GU (Wissema 2009; Lukovics and Zuti 2017). The way teaching and research are conducted in the universities and propagated to society is still evolving. Nevertheless, most world universities today are still 2GUs, while even the general 3GU framework does not adequately address the ever-changing dynamics of development in an age of rapid transformations. This chapter postulates learning in the industrial university in response to the emerging challenges and posits a new sub-type—the IndU—to respond to specific new challenges in the education and research functions of the university. There is no shortage of university types; besides, there are many conceptual models to idealize high-performance universities. The IndU is by no means agreed upon by teams of experts. Instead, this conceptualization of the IndU is an effort to create some benchmarks that will provide the basis for debate and analysis of the IndU, especially in regions where manufacturing transformations are needed. This chapter also points to relevant characteristics necessary for a 3GU the IndU. 3GU the IndU is a model in which the university is close to industrial establishments. It is not just physical closeness. It is also a mental closeness for strong collaboration and cooperation to educate and train students and develop new innovative products, processes and technologies. It is two-way cooperation and collaboration. The industrial establishment offers learning, research, projects and implementation, while the university offers know-how, process and technology. The informal learning offered by the industrial establishment should not be just one-way support from industrial establishments to the university. For meaningful cooperation, it needs to go both ways. The IndU also needs to support industrial establishments with its know-how and research capabilities.

Cost of University Education and Value of University Diploma: Modern Times Education increases the human capital of the labor force, the innovative capacity of the economy and, finally, the diffusion an d tr ansmission of knowledge promoting economic growth through the implementation of new technologies (Hanushek and Woessmann 2020, 171–182). The twentyfirst century learning and innovation skills framework emerged to promote creativity and innovation, critical thinking and problem solving as well as communication and collaboration (Partnership for 21st Century Skills 2009). The USA has been subject to increasing competitive pressure from emerging economies such as China and India (Friedman 2005, 33–37). Sanders

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(2008) underlines that the competitive pressure is closely related to education: “China and India were on course to bypass America in the global economy by outSTEMming us.” Consequently, funding “towards all things STEM, and STEMmania set in a decade” increased rapidly (Sanders 2008). Besides, there is strong evidence that “the cognitive skills of the population – rather than mere school attainment – are powerfully related to long-run economic growth” (Hanushek and Woessmann 2020, 171–182). 2GU and 3GU university education have been facing strong criticisms for lack of effectiveness and efficacy. Today university graduates are expected to acquire more than a diploma; the labor market requires fundamental skills and competencies but mostly theoretical and formal learning at a closed setting of university campuses appear to fail to provide market-demanded skills and competencies. The market demand and focus are on highly competent, skillful and technologically savvy graduates who are maker engineers and scientists. That requires a new type of education directly pertinent to the growing need for higher cognitive skills and knowledge capital (Hanushek and Woessmann 2020, 171–182) as well as university and industry integration (Table 14.1). In summary, significant criticisms for traditional 2GUs and 3GUs are lack of industry and university integration for market-demanded competencies, graduates with degrees without adequate, real hands-on, practical and vocational competencies, focus on teaching and not on research for innovation and technology, and high cost of education versus value offered for a future professional career.

The Case of Stanford University In the USA, the entrepreneurisation of universities has followed a straightforward path from teaching to research to economic development (Etzkowitz and Zhou 2017). It is probably best characterized by the pioneering experience of Stanford University after the Second World War, when the newly appointed Dean of Engineering, Frederick E. Terman, reshaped the institution into a research university with very close collaboration with industry (Lenoir 2014). The strategic objectives of Stanford led by Terman were (i) sectoral focus: to develop strong electronics graduate and research programs with a curriculum featuring a solid education of physics as well as social sciences; (ii) focus on the best talent: to attract the best researchers in the USA to Stanford; (iii) seeking public funds: aggressively pursue receiving funds from governmental R&D funding institutions; and (iv) institute close collaboration with industry: to urge the faculty to establish start-ups or to work closely with industry.

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Table 14.1  Challenges faced by the university in terms of the traditional and societal

expectations. Area

Challenge

Response

Traditional expectations Education and Closer relationship with the • Competence mismatch training: market through, between university teaching and teaching and market • Getting the industry involved vocational demand for graduates is a in and reviewing the aspects growing problem. curricula and course contents. • Slow adaptation to • More classes of applied technological progress. teaching, more student time • Alternative education spent in industry. institutions. Research: New • Other actors such as firms, • More involvement of the knowledge non-university research faculty in industry projects creation institutions produce new through joint projects and valuable knowledge as well. similar activities. New expectations The university has to have a Commercialization The university has been much closer relationship with of knowledge quite inactive in the the industry and the channels commercialization of to commercialize the knowledge it generated. technological and scientific knowledge it generates should be readily shared with the industry. The university should be Regional actor The university has been an active actor in the inactive also in terms of region. Close relationship being an active actor in with industry, public the region in providing administrations (including services to analyze and funding institutions), NGOs solve the region’s issues. and entrepreneurs are critical. The invisible Chinese walls between the university and the region should come down.

Proximity to industrial park, acquisition of talent and quality research

Stanford would be at the center of a techno-industrial ecosystem (Lenoir 2014), which could be considered a version of a technological industrial layer (Yulek 2018). Terman established Stanford Research Park (SRP) in 1951, where technological start-ups and mature companies would lease space to

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build facilities in cooperation with the City of Palo Alto. The first tenant of SRP came in 1953: Varian Associates, a company established by Russ and Sigurd Varian, both students and close associates of Terman. Another company, Hewlett-Packard, was established by two students of Terman, Bill Hewlett and David Packard. Terman’s vision was to establish a broader ecosystem consisting of not only buildings and land owned by the university and SRP but also to include a “technical community of scholars from regional electronics firms in the Bay Area and the West Coast with research facilities near Stanford staffed by Stanford-trained engineers” (Lenoir 2014). Stanford would benefit from the community in the form of knowledge and experience sharing for the problems that the industry wants to solve. Regional firms acted as receivers of the university’s research results while to some limited degree, funding some of the research programs. Stanford mainly supported this ecosystem with new high-technology start-ups (Lenoir 2014). Stanford alumni and faculty directly or indirectly established 1854 technology start-ups in Silicon Valley, representing 37 percent of all high-tech employment in the region (Lenoir 2014). Quality research was Terman’s priority; he endorsed funding applications by top research talent above a certain level of sophistication. Thus, market-oriented engineering research “as opposed to applied science and federal funding as an incubator of industrially relevant research” have been vital to Stanford’s success (Lenoir 2014, 122).

Need for New University Configurations: IndU The IndU is a model of a university which is close to industrial parks in proximity mentally and physically, has a clear vision and mission for integration with industry, and promotes entrepreneurship and vital informal learning for market demanded competencies. The IndU requires a strong education and training embedded with practical and hands-on learning in workplaces, proximity to industrial parks and zone, substantial value and ethics training and a keen interest in entrepreneurship. The IndU focuses on manufacturing and cutting-edge engineering technologies for innovation to propel development. The IndU is an alternative to a traditional university to settle some of the criticisms. Based on the critical societal expectations, the university has to respond to the following key challenges: 1. Does the university train an adequate number of students qualified with the market demanded competencies (skill, knowledge and experiences)?

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2. Does it produce knowledge and technology that are adequately beneficial to society? 3. Does the university adequately transfer knowledge and technology to society? To serve society better and address the challenges, the university must change, or different university types must emerge. The university slowly changed from 1GU to 2GU, and now attempts to shift to 3GU are underway. Wissema (2009) underlines that 2GU “is a dying model, and we are currently in a transition period towards the Third Generation University.” The world needs different types of universities and 3GU offers some valuable aspects to address these challenges. Teaching and vocational aspects: Education and training

There is evidence of a positive relationship between existing university configuration and regional and national economic growth and development (Kantor and Whalley 2014, 171–188) through education. Valero and Van Reenen (2019) report that a 10 percent increase in the number of universities (roughly, adding one more university in the average region in their data set) increases that region’s income by 0.4 percent with additional positive externalities in the neighboring regions, further reporting that establishing a new university would provide positive net economic benefits (i.e., benefits minus annual cost) to the region in the United Kingdom. Many universities today have become academic educational institutions without adequately providing market-compatible knowledge and skills to the students, limiting the benefits of a university education. The university has distanced itself from the realities of the job market. The result is a significant mismatch between the demand for skills by employers and the skills that the students can gain during university education. New knowledge generation and innovation

University researchers are generally not aware of the trends in the technology marketplace as their recruitment and tenure are independent of the market realities and largely driven by academic productivity. Another significant factor is the technology cycle defined as how fast technologies change or become obsolete over time ( Jaffe and Trajtenberg 2002; L ee 2013, 2017, 201–224; Park and Lee 2006, 715–753). Universities generally concentrate on long-cycle technologies which have a lower return on investment in the short run. The firms, on the other hand, aware of the market realities, generally

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concentrate on short-cycle technologies. A close relationship with the industry may thus help the university redirect its research activity, which can help optimize the return on research investment like Stanford experience. Knowledge has become the most critical factor in generating economic value added. At the same time, research and knowledge generation are characterized as a multi-actor process (Reichert 2019), and the university is no more the only major knowledge producer. The other actors, primarily the firms and non-university research institutions, have become significant knowledge producers. Further, these actors commercialize the knowledge they generate much more effectively than the university. Continuing as 2GU, the university is risking to become marginalized (Van Vught 1999, 347–355), or some universities may even become irrelevant in terms of research activities. The collaborative setting will increase access to public research funds and their efficiency regarding the value that the public funds generate for society. Commercialization of knowledge

Technology transfer from the university to industry is a major function of the university for industry and society (Baglieri, Baldi and Tucci 2018, 51–63). Most universities cannot fund themselves through commercialized R&D but do so from governmental or societal resources. In a way, society subsidizes academics almost as a privileged social class. However, the history of the development of the universities had much different economic dynamics. Furthermore, the efficiency and effectiveness of social subsidy to universities are generally not accounted for through studies such as economic impact analysis. It is evident that university–industry collaborations are critical to success as some university–industry research collaborations succeed and others fail (Rajalo and Vadi 2017, 42–54). New forms are being experimented with (Baglieri, Baldi and Tucci 2018, 51–63). The nature of innovation and commercialization is the critical backdrop in determining the success of the university–industry collaboration. Nevertheless, the university should distinguish between short-term solution-oriented, quickly commercialized research and long-term, high-risk open-ended research (Reichert 2019). University as a regional actor

The university, in most cases, has remained an ivory tower, distanced from the realities and problems of regions they are located. The university has also distanced itself as an implementer of specific tasks, the most important of which is to educate talent in the region. For example, economic literature

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has emphasized that industrialization triggers economic growth (Yulek 2018; McCausland and Theodossiou 2012, 79–92). The university can be a key actor in the region’s industrialization and subsequently revamped economic growth similar to Stanford Research Park and Ostim Industrial Zone. Regional development

The IndU is also an entrepreneurial university. It should contribute to regional development as a central entrepreneurial actor (Hagen 2002, Vol. 15; Reichert 2019; Veugelers and Del Rey 2014) through various roles and collaborative formats while learning from the regional industry. It should also be an actor leading necessary change in the region. Knowledge transfer and cooperative structures are vital for regional development, as are common cultural values, norms and narratives that create a shared sense of purpose among people (Reichert 2019). Ostim Technical University founded by an industrial zone providing manufacturing and servicing to the region and all of Turkey for over 50 years embodies such an endeavor. University as a conveyor of values

In some fields, such as medicine, the students show at least some-mostly symbolic gestures of value instilling, such as the Hippocratic oath and white coat-wearing ceremonies. The IndU prioritizes value-driven education and embodies values not only in regular subject classes but also in social classes. Master–apprentice model of education is a method to offer social values to students. The IndU requires a valued driven mission to support both social and economic development similar to Ostim Industrial Zone’s achievement over the past 50 years.

Learning at the IndU The IndU has a specific way of teaching and a flexible research collaborations and networks, concentrates on short-cycle research areas and situates in the proximity of industrial parks. A recent example is Ostim Technical University, an institution established within Ankara’s (Turkey) industrial basin consisting of 15,000 enterprises and over 200,000 thousand blue- and white-collar employees. The majority of the companies in the industrial basin are industrial small and medium businesses and, on average, have high-level productive capabilities with a mid-to-low level technological and R&D capabilities. In reforming the university to address the challenges, instead of a one-size-fits-all approach and a blueprint, a better strategy is to offer various university types. The IndU

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Learning in the Industrial University Table 14.2 University types and characteristics.

Main characteristics Teaching existing theoretical knowledge Undertaking R&D activities Undertaking R&D in short-cycle technologies Addressing new needs Location within manufacturing region Close collaboration with industry in commercializing knowledge Active participation in regional development Employing industry professionals as lecturers Instilling values Providing students with industrydemanded skills Close collaboration with industry in determining and reviewing the curriculum

IndU

3GU

2GU

1GU

Yes

Yes

Yes

Yes

Yes Yes

Yes Yes

Yes No

No No

Yes

Not necessarily Not Necessarily No

Fully

Yes

Fully

No

Yes

No/not necessarily No

Yes

Yes

No/limited

No

Yes Yes

Limited Limited

Limited Limited

No No

Yes

Yes

No

No

No

would share the main characteristics of 3GU but with some additional features to increase its collaboration with industry (Table 14.2). Physical proximity to the industry

In the traditional university setting, the university is located far from industrial parks and zones in the region. In open innovation environments, various tools such as collaborative spaces and co-location initiates are being used (Reichert 2019). The physical distance between academia and industry also creates a “mental distance.” This results in a complex understanding between academicians and industry cadres. Physical proximity helps communication channels to be open for collaboration. Physical proximity to the industry can thus help increase returns to academic research activity and commercializing knowledge. Competency-based learning to prepare for industrial settings

A principal role of the IndU is to contribute to the region by providing an adequate number of graduates with market-demanded competencies,

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Skills

Competence

Education Knowledge

Experience Ability

Behavior

Attitudes and Values

Figure 14.1 Learning in the IndU.

knowledge and skills. The debate on competence-based education goes back to the beginning of the twentieth century (Ashworth and Saxton 1990, 3–25). UK’s Further Education Unit (1984, 1985) defines competence as “the possession and development of sufficient sk ills, knowledge, appropriate attitude and experience for successful performance in life roles” (Ashworth and Saxton 1990, 3–25). We define competence slightly d ifferent (Figure 14.1) for the university students: “equipping the students with an optimal combination of knowledge, skills, ability, attitudes and values.” This could be combined with the Dreyfus model, defining s kill a cquisition in stages: novice, advanced beginner, competent, proficient, and expert (Dreyfus 2004). The knowledge content of university teaching should be reconfigured. As the curricula generally are a product of university academicians and are not shaped or designed by industry, the students often take extra-curricular professional training outside the university to increase their employment opportunities; or, if and when employed, to perform better at their jobs. Thus, departmental curricula should be reviewed or even supervised by industry representatives regularly or designed with the involvement of the industry. In achieving competence-based learning, the IndU should blend formal and informal learning in the university. Classroom-based formal learning at all levels of education is additional to informal learning. The guild apprenticeship system is an excellent example of that understanding (Boileau 2017; Collins and Kapur 2014; Eberle 2018, 44–53). Research results underline that throughout a lifetime, 70–95 percent of all learning occurs via informal learning outside schools; thus, investment in informal learning would provide a cost-effective way to increase public understanding of science (Merriam and Bierema 2013; Boileau 2017; Falk and Dierking 2010, 486–493).

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University–industry joint research and knowledge commercialization

Interaction between the university and industry may facilitate joint research. Joint appointments between the university and businesses in various forms, such as academicians working as consultants in firms or industry employees participating in academic research projects or even teaching at the IndU should be planned for greater collaboration. Start-ups established by university professors or students may also help for better interaction with the industry. A university should have a broader role in the region (Baglieri, Bald and Tucci 2018, 51–63; Veugelers and Del Rey 2014). Much closer relationship formats would significantly improve traditional interfaces such as technology transfer offices. Nevertheless, proximity to industry does not guarantee a close relationship. The IndU should be flexible in developing new frameworks of collaboration, which in many situations might require a case-by-case approach. Values and attitudes

Most universities have flashy v isions a nd m ission statements a nd strategic plans to emphasize social and moral values that they are committed but there are few channels to operationalize those values to educate their students and implement those values through social, economic and industrial projects. The IndU could contribute to the need to instill values in a variety of ways. First, the program curricula should include classes to strengthen value appreciation and internalization. Second, as in the medical programs, each program can organize oath-taking ceremonies. At Ostim Technical University, engineering departments undertake factory work cloth-wearing ceremonies where the societal responsibilities of an engineer are reminded akin to guild apprenticeship ceremonies in history. Traditional gownwearing ceremonies are celebratory while work cloth-wearing ceremonies are value-appreciation tools. Attitudes are as important as values in defining competence. Traditional university has almost entirely left that area to the industry when students graduate adding to the broad competencemismatch problem. At Ostim Technical University, students have to take three credit hour work-experience classes at firms for at least six semesters in addition to spending the final semester entirely in an industry. Classes are graded jointly by the mentor in the industry and academic advisor for industry-required skills and experience.

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Flexible administration

University ecosystems are regulated highly to meet certain quality and accreditation requirements making them less flexible and unresponsive to the fast-changing disruptive economic and technological powerhouses. Most importantly, one might argue that the components of the university ecosystem are missing innovation competencies. “Better university,” to fill gaps and contribute more to society cannot be a theoretical construct but should be a learning and evolving organization with overarching objectives for a flexible administration. A learning organization should evolve by learning from its and others’ experiences. This is easily true for the IndU. It is too early to draw a time- and case-proof the IndU blueprint (organizational chart and regulations). An evolutionary approach would be more appropriate for making a dynamically responsive structure.

Conclusion The IndU is one form of 3GU that can contribute to economic development at regional and national levels, particularly in terms of technological progress and industrialization. Essentially, it takes force from close physical and mental proximity to industry. The principal societal benefits of the IndU are (i) closing the competence gap in education and training and (ii) strengthening the research collaboration with industry and better commercialization of knowledge through collaborative research in short-cycle technologies. The developing countries have not been able to establish solid industrial layers. The IndU may have better promise for developing countries by strengthening the industrial layer. The IndU may increase the effectiveness of industrial policies, accelerate technical progress, and support successful industrialization and economic development. The IndU has a global perspective and, at the same time, has the premise that the university shall engage regional capacity to manufacture and transform this for improved economic growth through education, training, and research in collaboration with private and public sectors. However, regional and national challenges and realities may be different. Therefore, the IndU may relate to the need of the immediate society and economy and be responsive to regional communities through manufacturing capabilities. The concept of the IndU may differ when relating to these different regions depending on the resources available. However, the emphasis on manufacturing through physical and mental proximity may affect relative better performance to fulfill the functions of a university in general.

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References Ashworth, P. D., & Saxton, J. 1990. ‘On ‘competence’’. Journal of Further and Higher Education 14, no. 2: 3–25. Baglieri, D., Baldi, F., & Tucci, C. L. 2018. ‘University technology transfer office business models: One size does not fit all’. Technovation 76: 51–63. Boileau, T. 2017. ‘Informal learning’. In R. E. West (Ed.). Foundations of Learning and Instructional Design Technology. https://opentextbooks.uregina.ca/practicespace/chapter/ informal-learning/. Collins, A., & Kapur, M. 2014. ‘Cognitive apprenticeship’. In R. K. Sawyer (Ed.). The Learning Sciences. New York: Cambridge University Press. Dreyfus, S. E. 2004. ‘The five-stage model of adult skill acquisition’. Bulletin of Science, Technolog y & Society 24, no. 3: 177–181. Eberle, J. 2018. ‘Apprenticeship learning’. In International Handbook of the Learning Sciences, pp. 44–53. New York: Routledge. Etzkowitz, H., & Zhou, C. 2017. The Triple Helix: University–Industry-Government Innovation and Entrepreneurship. New York: Routledge. Falk, J. H., & Dierking, L. D. 2010. ‘The 95 percent solution’. American Scientist 98, no. 6: 486–493. Friedman, T. L. 2005. ‘It is a flat world, after all’. The New York Times, no. 3: 33–37. Hagen, R. 2002. ‘Globalization, university transformation and economic regeneration, A UK case study of public/private sector partnership’. The International Journal of Public Sector Management 15, no. 3: 205–219. Hanushek, E. A., & Woessmann, L. 2020. ‘Education, knowledge capital, and economic growth’. The Economics of Education 14: 171–182. Jaffe, A. B., & Trajtenberg, M. 2002. Patents, Citations, and Innovations: A Window on the Knowledge Economy. Cambridge: MIT Press. Kaldor, N. 1966. ‘Marginal productivity and the macro-economic theories of distribution: Comment on Samuelson and Modigliani’. The Review of Economic Studies 33, no. 4: 309–331. Kantor, S., & Whalley, A. 2014. ‘Knowledge spillovers from research universities: Evidence from endowment value shocks’. Review of Economics and Statistics 96, no. 1: 171–188. Krimsky, S., Ennis, J. G., & Weissman, R. 1991. ‘Academic-corporate ties in biotechnology: A quantitative study’. Science, Technolog y, & Human Values 16, no. 3: 275–287. Lee, K. 2013. Schumpeterian Analysis of Economic Catch-Up: Knowledge, Path-Creation, and the Middle-Income Trap. Cambridge: Cambridge University Press. Lee, K. 2017. “Smart Specialization with Short-Cycle Technologies and Implementation Strategies to Avoid Target and Design Failures.” In Slavo Radosevic et al., (Eds.). Advances in the Theory and Practice of Smart Specialization (pp. 201–224). Cambridge, MA: Academic Press. Lenoir, T. 2014. ‘Inventing the entrepreneurial university: Stanford and the co-evolution of silicon valley’. In T. J. Allen & R. P. O’Shea (Ed.). Building Technolog y Transfer Within Research Universities: An Entrepreneurial Approach. Cambridge: Cambridge University Press. Lukovics, M., and B. Zuti. 2017. “New Functions of Universities in Century XXI Towards ‘Fourth Generation’ Universities.” Academia. edu, 9.

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McCausland, W. D., & Theodossiou, I. 2012. ‘Is manufacturing still the engine of growth?’. Journal of Post Keynesian Economics 35, no. 1: 79–92. Merriam, S. B., & Bierema, L. L. 2013. Adult Learning: Linking Theory and Practice. New York: John Wiley & Sons. Nadvorny, E. 2019. ‘Fewer students are going to college. Here’s why that matters’. NPR, 16 December 2019. https://www​.npr​.org​/2019​/12​/16​/787909495​/fewer​-students​ -are​-going​-to​-college​-heres​-why​-that​-matters. Park, K. H., & Lee, K. 2006. ‘Linking the technological regime to the technological catch-up: Analyzing Korea and Taiwan using the US patent data’. Industrial and Corporate Change 15, no. 4: 715–753. Partnership for 21st Century Skills. 2009.‘A framework for twenty-first century learning’. http://www​.p21​.org/. Rajalo, S., & Vadi, M. 2017. ‘University-industry innovation collaboration: Reconceptualization’. Technovation 62: 42–54. Reichert, S. 2019. The Role of Universities in Regional Innovation Ecosystems. Brussels/ Geneva: European University Association. Sanders, M. (2009, December/January). “STEM, STEM Education, STEMmania.” The Technology Teacher, 20–26. Retrieved December 12, 2016, from https://vtechworks​.lib​.vt​ .edu​/bitstream​/handle​/10919​/51616​/STEMmania​.pdf​?sequence​=1​&isAllowed​=y. Szirmai, A. 2013. ‘Manufacturing and economic development’. In Pathways to Industrialization in the Twenty-First Century: New Challenges and Emerging Paradigms, pp. 53–75. Oxford: OUP. Teichler, U., and S. Bürger. 2011. The Changing Numbers and Composition of Student Enrolment in Europe and Japan. Kassel: Centre for Research on Higher Education and Work. Valero, A., & Van Reenen, J. 2019. ‘The economic impact of universities: Evidence from across the globe’. Economics of Education Review 68: 53–67. Van Vught, F. 1999. ‘Innovative universities’. Tertiary Education and Management 5, no. 4: 347–355. Veugelers, R., & Del Rey, E. 2014. The Contribution of Universities to Innovation, (Regional) Growth, and Employment. EENEE Analytical Report, No. 18. Brussels: European Commission. Wissema, J. G. 2009. Towards the Third Generation University: Managing the University in Transition. Cheltenham, UK: Edward Elgar Publishing. Yulek, M. A. 2018. How Nations Succeed. Singapore: Palgrave Macmillan US.

Chapter 15 A NEW, EFFECTIVE MODEL OF INDUSTRY–UNIVERSITY COOPERATION Vazgen Shavarsh Melikyan

When the time constants for change in technology respectively education in that technology differ widely, special ways of education are required. The training of highly qualified specialists in microelectronics is a such a case. In addition, this industry has several peculiarities which lead to other problems to the higher educational system. This chapter reports on a successful collaboration between an advanced IT-firm and a technical university. First, we will discuss the peculiarities inherent in IT-education.

Peculiarities Inherent in IT-Education The disbalance between the dynamics of the semiconductor industry and the educational system

Over the past 50 years the development of the semiconductor industry has shown very high rates of growth. That circumstance is vividly reflected in Moore’s law, according to which the number of transistors in an integrated circuit (IC) doubles every 18 months. Currently the number of transistors in ICs already surpasses several dozens of billions. This unprecedented rate of development has led not only to a change of IC complexity, but also of other basic parameters, in particular: ●

●

the clock frequency has racked up from several dozens of hertz (Hz) to several dozens of gigahertz (GHz); the capacity of memories today is measured in terabytes rather than kilobytes;

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the power consumption has moved to several kilowatts (kWs) from less than one Watt; the power density has moved up to several thousands of W/cm2; the gate length of complementary metal–oxide–semiconductors (CMOS) transistors has been reduced from several micrometers to three nanometer (nm).

All this has been achieved thanks to the application of constantly renewed technological processes of IC manufacturing. Where micrometer technologies were used a few years ago, today 65, 45, 22, 14, 7, 5 and 3 nanometer technologies have become a reality. The transition of every new technology is considered a “revolution” in the semiconductor industry. The reason is that each transition rearranges the significance of different physical phenomena occurring inside a IC, which necessarily leads to the development of specific new methods of IC design as well as corresponding new Electronic Design Automation (EDA) tools at each transition. In particular, the role of leakage currents up to 0.13 micrometer technology was rather small. Starting from that technology the role of leakages has gradually increased and has become a decisive issue. Besides leakage, other challenges are the struggle against the constantly increasing effects of interconnects, noises, crosstalk, power consumption and others. It constantly requires a search for new ways to increase yield, reliability, manufacturability, reduction of power dissipation, time-tomarket, etc. It is especially worth emphasizing that, over the years, the period of each transition into a new technology has been reduced considerably. For instance, it took only several months to pass from 5 nm to 3 nm technology. It means that a continuously shorter period of time is available for mastering the peculiarities of new technologies from the viewpoint of IC design, fabrication and testing and for the creation of new EDA tools necessary for that. Summarizing we can conclude: 1. Microelectronics is one of the most dynamic areas in industry, if not the most dynamically changing one. 2. Dramatically rapid changes occurring in that area require frequent corresponding changes of the tools and methods used in IC design, manufacturing and testing. 3. All the above requires a provision of high quality, dynamically changing specialists with state-of-the-art knowledge and a continuous renewal of their educational level. 4. Regarding the flexibility of university education in terms of the speed of improving knowledge of the learners, it does not always coincide with the rate of microelectronics growth, rather, it lags behind. This has its natural

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grounds, such as the huge effort required for creating new curricula and educational materials, the difficulties of organizing the training of appropriate lecturers and students at the universities and so on. In short, there is a gigantic incompatibility between the paces of microelectronics development and the development of educational systems. Extreme necessity to combine theoretical knowledge and practical skills

The combination of fundamental knowledge and practical skills is especially important in microelectronics. The reason is that IC development is conditioned mainly by the research of new physical phenomena, which requires the creation of models based on new principles, the development of new algorithms and other theoretical issues. It requires mastering of profound theoretical knowledge of specialists involved in the area. Simultaneously the success of IC design is mainly conditioned by skilled use of state-of-the-art EDA tools, new methodologies of measuring IC parameters and so on. EDA tools, in their turn, also become powerful at a brisk pace and qualitative changes occur in the course of time, and without mastering those skills, specialists will come across difficulties to ac hieve success in se miconductor industry. In addition to profound theoretical knowledge and the necessity to acquire simultaneously practical skills, their tight combination is characteristic of the area as well. As theoretical knowledge is strictly aimed at practical requirements, the creation of EDA tools is also rather science intensive. Unattainability of hardware and software to universities necessary for studying microelectronics

Hardware with powerful calculating capabilities as well as complex EDA tools are used in IC design. These two components are rather expensive and, as a rule, practically unavailable for educational institutions. Inaccessibility of technological information to educational institutions

It is impossible to implement studies of IC design and testing methods, methodologies of creating EDA tools and IC fabrication-related issues, without the use of appropriate technological information in microelectronics. In particular, this refers to the models of various electronic components, design rules, standard digital and I/O libraries, intellectual property (IP) blocks, design kits and so on. The lack of such technological information in

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educational process makes it impossible for the trained specialists to become fully proficient of all the peculiarities of contemporary microelectronics. Universities lack all the mentioned capabilities because of various objective consequences.

A New Model for Collaboration In order to address the above outlined challenges, in 2001, the National Polytechnic University of Armenia (NPUA), in conjunction with an Armenian company called Leda Systems, established a new University-Industry educational model by opening the “Microelectronic Circuits and Systems” Chair. There were many graduates who didn’t meet the requirements of the Company to be hired. In 2004, Synopsys Inc, one of the worldwide leading companies in the field of microelectronics, acquired Leda Systems by signing a cooperation agreement between NPUA and Synopsys Armenia CJSC and has since continued to be NPUA’s major educational partner by contributing to the University–Industry educational model. The distinctive objective of this University–Industry educational model is to train highly qualified specialists who will meet the qualification requirements of semiconductor industry. Only through this model it is possible to provide the semiconductor industry companies with highly qualified specialists from the educational sphere. That is why the cooperation of NPUA, as the leading technical educational institution of Armenia, with the Armenian branch of Synopsys, Inc. has been well-motivated from the beginning and has become an effective model for educating and graduating qualified IC design and EDA engineers in Armenia. Thus, new, efficient model, based on such cooperation, created in the framework of Synopsys University Programs, and realized in Armenia, was created. Within the frames of the established educational model, Synopsys Armenia provided the NPUA Chair of Microelectronic Circuits and Systems with classrooms, laboratories, Synopsys EDA tools, computer hardware and software, professors’ salaries, students’ scholarships, professors’ training, development of the University’s technical infrastructure and employment offers to students upon successful graduation. Synopsys provided NPUA with 70 complete packages of the company’s commercial EDA tools, and the total value of the tool contribution was 350 million dollars. The tools are used in practical, laboratory, diploma works, Master theses and PhD dissertations. Each student has a PC in the classroom which is connected to the educational network where Synopsys tools are installed. The professors are trained by Synopsys’ leading specialists. NPUA provides the curricula and the best professors and students.

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Studies are carried out in specially equipped classrooms donated by Synopsys, located both in Synopsys Armenia offices an d at th e un iversity. Classrooms are constantly renovated and equipped with computer hardware, networks, software, and the latest EDA software tools from Synopsys. Teaching, course projects, diploma works, Master theses and PhD dissertations are targeted toward real industry projects at Synopsys Armenia. Students study their third and fourth years of the Bachelor program as well as the Master and PhD programs on the premises of the Company. This can be viewed as “University goes to Industry.” Studies in all educational programs have been implemented: Bachelor, Master and PhD. Studies include IC Design and EDA specializations. Students obtain their basic education (mathematics, physics, etc.) in the first years of the Bachelor program from the University. After acquiring a basic technical education (after the fourth or fifth semesters), the best students are selected on competitive grounds to participate in the joint Industry–University educational program. Further education in the Bachelor program as well as Master program and PhD studies is conducted using specially developed curricula. The curricula are developed taking into consideration the contemporary requirements of leading companies in the semiconductor industry, along with involvement of the area’s best professionals and professors and oriented to the use of Synopsys EDA tools. Every student has the necessary state-of-the-art hardware and an opportunity to use the donated EDA tools. Teaching as well as supervision of course projects, diploma works, Master theses and PhD dissertations are realized jointly by the leading professionals of the Company and experienced professors of the University. Diploma works, Master theses and PhD dissertations and are close to the real projects of the Company. As key results of fruitful cooperation within the established cooperation between NPUA and Synopsys Armenia the following achievements have been realized: high appreciation of efficiency an d qu ality of th e re alized educational programs by Community, qualitative and quantitative rise of NPUA students, involvement of best specialists in microelectronics in teaching, demand for graduates and high employment percentage, full provision of educational materials, presence of state-of-the-art hardware and software, continuous expansion of scientific-research activities (participation in international conferences, publications, etc.), successful defenses of PhD dissertations and involvement of PhD students in teaching.

Application of the Model in Other Universities Taking into consideration the peculiarities of each university, different modifications of the presented educational model have been applied in the

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framework of cooperation with the following universities of Armenia as well: Yerevan State University (YSU), Russian–Armenian Slavonic University, European University. In 2005, a cooperation agreement was signed between Synopsys Armenia and YSU. Simultaneously, Synopsys provided YSU with 30 complete packages of the Company’s commercial EDA tools. The total value of this contribution amounted to 150 million dollars. Synopsys also provided computer hardware and software, professors’ salaries, students’ scholarships, professors’ training and employment offers to students upon successful graduation. The characteristic of this modification of the Industry–University educational model is that classrooms and laboratories are located on the premises of the University. Students study their sixth, seventh and eighth years of the Bachelor program on the premises of the University in the classrooms and laboratories donated and equipped by Synopsys. This modification can be viewed as “Industry goes to University.” In 2007, the Russian–Armenian Slavonic University (RAU) became a member of Synopsys’ Worldwide University Program. Synopsys Armenia has established “Microelectronic Circuits and Systems” Chair in RAU, providing 50 complete packages of the company’s commercial EDA tools, and the total value of the tool contribution was 260 million dollars. RAU provided equipped classrooms. In 2011, the European University became a member of the Synopsys Worldwide University Program through a cooperation agreement between VivaCell-MTS, Synopsys Armenia CJSC, and the university. The program has also established the “Microelectronic Circuits and Systems of Communication Facilities” Chair based at the university. Going forward, the presented Industry–University educational model will be further developed. Constant modernization of the curricula, its standardization, and involvement of the needs of other Companies in the microelectronics industry located in Armenia are among the opportunities for enhancing this valuable and effective model. The described University–Industry cooperation model is illustrated in Fig.15.1.

Evaluation The valuable experience of applying this model has demonstrated so far that it is one of the most effective ones among the existing other university– industry cooperation models. This is witnessed by the achievements over the 20 years since this educational model has been implemented.

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Figure 15.1 Industry–university educational cooperation model.

●

●

As of today, about 1000 students have graduated from the program. All graduates choose to work for Synopsys or other local IT companies. Approximately 75 percent of the program graduates work for Synopsys Armenia which amounts to 51 percent of all Synopsys Armenia employees. The NPUA “Microelectronic Circuits and Systems” Chair has created a complete set of full educational curriculum. All the courses have full methodical materials—syllabus, lectures, labs, course projects, homework, exams and soon. The curriculum of IC Design is oriented at training such specialists who will be able to design contemporary ICs, IPs, digital standard cell and I/O libraries as well as special I/Os (SSTL, HSTL, LVDS, MLVDS, USB, DDR, etc.) using Synopsys EDA tools. The EDA curriculum is oriented at training such specialists who will be able to create different types of EDA tools (for digital circuits—synthesis, simulation, place and route, physical verification, etc., for analog circuits—simulation, physical design, layout

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verification, etc. as well as design for manufacturing (DFM), design for test (DFT), etc.). Scientific seminars are anticipated in the last two semesters of the Master program, which allows discussion of the problems in the domain of their Master theses. All the basic and large volume courses include course projects and works which are carried out by the use of Synopsys EDA tools. All developed curricula meet the requirements of leading microelectronics companies and can be applied not only for students’ study, but also for retraining specialists. The courses are modular in structure which means that they can be used in their entirety or selected components can be used to augment existing programs or used as the base to create a completely. The Synopsys Worldwide University Program provides access to over 125 full semester courses for Bachelor and Master degree programs (59 of which are used in NPUA classes). Curricula developed by the NPUA “Microelectronic Circuits and Systems” Chair are used by the world’s leading universities in over 65 countries. These educational materials are available in four languages (Armenian, Russian, English and Chinese) and are posted on the Synopsys University Program web page. Students are encouraged to attend and submit their research work to conferences such as IEEE East-West, Design & Test (Ukraine), Problems of Developing Advanced Microelectronic Systems (Russia), Asia-Pacific Conference on Postgraduate Research in Microelectronics & Electronics (India), Electronics (Bulgaria), Moscow-Bavarian Joint Advanced Student School (Germany, Russia) and Electronics, Telecommunications, Computers, Automatic Control and Nuclear Engineering (Montenegro). In the past 20 years, more than 200 student papers have been presented at various conferences, many of which have received the conference “Best Paper” Award. Students are also actively involved in research activities with Synopsys and other international organizations such as EU Terminator, TEMPUS projects, the Black Sea Economic Cooperation Organization (BSEC), and the Armenian-Belarusian research projects. The results of the scientific research—various integrated circuits and intellectual property nodes, Educational Design Kits, EDA tools which are widely used in many of the world’s leading universities. 100 percent of the 65 PhD graduates from the NPUA program successfully defended their PhD dissertations and now all currently work at Synopsys Armenia. 20 of these PhDs also teach at NPUA.

Recent Development To further increase the effectiveness of the model, the latter has advanced one more step and the idea of Student Working Group (SWG) has been formed.

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The essence of SWGs is as follows: SWG represents a group of small number of interns (usually eight), who combine their studies in SAED with part-time employment. Their employment is realized not in the business environment of the Company but in SAED. They mainly realize two types of projects: 1. Educational projects, the customers of which are both SAED, and other educational institutions. Such projects can be development of various educational EDKs and iPDKs for different technology nodes, design of ICs and IPs, on the basis of which educational courses are developed. 2. Industrial projects the customers of which are both Company Business Units (BUs) and other Companies. Such projects may include: the development of DRC and LCS runsets for different EDA tools and technology nodes, various libraries and so on. During the implementation of those projects the interns acquire experience to realize projects close to real foundry projects while they gain teamwork experience as well. In SWGs students gain more work experience and when they are ready for employment, they can be hired by business units of the Company. The hired interns are substituted by new top performing SAED students. During their internship students are tasked with educational and industrial projects. The students participating in SWG get hands-on experience designing integrated circuits and teamwork experience. It is a noteworthy fact that 80.5 percent of SWG interns have been hired by Synopsys. The Groups have developed the most contemporary 90 nm, 32/28 nm and 14 nm Educational Design Kits and Interoperable Process Design Kits that enable teaching of a complete and modern IC design flow. The Kits contain technology-generic information, and therefore can be used in universities to create results very close to those produced by industry. The Kits are currently being used in more than 550 universities and company training centers in 65 countries. The developed design kits are used for educational and research purposes, they are free from intellectual property restrictions. They can be used in a wide range of design flows for digital, analog and mixed-signal designs using Synopsys’ EDA tools. The breadth of their deployment in universities and training centers around the world demonstrates that it serves its goals. SAED has already had a successful experience to create three SWGs. SWGs are successfully implementing both of their functions: providing experience to the students and implementation of projects. So far SAED SWGs have implemented the following projects: a) Educational Design Kits (EDKs) for different (90 nm, 32/28 nm and 14 nm) Technology nodes.

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Universities need corresponding EDKs in addition to EDA tools. But those EDKs should have certain challenges, the most important of which is not to contain confidential information of foundries. That is why SAED SWGs have developed EDKs which on the one hand do not contain confidential information, and on the other hand, have characteristics very close to the real design kits. The developed EDKs allow students to design different ICs using 90nm or lower technologies and Synopsys’ EDA tools. The Synopsys EDKs contain all the components necessary for designing any digital IC or IP in educational environment: Technology Kit, a Digital Standard Cell Library, I/O Standard Cell Library, a set of memories and a phase-locked loop. In addition, the EDKs also include various open, well-known and large circuit (OpenSPARC, etc.) designs that are implemented by the application of the developed libraries and serve as a base for the development of educational courses. The EDKs have been deployed at about 450 universities of 65 countries. b) Interoperable Process Design Kits (iPDKs) for different (90nm, 32/28nm and 14nm) Technology nodes. Universities need corresponding iPDKs in addition to EDA tools. SAED SWGs have developed interoperable iPDKs which are free from IP restrictions and are targeted for educational and research purposes. The developed iPDKs allow students to design different analog ICs and IPs using 90 nm or lower technologies and Synopsys’ EDA tools. The Synopsys iPDKs contain all the necessary components to realize Custom Design: technology files: physical verification files, parasitic extraction files, Spice models, schematic symbols, PCells and scripts. The iPDKs developed have the same large application in universities as EDKs. Thus, SWG is a new form of study in semiconductor design area, during which the involved interns acquire experience at most close to industrial projects. The experience of SAED SWGs witnesses about the extremely high efficiency of this form to organize education. To focus on the sustainability of the first considered university-industry cooperation model established between NPUA and Synopsys seems to be unnecessary. During the past 20 years of its existence, it has demonstrated obvious advantages and well-motivated continuity. It is worth mentioning that as a result of the Synopsys and NPUA cooperation many graduates have become employees of Synopsys by meeting the necessary requirements put forth during implementation of the model. They are filling the positions of leading specialists of the Company and even Technical Managers. The rest

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of the students continue their education in other programs—Master and PhD or are employed by other companies of the area. The NPUA–Synopsys cooperation model has been an effective and successful program in educating and graduating qualified microelectronic engineers for the past years. To show the transferability of NPUA–Synopsys educational cooperation model it will be enough to present just a few valuable examples. As has been mentioned above, the success of NPUA–Synopsys’ educational cooperation model has gained popularity in that the leading universities of Armenia such as Yerevan State University, Russian–Armenian Slavonic University and European University have expressed interest in implementing a similar educational model. In addition, universities of several countries ( Jordan, the United Arab Emirates, Saudi Arabia, etc.) have expressed a wish to adopt courses taught at NPUA “Microelectronic Circuits and Systems” Chair in their universities.

Chapter 16 CASE STUDY—NETWORK YOUNG ENTREPRENEURS NJO L.M. van der Mandele

Roots—The Frustrations of a Venturing Student The Network for Young Entrepreneurs (NJO) had its origins in the frustrations of a student at the Delft University of Technology, who in the early 1990s had invented a new design of a fuel cell. But he had no idea how to bring his invention to market. In the last year of his studies at the University, he had noted that there was very little, if any, education in how ventures were set up and run. For lack of a better option, he then joined a consulting firm, Arthur D. Little. Together with a few colleagues, he suggested that the consulting firm organize a course on how own businesses are started. The first step of the team was to visit the deans of the different faculties of Delft University to elicit their support. After all, a good connect to the University and its curriculums was an important factor in determining the success of the course. The first signs were not very encouraging. A few faculties and departments were considering their own academic courses in entrepreneurship. Others considered the proposed curriculum to have insufficient th eory to be pa rt of a un iversity pr ogram. An d fo r so me, th e proposal simply was not a topic of interest. Fortunately, there was one professor in the faculty of “Science, Technology and Society” (nowadays called Technology Policy and Management) who understood what NJO was about.

Driving Principles : The proposed venture had five basic principles

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1. Driving objectives were twofold: to help students who sooner or later wanted to be involved in venturing to understand what it takes to set up a successful business, and where possible to help them actually set up their new firms. In that sense, the course differed fundamentally from the average business course that teaches how to run a business, not how to start it. 2. For entrepreneurs by entrepreneurs: to the extent possible, the participants would work together to explore and develop the different skills needed to start their company. Furthermore, instruction would be given as much as possible by alumni and external experts in the field of areas like accounting, employment law, finance and so on. 3. Connection with the University: the target audience of this course was to be students from the different faculties of the Delft University of Technology. Good integration with the curriculum of the different faculties was deemed necessary, preferably with course credit to give recognition for the effort and time spent on the course. 4. The course also needed support, inspiration and limited funding from established professional organizations. After a careful selection process, the organizers opted for Ernst & Young (EY) to provide training in the basics of accounting, financial planning and finance, Holland van Gijzen for legal content, Arnold & Siedsma for intellectual property—an important topic for this population and Rabobank for financing issues. Consultants of Arthur D. Little provided planning skills in the first years; their role was taken over by Roland Berger later on. It is interesting to note that, after 25 years, all these firms are still active in NJO. 5. Rather than lecturing on entrepreneurship, the course would focus on the writing of a business plan to help the student-venturers to structure and focus their ideas. Learning as you go. And to expose them—through self-education—to the different necessary skills.

Design of the Course “Driving objectives” determined the curriculum had five basic parts: fi rst of all, participants had to be recruited from the different faculties. It turned out that a minimal course credit was enough to motivate a large percentage of potential students with venturing interest to apply; creating awareness for the new program across the Delft campus was more of an issue. A start was made by posting the new course in different faculty halls and auditoriums. Once the course was running, word-of-mouth assured an ample supply of applicants. Next step was to select the most promising students. Prevalence was given to students or teams that could move directly from the course into their actual venture. Motivation was an important criterion, as well as a

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commercial interest. The course proper started with the development of a vision and mission for the new ventures. This was accomplished in a number of afternoon group sessions with spin-out groups. In parallel, the participants and their teams were introduced to basic business skills like market analysis, marketing and sales as well as finance. In subsequent phases special venturing skills like taxes, intellectual property and patents were taught.

For Entrepreneurs, By Entrepreneurs The overriding motto of the course was: “for entrepreneurs, by entrepreneurs” which indicated that the participating student-entrepreneurs were expected to take an active role in courses by contributing their own experiences in setting up their ventures and by coaching their fellow students. Students were also expected to return to monthly alumni events to share their experiences with next classes. Most of the training days were closed off with a get-acquainted session—short introduction, lot of question and answer (Q&A)—with successful entrepreneurs, like Eckart Wintzen and Michiel Westerman who started their respective software companies BSO and Pink Elephant in an attic room and developed them into successful companies: BSO, for example, had 75 offices wh en Wi ntzen so ld it to At os Or igin. Th ese la te af ternoon sessions of course were not only quite instructive, but also a lot of fun for all.

Stakeholders A “close connection with participating universities” remained an important determinant of the program, necessary for its function to enable students to apply the good ideas that emerged in the course of their studies in new ventures. At Delft University of Technology, the NJO course was formally part of the Science, Technology and Society faculty (nowadays called Technology Policy and Management) and students from the other faculty could transfer their course credit. As a result the student population was a broad cross-section of the Delft University of Technology, with strong representation, in particular, from industrial design, computer science, mechanical and civil engineering. For many students this was a first and valuable experience in working across disciplines. The civil engineering faculty even provided, for free, a somewhat dilapidated workshop that was coined “the cubicle,” of which the main redeeming qualities were its location close to the university laboratories and its super-fast internet connections. “Support from established professional firms” was essential to provide specialized training, coaching and financial help. The general management introduction as well as the administration and coaching of the course was done

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on a free-lance basis by consultants from Arthur D. Little and later Roland Berger who by doing this gained valuable experience in what it takes in terms of know-how and character to become a successful entrepreneur. Expert courses were given by experienced professionals, for example from the tax law firm of Loyens and Loeff, accountants EY, patent advisors Arnold & Siedsma and venture capital bankers from Rabobank. Again, this was done without charge; the participating instructors gaining the fun of helping young people develop their future and, incidentally, clients with a chance of an interesting and profitable future. The curriculum was very inspiring, instructive and fun although the organizers did at times (but not that often) run into speakers who did not offer appropriate instruction. And, because of the way the course was set up, it was almost impossible to assure that there were no gaps or overlaps in the variety of courses and teaching methods. Which was one of the reasons why the business plan was the linchpin of the program. “Writing a business plan” was the crucial core of the course. It was the integrative element in a potentially fragmented set of offerings. In the instruction for the business plan, the topics to be addressed were provided. And the regular progress review of business plan writing enabled the course supervisors to actually gauge not only whether the participants were learning enough, but also what progress they were making toward developing their own ventures. The business plan forced the participants to develop the business model and logic for their company. And it also forced them to develop and verify the necessary intelligence about their venture, answering questions like: ●

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Who are my customers and why should they buy my product or service? Who are my competitors? Where do I find the materials and equipment and financing to start my venture? How do I develop or source my technologies at a decent price and timeframe? And, how do I protect the resulting intellectual property? How do I manage my people: find them, motivate them, pay them, evaluate them and, when needed, fire them? What should my ambitions be—should I be conservative and careful, or should I charge ahead and take the risks? Which actions should I take in the first weeks, months and years of my venture?

Success and Learning Points The entire course required 18-afternoon sessions and a few Saturdays. The course started in early 1996. From the first day onward they attracted

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a large audience which meant that quite a lot of selection had to be done. The main criteria were a serious interest in really starting a company while the enterprise should have a technical basis—no new taxi companies. Many students were interested because they wanted exposure to the business world without putting in any effort. Others started and fi nished th e co urse wi th enthusiasm but went on to either join an existing company and/or start their own venture at a later date. And, yes, there were a few who just wanted to sniff at entrepreneurship with the least effort possible. A few years later, NJO was a beehive of activity with several alumni who had started their company and who were supported by students and instructors. But NJO wanted to be accessible to a wide audience and lacked the scope and resources to become a full incubator. Alumni from the course agreed that it was—and is—a wonderful learning experience that succeeded very well in creating a real understanding of what entrepreneurship is about and a good basic insight into the essentials of running a business. It was particularly successful in stimulating awareness of the outside world: many technical venturers are entirely focused on their invention and business idea. They have not come to grips with the fact that their product or service needs a customer who has a need (preferably an urgent need) to buy and that it is important to understand what their customer population is and what their desires are. Starting venturers are not used to thinking beyond their comfort zone—which includes both tangibles like customers but also ambitions. For most of the starting participants, a target turnover amount of €50-–€100 thousand was considered quite enough. The second important learning point is to gain exposure to the professional skills what support business development and to lose the fear for the complicated sometimes confusing concepts and terminology. The course was good at demystifying the unknowns in finance, law, intellectual property and banking. The best answer to the most important questions is gained by talking to peers, which NJO did reasonably well. But since NJO itself was a start-up and it had a relatively high percentage of amateurs, students who did not really want to start a business, peer-coaching remained limited. That role was taken a few years later when Delft University realized that it had to build its science valorization capabilities and together with the municipality of Delft set up YES!Delft—with its own building, a faculty and strong connections to the funding community—YES stands for Young Entrepreneurs Society. Today, YES!Delft can claim it is the leading European technology incubator. It has two facilities, in Delft and in The Hague with more than 150 active start-ups, more than 200 (potential) investors and more than 50 employees. It specializes in projects that fit with its university

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linkages: complex with long lead times. For these kind of topics, the non-profit status of YES!Delft that does not demand equity positions is quite helpful. Yes!Delft has close connects with the Erasmus University in Rotterdam and the University of Copenhagen. The main program blocks of YES!Delft are dedicated to the validation of technology, acceleration, investor readiness and funding support. As an aside, YES!Delft provides project support for investors and innovation support for major companies. And the old NJO has morphed into an active part of YES!Delft by providing a course Ready for Startup which still is based around the writing of a business plan. NJO has come a long way in the last 25 years, but the essence—learning how ventures work by writing the business plan—has remained.

Part V CONCLUSIONS

Chapter 17 THE FUTURE OF LEARNING Johan G. Wissema

Having come to the end of this book, let us try to get the overall picture. What will learning be like in the not-too-distant future?

Brain Research If there is anything this book makes clear, it is the pervasive impact of the results of (recent) brain research; its influence turns up in most chapters. Teachers are brain changers—says David Sousa in Chapter 3. And he warns: “Because they (students) know where to find the information, they are not motivated to learn the information itself. Thus, their brains are not practicing the mechanisms of higher-order thinking, such as application, analysis, evaluation, creativity, and metacognition. We need to recognize that early and consistent reliance on the Internet may diminish the brain’s need to be creative, think critically, and retain information. Teachers at all levels need to plan their instruction to use the Internet to expand student creativity and problem-solving skills rather than replace them.” At the same time, Chapter 12 warns us there is much hot air in the use of educational neurology and we must be careful that it doesn’t become all hype. In the words of Bruno della Chiesa, “This chapter seeks to reflect on the role that educational neuroscience plays or does not play in public opinion-building and decision-making processes. The challenges met by this new discipline during the first two decades of its existence (2000–2020) range from skepticism and indifference to fashion phenomenon that saw the proliferation of neuromyths, and the mushrooming of neuro-traffickers and neuro-hijackers.” Nevertheless, today it is imperative that anyone engaged in, or associated with teaching and learning, understands how the brain works, keeping abreast of the new insights that are pouring in almost daily at a rapid pace.

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The Way We Learn and Teach From this book it emerges there are five dominant developments that will shape the future of learning and education—Third Generation Learning as we have called it in Chapter 1. These are, to be elaborated upon in the next paragraphs: 1. Students will take the driving seat in their education. Courses will increasingly become individualized while students get more influence in the governing of their educational institution (see “Students Take the Driving Seat”). 2. Learning soft skills (social skills, empathy) will become just as important as cognitive competencies. High tech—high touch 2.0 (see “ Social Skills”). 3. There is a host of new educational approaches that will enhance the palette of educators as well as learners (see “New Learning Technologies”). 4. Interaction between educators and employers will become standard, especially in higher education and in the fields of science and engineering (see “Interaction with Employers”). 5. As a result of the above, diplomas will no longer be the linking pin between education and employment. New assessment techniques will have to be developed (see “Interaction with Employers”).

Students Take the Driving Seat Third Generation Learning will take the student back in the driver’s seat—as it was in the 1GU of the Middle Ages. Many schools and universities offer students the option of designing their own courses; allowing students to take responsibility of their own education. For instance, in DE MET, a grammar school in Leuven, Belgium, students design their own learning program in an entrepreneurial atmosphere. There is intensive coaching and time for relaxing, sports and culture. This seems to be the future1. In higher education, courses will also be individualized, with exception of courses leading to professions such as medicine. The student is in charge as of the first day in kindergarten. Vervest (2021) has proposed giving educoins to students, that they can spend on the courses of their choice, perhaps even courses outside the university such as other universities and even Coursera and other online schools. If students take responsibility for their education, they also must learn how to prepare for future employment, balancing their interests with the requirements of the job they seek. The intensified interaction with employers during education will facilitate this—the fourth trend. Students might take quite

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unorthodox combinations, such as mixing Mandarin or Philosophy with a course in Physics. In South Africa they call it “blended learning,” with pupils spending time in conventional classes and some time in a computer room where they complete lessons on the screen2. The current system at schools is not student friendly. Why should students be working in year classes, rather than in multi-age groups? Why should a school student be forced to fully repeat a year—and waste time and motivation—if only some subjects are below standard? Some grammar school pupils do not finish school for the simple reason that they have no affinity to, say, mathematics, while they are brilliant in every other subject. And, why should pupils and students follow standard programs when neither they nor the jobs positions they are going to fill are standard? Industrialized learning is highly demotivating for students and teachers alike; people differ and don’t like to be cramped into a straitjacket while employers don’t want standard graduates anymore.

Social Skills “On the individual level social skills play a critical role in human Survival of the fittest (the Darwinian rule of evolution), and therefore the human brain is ‘programmed’ to connect and group with others” (Goldberg, Chapter 5). Darwin’s Evolution Theory was focused on the individual—not surprising in the very individualistic culture of the Victorian age. Goldberg and many others, like Harari (2015), argues it is the way early humans communicate with each other that brought them to the top of the food chain. Following this, one may state that evolution is not only a matter of the survival of the fittest but also of the survival of the fittest, that is most communicative, tribes. In our Western society, we seem to have lost the appetite for group work. Since their origins, schools and universities have concentrated on the individual and on cognitive thinking. Activities in students’ societies were considered enough to acquire social experience. In the industrial era, this was perhaps all that was needed for people to go ahead as they would communicate mostly with peers of the same trade or profession. Now that mono-disciplinary work has almost been eradicated and people work with others with completely different backgrounds, not to mention the many international teams we see in today’s companies, social skills are urgently required. Therefore, acquiring social skills should be as much a part of the education as cognitive sciences, says Goldberg. This requires special classes in communication and empathy and “social coaching” of teamwork. The attitude of employers is already changing. Ayden Fenerli reports in Chapter 4 a study into the characteristics employers seek in new employees.

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“The results are that employers were seeking non-technical skills and even personal traits and attitudes as much as technical skills; there were even cases where non-technical skills were considered more important than technical skills. Technical or job-related skills, such as technical literacy (e.g., the ability to read technical drawings and guidelines) and the ability to work with tools and equipment, were expected to be present in hiring decisions. Employers were eager to invest in candidates by providing on-the-job training for technical skills, however, only if the candidate had the desired non-technical skills and personal traits.”

New Learning Technologies Part III of this book explores several new learning techniques. We could have easily doubled the number of these seven chapters; distance learning for instance is not present, not because we consider it unimportant, but we are bound by the size of the book. Phenomenon-based learning is overtaking classroom-learning head over heel. We have dubbed it: Don’t teach them dancing, let them dance. It is much more motivating to solve a problem than to learn, say, Chapters 10–15 of a textbook and be examined about it. “Conceptualizing learning as an experience means that words used to describe that experience transform our perception of it,” writes Evi Agostini in Chapter 7. Solving problems can be done individualistic or in groups. The latter seems to be prevailing, hence the need for social comprehension, as discussed above. This raises the question of what the role of the teacher will be in problem-oriented learning. Listen to the voice of a student: “Teachers must go. Their critical role must be reimagined as ‘facilitators’. I propose there be no teachers after kindergarten and elementary school. Instead, middle- and high-schoolers are assigned ‘facilitators’. They would … guide students, meeting them twice a week online and focusing on forming meaningful connections” (Gause, 2021). Gaming and storytelling come in as new and powerful techniques; after all, did we not receive our earliest lessons by playing games and listening to stories? Evgeniya Kaz and Evgeniya Nekhoda posit in Chapter 10 that: “The main goal of entertaining games is to have fun. In contrast, the goal of gamification is to encourage people to carry out certain behaviors by applying game mechanics to non-game entities or bringing game elements into the current business process.” And Khaldoun Dia-Eddine writes in Chapter 9: “The experiences have demonstrated a strong interest of students in methods of Digital Story Telling as an alternative to traditional lecturing. The students were better motivated to question, comment, search and to interact.” Like phenomenon-based learning, gaming and storytelling are student-friendly

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techniques. They can’t be applied in every setting, but teachers should investigate in which cases they offer great potential. Likewise, the modern techniques of virtual and augmented reality are student-friendly and offer new opportunities. “The development of smart technologies has created a new paradigm for machine-human relationships in which the machine is not only becoming the repository, but also the curator and orchestrator of knowledge. Knowledge acquisition in itself no longer suffices to succeed and th rive in the world. Ac quiring and applying knowledge in and through experience is becoming ever more important to tackle the challenges and uncertainties of our world,” writes Carla Aerts in Chapter 8. Virtual reality can also be used to train teachers. “Researchers at the University of Virginia are training their education students using virtual reality simulators created by Mursion, a tech company. The teacher trainees experience several virtual practice scenarios, such as a parent-teacher conference, small group instruction and large group instruction … The technology is currently being used in over 50 American colleges” (The Economist, 2021). Learning is not only expanding one’s cognitive and social capabilities but also a matter of improving oneself. Here, the Habits of Mind approach offers g reat p otential a nd s hould b e p art o f a ny c urriculum. “ Numerous international futurists, neuroscientists, educators, and sociologists advocate for problem solving, creating, innovating, and communicating to sustain our global society. The needs they list are dispositions that are necessary to lend oneself to learning”, according to Arthur Costa and co-authors in Chapter 6. The application in learning of AI is a much-discussed subject and a muchdiscussed subject of speculation. Wayne Holmes and Iaroslava Kharkova are cautious in Chapter 11, where they write: “while promising to enhance education, the design and deployment of these tools again raise particular concerns and challenges.” They distinguish three analytical categories: learning with AI, learning about AI and preparing for AI. They conclude that: “Education as a site for critical study (i.e. both learning about AI and preparing for AI) can enable people to handle the challenges arising from AI from both a technical and humanistic perspective. It can provide the underpinning for new professions, but also raise awareness of the emerging impact of AI on humanity and inform philosophical debates on potential human – machine relationships. Informed citizens should be able to establish regulatory and monitoring mechanisms to ensure that AI technologies – especially those used in educational contexts – do not amplify existing inequalities, but instead benefit the most vulnerable or marginalized and contribute to the common good. Indeed, AI and its impact on education create challenges and opportunities, but ultimately it is social and political choices – not just the technologies or

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the technologists – that will determine how AI contributes to education and the outcomes for all.”

Interaction with Employers Right now, employers spend fortunes for acquiring the best talent; sometimes a candidate must pass more than half a dozen interviews or tests which puts them under a heavy strain. Clever employers create talent rather than select it. Instead of asking how to get the best employees, they should ask how they can help create them. Ostim University was established in an industrial complex near Ankara in Turkey. In this university, students are educated in close cooperation with industry. Murat Yülek, the rector, calls it an Industrial University (Chapter 14). “The Industrial University emerges as a type of Third Generation University (3GU) to address intensive formal learning and minimal informal learning through greater interaction with industry…. An Industrial University may offer a stronger lens to educate and train contemporary university students in real world settings as industrial manufacturing is getting even more important than before after Covid-19 strain on global logistics.” The development of Industrial Universities follows logically from the overview of public education in Chapter 13. A more dramatic situation occurred in Armenia where an IT company, Synopsys, was threatened by closure due to the lack of sufficient new specialists. In IT, the pace of change is very rapid indeed and universities don’t have the means to offer up-to-date equipment to students, nor can they retrain their staff in time. This led to an initiative to create a joint course in IT by the company and a university. The course followed a practical approach combined with teaching the theoretical backgrounds. The company goes a long way in providing facilities and teachers (from their staff ) but graduates are not obliged to join the company—although many do. Vazgen Melikyan, one of the founders of the course, concludes in Chapter 15: “During the past 20 years of its existence, it has demonstrated obvious advantages and well-motivated continuity. It is worth mentioning that as a result of the cooperation between Synopsys and the National Polytechnic University of Armenia many graduates have become employees of Synopsys by meeting the necessary requirements put forth during implementation of the model. They are filling the positions of leading specialists of the company and even technical managers. The rest of the students continue their education in other programs – Master and PhD or are employed by other companies of the area.” He goes on to show the transferability of the cooperation model with examples.

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A good example of phenomenon-based learning is given by a course in entrepreneurship, called Ready for Startup, at the Technical University of Delt in the Netherlands. Rather than teaching the ins and outs of entrepreneurship, students learn how to write the business plan of the company they want to start, helped by experts in disciplines like marketing, finance, law and others, from the industry. Martin van de Mandele, one of the founders of the course and subsequently of the university department that sponsors new entrepreneurs, writes in Chapter 16: “The course Ready for Startup still is based around the writing of a business plan. It has come a long way in the last 25 years, but the essence – learning how ventures work by writing the business plan – has remained.”

The Changing Role of Assessment “To solely use standardized achievement tests is like casting a net into the sea – a net that is intentionally designed to let the most interesting fish get away. Then, to describe the ones that are caught strictly in terms of their weight and length is to radically reduce what we know about them. To further conclude that all the contents of the sea consist of fish like those in the net compounds the error further. We need more kinds of fish. We need to know more about those we catch. We need new nets.” This description of assessment technology from William T. Randolph3, the Commissioner of Education in Colorado, seems the logical consequence of the individualization of courses and the varied demands to graduates by employers. It is put even stronger by Lucas (2021): “Across the world assessment is not working. We are not evidencing the kinds of dispositions and capabilities that society increasingly wants. Educational jurisdictions are placing too much reliance on highstakes, standardized testing. They are testing the wrong things in the wrong ways. High-stakes assessment is having a damaging impact on the health and well being of students, and it is not giving universities, colleges or employers the kind of information they want. Assessment is out of sync with curriculum and pedagogy…… Where we have become increasingly evidence-based in teaching and learning, we are failing to keep up with the science of assessment, preferring to rely on outdated, outmoded and unsubtle methods. Our young people require all of us working in education to establish greater clarity about the uses of assessment in education, linked to a greater understanding of the science of assessment. We need nothing less than a paradigm shift in our understanding about how best to create assessment systems that use more effective ways of evidencing the full range of student progress. In addition, we want to move rapidly from theoretical debate to practical prototyping and implementation.”

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Summing Up The landscape of learning and teaching is changing rapidly and profoundly. ●

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Learning and teaching become a succession of individual learning projects, team projects and classroom exchanges with increasing complexity (levels, as in games). It will also be a mix of high-tech modules (AI, VR and AR) and good oldfashioned handwriting in notebooks, with everything in between. The emphasis will go from learning standard texts to problem-solving (phenomenon-based learning). Teachers become coaches rather than orators4. Teaching becomes a high-standard profession with transdisciplinary Institutes of Advanced Learning at major universities. In universities, teaching has always been a suppositious child; if you want to make an academic career you must publish and your quality as a teacher hardly matters. It now seems that teaching is undergoing a re-evaluation and becoming a profession by itself. Learning soft skills (empathy, teamwork, presentation, etc.) will be part of just about every module. “No lectures, no classrooms, no majors, no departments”—Christine Ortiz at MIT5. Contacts with all kinds of employers start at day one. Students learn to pitch what they have learned and what they seek in employment. “Rise of the challenge-driven university” rather than coercion-driven education—Geoff Mulgan. End of overspecialization—knowing more and more about less and less—A.D. Lindsay of Oxford. Instead, return of the “Renaissance men (and women) in transdisciplinary research” (Wissema, 2009). Students are in charge of education, not the system. This means devolution of the National Education System; let students and teachers decide what and how to learn, top–down design of courses is contra-productive. Output exams and diplomas become just a festive celebration of the completion of a course. The power shifts to input exams. The world’s top universities already apply this, requiring not only a good school diploma but engaging in a series of interviews with potential students. Back to education as a transformational experience. Students will be encouraged to engage in all kinds of social activities, urged to do so by potential employers.

This “New Learning” does not come as a revolution. It is rather an evolution that fits in the development of learning over the ages, as described in Chapter 2.

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References Gause, N. (October 2021), Voices from the front line, investing in education, financial times special report. In this report students like Gause are asked to comment on their views on learning. Harari, Y. N. (2015), Sapiens – A brief history of humankind. London: Vintage. Lucas, B. (2021), Rethinking educational assessment: The case for change. Melbourne: Centre for Strategic Education. The Economist, Robot Masters, September 18th, 2021. Vervest, P. H. M. (2021), Fast forward digital – A new kind of university, Valedictory address. Rotterdam: Erasmus University. Wissema, J. G. (2009), Towards the third generation university – Managing the university in transition. Cheltenham: Edward Elgar Publishers.

NOTES ON CONTRIBUTORS Carla Aerts is an interdiscplinary thought leader and practitioner in learning with technologies, informed by Learning Sciences. She was Director of Futures at University College London—Institute of Education, Global Digital Director Education at Cambridge University Press and ran an EdTech Think Tank, the Tmrw Institute.

She sits on the board of R:Ed Africa, and advisory board of ErasmusX at Erasmus University Rotterdam and DEFi at Cambridge University and is a member of World Economic Forum’s (WEF’s) Expert Network for AI. Carla founded Refracted!, an online interdisciplinary community on learning and translational research and mentors EdTech start-ups. Evi Agostini is an associate professor at the Centre for Teacher Education and the Faculty of Philosophy and Education at the University of Vienna. She lectures on learning theory, research methods and anthropology at different universities in Austria and Italy and is concerned with teachers’ and school leaders’ professional learning in Austria, Germany and Switzerland. Evi is co-leader of the phenomenologically oriented International Vignette and Anecdote Research Network (VignA). Her main research interests are linked to (Phenomenological) Learning and Teaching Theories, Responsive Teacher Education and School Development, Aesthetic Education, Ethics and Epistemology. Arthur L Costa, Ed.D., is an internationally recognized author, consultant and co-founder of Institute for Habits of Mind. He has devoted his career to improving education through self-directed learning and thought-filled instruction through his works as a teacher, curriculum consultant, assistant superintendent for instruction and Director of Educational Programs for National Aeronautics and Space Administration (NASA), former president of Association for Supervision and Curriculum Development (ASCD) and an Emeritus Professor of Education, California State University Sacramento.

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Bruno della Chiesa is a European linguist trained at the Universities of Bonn and Paris Sorbonne. A former diplomat who describes himself as a “multilingual, multicultural engaged cosmopolitan,” he teaches at Harvard University and is considered one of the main founders of educational neuroscience. Khaldoun Dia-Eddine ([email protected]) is from Syrian–Swiss origins, an experienced engineer and an educated manager. Was active as a manager in different international industries before moving to the academic world mainly in Switzerland with Zürich University for Applied Sciences (ZHAW).

Awarded senior lecturer and consultant. Participated in many international conferences and published research papers and books in the Arabic, English, German and French languages. Aydın Fenerli is a consultant in analytics and artificial i ntelligence s olutions provider, SAS Institute. He is a program manager delivering analytics projects for Government and Public sector clients. Before this, he was working in business consulting in EY’s Ankara office in Advisory Practice, focusing primarily on Government and Public Sector. He led various projects on social and economic research, public policy-making, monitoring and evaluation, strategy development and capacity building and transformation. Aydın holds a BSc degree in International Relations and an MSc Degree in Social Policy, both from Middle East Technical University, Ankara.

Dr. Hagar Goldberg is an educational neuroscientist and a leadership consultant. She believes that cultivating social and emotional intelligence in our education systems is key to growth and thrive (as individuals and as communities). This belief drives both her academic studies and her work with educators, students and scientists. She established Social–Emotional Educational Neuroscience (SEEN) to support knowledge exchange between the science and the practice of teaching and learning. She facilitates leadership workshops around the world, emphasizing the power of social and emotional intelligence in transformative leadership. In her research, she has studied the behavioral and neuronal mechanisms of empathy, creativity, human emotional and social perceptions. She is teaching at the University of British Columbia and the University of Calgary. Prof. Dr. İsmail Guven had his BA from the Faculty of Educational Sciences, Department of Instruction and Curriculum at Ankara University, Turkey, in 1989. He earned his master’s degree in 1992 and doctoral degree (PhD) in 1998 from the Social Sciences Institute of Ankara University, Department of Instruction and Curriculum. He has a second doctoral degree (PhD) in history.

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Dr. Guven has been working in the Faculty of Educational Sciences in Ankara University since 1990. His main subject areas are Historical, Social and Philosophical Foundations of Education, Teacher Training as well as Social Studies and History Teaching, He has served as head of department of Social Studies and History Teaching and Director of Graduate School of Educational Sciences at Ankara University. He is working as full Professor at the Department of Social Studies Teaching in Faculty of Educational Sciences in Ankara University since 2011. Dr. Guven worked as visiting scholar at College of Education at Arkansas Tech University, in USA in 2018/2019. Dr. Guven lectured on many topics such as teaching methods, textbook analyzing, social studies and history teaching method courses, citizenship education courses at every level and in-service education. He published journal articles, books, book chapters and proceedings and organized teaching method workshops at national and international levels. Wayne Holmes is a learning sciences and innovation researcher who teaches at University College London, is a consultant researcher on AI and education for United Nations Educational, Scientific and Cultural Organization (UNESCO) and is a member of the Education Programme Committee for the International Research Centre on Artificial Intelligence. He holds a PhD in education and technology (University of Oxford), and master’s degrees in philosophy (University of Greenwich) and education (University of Oxford). Having been involved in education throughout his life, over the last decade Wayne has focused on AI and education, researching the use of AI to enhance and further understand learning, and its ethical and social implications. His recent publications include Artificial Intelligence in Education. Promise and Implications for Teaching and Learning (2019), Ethics of AI in Education: Towards a Community-Wide Framework (2021) and, for UNESCO, AI and Education: Guidance for Policy-makers (2021). Wayne has given invited talks about AI and education to audiences in countries around the world. Bena Kallick, PhD, is an internationally recognized author and consultant known best for bringing personalized learning with Habits of Mind dynamically into curriculum, instruction and assessment. She, with Dr. Art Costa, have coauthored Habits of Mind books over the past three decades and co-founded the Institute for Habits of Mind. Evgeniya Kaz She is a “candidate” of economic sciences and associate professor at the Department of Strategic Management and Marketing of the Institute of Economics and Management (EIM), Tomsk State University (TSU). She is Director of the International Exchange Centre of IEM, TSU. She is certified by the University of Maastricht as a trainer of university teachers focused on the use of problem-based learning methods in universities.

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Iaroslava Kharkova has five years’ experience of managing projects at the international level, for two United Nations (UN) agencies, in education and development. In particular, she has contributed to numerous education programs, including the use of technologies and AI in education, skills development, global citizenship education and education policies. Her current research interests are in the area of educational innovations, including emerging technologies and AI, and how they affect learners, educators and education systems. She holds master’s degrees in philosophy from Paris-Sorbonne University and the Taras Shevchenko National University of Kyiv, Ukraine. L. Martijn van der Mandele LLM, MBA, is a former managing director of Arthur D. Little Netherlands and co-founder of the Society for Young Entrepreneurs at Technical University Delft, the Netherlands. Vazgen Melikyan is the Director of Synopsys Armenia Educational Department. He is a Corresponding Member of National Academy of Science of Armenia, Doctor of Technical Sciences, Honorable Scientist of Armenia. His latest book, Simulation and Optimization of Digital Circuits: Considering and Mitigating Destabilizing Factors has been published by Springer. He is an author of 12 monographs, more than 300 scientific and 135 methodical publications, had more than 150 reports in international conferences. A total of 70 PhD dissertations have been successfully defended under his supervision. He has been awarded with the Gold medal of the Ministry of Education and Science of Armenia for “the greatest contribution to education and science.” Evgeniya Nekhoda is a Doctor of Economic Sciences, Professor, Head of the Department of Strategic Management and Marketing of the Institute of Economics and Management, Tomsk State University. She has also taught at Tomsk Polytechnic University and Siberian State Medical University and has 15 years of work experience in various Tomsk companies. David A. Sousa, EdD, is an author and international consultant in educational neuroscience. He has written more than 20 books for educators and parents on ways to use the findings from brain research to improve teaching and learning. His first book, How the Brain Learns, appeared in1995, and is now available in its sixth edition, published by Corwin Press. This book is used in pre-service teacher preparation courses at colleges and universities, and as a resource in school district in-service professional development programs.

Some of his other popular books include, How the Brain Learns Mathematics, How the Brain Learns to Read, How the Gifted Brain Learns, How the Special Needs Brain Learns, and Mind, Brain, and Education,which is a collection of articles

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from international experts on educational neuroscience. One of his recent books, Engaging the Rewired Brain, discusses the impact that technology is having on the young brain. He alerts educators and parents to research evidence suggesting that a large amount of time young people spend with technology is affecting the development of their attention, memory, thinking skills and social skills. His books have been translated into French, Spanish, Chinese, Russian, Arabic, Korean and several other languages. In the past 25 years, he has conducted workshops for more than 200,000 educators in hundreds of school districts on brain research and science education at the Pre-K to Grade 12 and university levels. He has presented at numerous national conventions of educational organizations and to regional and local school districts across the United States, Canada, Europe, Australia, New Zealand and Asia. Dr. Sousa has a Bachelor of Science degree in chemistry from Massachusetts State University at Bridgewater, a Master of Arts degree in teaching science from Harvard University and a doctorate from Rutgers University. His teaching experience covers all levels. He has taught high school science and has served as a K–12 director of science, a supervisor of instruction and a district superintendent in New Jersey schools. He was an adjunct professor of education at Seton Hall University and at Rutgers University for more than 10 years. A past president of the National Staff Development Council (now called Learning Forward), Dr. Sousa has edited science books and published numerous articles in leading educational and research journals on professional development, science education and brain research. He has received awards from professional associations, school districts and Bridgewater State University (Distinguished Alumni Award), as well as several honorary doctorates for his commitment and contributions to research, professional development and science education. He has been interviewed on the NBC Today show, on National Public Radio, and other international programs about his work with schools using brain research. He makes his home in South Florida. Vasileios Symeonidis is a Postdoctoral Researcher at the Institute of Education Research and Teacher Education at the University of Graz. He holds a PhD from both the University of Innsbruck and the Eötvös Loránd University awarded in the framework of the European Doctorate in Teacher Education. His research interests include teacher education policy and practice, international and comparative education, and experiential dimensions of learning. Ahmet Uludag holds a PhD in Educational Leadership and Policy Studies from Arizona State University, an MA in Curriculum and Instruction with a concentration in English as a Second Language and Bilingual Education and a BA in

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Foreign Language Education from Middle East Technical University. He has been managing and directing projects and programs for over 20 years at national and multinational organizations.

He is the Founder and Director of the Department of Foreign Languages at Ostim Technical University in Ankara. He also founded the Turkish Teaching Center to facilitate multilingual education at Ostim Technical University. He led the strategy development group to design and implement the first Strategic Plan of Ostim Technical University. Dr. Uludag worked with the Cisco Networking Academy Program to support Cisco Networking Academies around the world for 10 years. He managed the translation of the Cisco Networking Academy Curricula into the UN languages. He coordinated the design of the instructor resources for Cisco Learning Institute. He implemented the development of the English Interactive Online program for K-12 schools in the country of Jordan. His work focuses on educational leadership and language policy and online learning and assessment in English as a Foreign Language. He designs and develops educational materials for the use of educators and learners. He was Lead Designer in developing educational resources, games and tools for special education students for the Ministry of Education in Turkey. His educational tools vary from special education tools to online language learning materials. He also directed a division for the Science and Society Division of the National Council of Science and Technology of Turkey, known as TUBİTAK, overseeing the development of National Science Centers and science and society publications. He oversaw and managed the publication of a monthly science magazine and arranged the translated popular science books while delegating a budget of over 30 million dollars over 3 years. Johan G. Wissema (www.wissema.com) is a professor emeritus in innovation and entrepreneurship at Delft Technical University in the Netherlands, currently working part-time at the faculty of Ostim Technical University in Ankara. His most recent academic book Towards the Third Generation University had numerous translations and the concept of the current book builds on that. Murat A. Yülek is the Rector of Ostim Technical University in Ankara and has taught at Georgetown and Columbia Universities in the US. A former International Monetary Fund (IMF) economist and a corporate executive, he had finance, development and macroeconomic policy responsibilites in various countries. His latest book, How Nations Succeed has been published by Palgrave MacMillan in the UK and Tsinghua University Press in China.

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Allison Zmuda is an education consultant, author and newly appointed codirector of the Institute for Habits of Mind. She specializes in developing personalizing curriculum, assessment and instructional practice in schools around the world. Her book with Bena Kallick, Students at the Center: Personalizing Learning with Habits of Mind (ASCD, 2017) shows the synergy of the dispositions and personalized learning.

NOTES Chapter 4 1. Spence conceptualized signaling in his famous paper in 1973 “Job market signaling”. 2. The terms, General and Specific Training are proposed by Becker in his book “Human Capital: A Theoretical and Empirical Analysis With Special Reference to Education”. According to Becker, General Training is applicable to all jobs so that worker could utilize it in other companies as well, however, Specific Training is for a specific job in a company and this training might not be utilized in other firms (Becker, 1964). 3. Full-time job contracts were mainstream throughout the twentieth century. Temporary contracts, part-time contracts and freelancing are on the rise.

Chapter 7 1. For this shift from teaching to learning, see for example the “supply-usage model”— in German: Angebots-Nutzungs-Modell—by Helmke, 2012, a model that has become highly influential in educational contexts, especially in the German-speaking world. See also the critique of this model by Meyer-Drawe, 2013. 2. All German quotations and the vignette have been translated into English by the authors of this chapter. 3. A concept that is also expressed by the ancient Greek term Καιρός or Kairos. 4. The “ProLernen – Professionalization of educators and educational leaders through learning research with vignettes” project—is funded by the Erasmus+ program/20201-AT01-KA203-077981 (Duration: 11/2020-11/2022).

Chapter 8 1. Generation Z, Wikipedia, https://en​.wikipedia​.org​/wiki​/Generation_Z & Generation Alpha, Wikipedia, https://en​.wikipedia​.org​/wiki​/Generation​_ Alpha 2. EdTech, edTech or Edtech, refers to digital technologies for education and learning. These technologies are often developed by start-up companies, wishing to improve learning and education. They can span learning from early years to continued or lifelong and professional education or learning, in informal or institutional contexts. 3. Oculus brand, https://en​.m​.wikipedia​.org​/wiki​/Oculus_(brand)

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4. Statt, N., 2015, Microsoft’s HoloLens explained: How it works and why it’s different: Has Microsoft suddenly pushed us into the age of “Star Trek” and “Minority Report”? For those confused about what’s actually going on with the company’s new head-mounted gadget, here’s the rundown. January 24, 2015, cNet, https://www​.cnet​ .com​/news​/microsoft​-hololens​-explained​-how​-it​-works​-and​-why​-its​-different/ 5. 5G, the successor of 4G, refers to the fifth generation technology standards for broadband mobile networks. 5G supports greater bandwidth, allowing for faster download speeds, allowing for more sophisticated virtual environment and immersive experiences, supported by higher-end graphics and faster navigation and interaction speeds, enhancing the virtual environment and immersive gaming experiences. 6. Mordor Intelligence, Virtual Reality(VR) Market in Education - Growth, Trends, COVID-19 Impact, and Forecasts (2021 - 2026), https://www​.mordorintelligence​ .com​/industry​-reports​/virtual​-reality​-vr​-market​-in​-education 7. Gaming Market – Growth, Trends, Forecasts (2020 – 2025), October 2020, https:// www​.reportlinker​.com​/p05903704​/Gaming​-Industry​- Growth​-Trends​-and​-Forecast​ .html​?utm ​_ source​=GNW 8. Sandbox games, refer to video games that promotes creativity and experimentation in its gameplay, allowing users significant amounts of freedom to complete tasks or reach a goal (provided a goal exists, as many sandbox games are openended without having set goals or objectives). Minecraft is a sandbox game in which players are able to enjoy more creative or more goal-oriented open-ended mode of gameplay. 9. The author uses the term iVR to highlight the interactive nature of the tools and technologies introduced. 10. Creative and Performing Arts. 11. Polymerase Chain Reaction machine or thermocycler, also referred to as a “molecular photocopier,” used to make millions of copies of a small DNA segment, allowing scientists to amplify DNA. https://www​.excedr​.com​/blog​/what​-is​-a​-pcr​ -machine/ 12. SDG—Sustainable Development Goals refers to 17 interlinked development goals, designed as a blueprint to “achieve a better and more sustainable future for all.” Invoked by the United Nations in 2015, aimed to be achieved by 2030. 13. Hackathon: an event in which participants engage in an intense collaboration on a design, software challenge or problem-solving project, resulting in a concept or prototype. These events can be galvanized to become deep learning experiences, supporting knowledge transference, cognitive and metacognitive competencies as well as collaborative and inter-personal skills.

Chapter 11 1. For example, “Hundreds of AI tools have been built to catch covid. None of them helped.” (Douglas Heaven, 2021) (https://www​.technologyreview​.com​/2021​/07​/30​ /1030329​/machine​-learning​-ai​-failed​-covid​-hospital​-diagnosis​-pandemic). 2. For example, “ The race to understand the exhilarating, dangerous world of language AI” (Hao, 2021) (https://www​.technologyreview​.com​/2021​/05​/20​/1025135​/ai​-large​ -language​-models​-bigscience​-project).

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3. For example, “Deepfakes Are Going To Wreak Havoc On Society. We Are Not Prepared.” (Toews, 2020) (https://www​.forbes​.com​/sites​/robtoews​/2020​/05​ /25​ /deepfakes ​ -are ​ - going​ - to ​ -wreak​ -havoc​ - on​ - society​ -we ​ -are ​ -not​ - prepared/​ ?sh​ =7dfbdd9e7494). 4. For a helpful explanation, see “About Face ID advanced technology” (https://support .apple​.com​/en​-gb​/HT208108) 5. For a helpful explanation, see “The Impact of Artificial Intelligence On Social Media” (https://medium​.com​/humansforai​/the​-impact​-of​-artificial​-intelligence​-on​ -social​-media​-579345b6f751). 6. For example, “Colleges are turning students’ phones into surveillance machines, tracking the locations of hundreds of thousands.” (Harwell, 2019) (https://www .washingtonpost​.com​/technology​/2019​/12​/24​/colleges​-are​-turning​-students​-phones -into​-surveillance​-machines​-tracking​-locations​-hundreds​-thousands). 7. For example, Unitime (https://www​.unitime​.org). 8. https://www.digitaltrends.com/home/what-is-amazons-alexa-and-what-can-it-do. 9. For example, see “Using AI to measure student engagement” (https://www​.educationmattersmag​.com​.au​/using​-ai​-to​-measure​-student​-engagement). 10. For a helpful explanation of deep learning, see “Top 10 Deep Learning Algorithms You Should Know in 2021” (https://www​.simplilearn​.com​/tutorials​/deep​-learning​ -tutorial​/deep​-learning​-algorithm). 11. https://www​.apple​.com​/uk​/siri 12. See Endnote 8. 13. At the beginning of 2021, Google announced Google Brain, an artificial intelligence language model that uses 1.6 trillion parameters (units of data). Nonetheless, although in many ways impressive, Google Brain still does not understand language as a human does. 14. For one perspective on the funding of student-facing AI, see: “The Next Wave of Edtech Will Be Very, Very Big — and Global” (Corcoran, 2021) (https://www .edsurge​.com​/news​/2021​- 07​-30​-the​-next​-wave​- of​-edtech​-will​-be​-very​-very​-big​-and​ -global). 15. Commercial examples of ITS include Mathia (https://www​.carnegielearning​.com​/ solutions​/math​/mathia), alta (https://www​.knewton​.com​/what​-is​-alta), Alef (https:// alefeducation​.com), ALEKS (https://www​.aleks​.com), Byjus (https://byjus​.com), Eshuri (https://eshuri​.rw), M-Shule (https://m​-shule​.com), and Squirrel AI (http:// squirrelai​.com). Non-commercial examples include ViLLE (https://www​.learninganalytics​.fi​/en) and Assistments (https://new​.assistments​.org). 16. Examples of DBTS include AutoTutor (http://ace​.autotutor​.org​/IISAutotutor​/index​ .html) and Watson Tutor (https://www​.ibm​.com​/blogs​/watson​/2018​/06​/using​-ai​-to​ -close​-learning​-gap). 17. Examples include ECHOES (https://sites​.google​.com​/site​/echoesproject), Fractions Lab (https://www​.italk2learn​.com), and Betty’s Brain (https://wp0​.vanderbilt​.edu​/ oele​/bettys​-brain). 18. Examples include Ada (https://www​.jisc​.ac​.uk​/news​/chatbot​-talks​-up​-a​-storm​-for​ -bolton​-college​-26​-mar​-2019), Eneza (https://enezaeducation​.com), and Deakin Genie (http://genie​.deakin​.edu​.au). 19. The two true teacher-facing AI examples, both of which curate learning resources, are X5GON (https://www​.x5gon​.org) and IBM’s Teacher Advisor (https://teacheradvisor​.org).

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20. One example is OUAnalyse (https://analyse​.kmi​.open​.ac​.uk). 21. https://ai4k12​.org 22. For example, see “Black in AI” (https://blackinai​.github​.io/#) and “Women in AI” (https://www​.womeninai​.co).

Chapter 12 1. Production of ignorance, mostly on purpose; the word is based on Greek  ἄγνωσις (agnoˉsis, “not knowing”) and γένεσις (génesis, “creation, beginning, origin”). Coined in 1995 by linguist Iain Boal—in Proctor, R.N. & Schiebinger, L. (2008): Agnotolog y. The Making and Unmaking of Ignorance, Stanford: Stanford University Press). 2. From Greek κάκιστος (kakistos, worst) and  κράτος (kratos, rule): government of the worst (by the worst and for the worst). 3. In the first parts of this chapter, I take another, expanded look at some historical considerations I already had the opportunity to discuss in various talks and courses over the last decade, and to also briefly reflect on in a paper published 2016 in French (“Neuroéducation: attention danger!” in Cahier Pédagogiques, vol. 527, Feb. 2016, pp. 26–28, Paris). 4. See B. della Chiesa et al. (2007): Understanding the Brain: the birth of a learning science, Paris: OECD. 5. As other professionals, established researchers often tend to perceive new hypotheses and/or paradigms as not only challenges to their own work, but moreover as threats to acquired positions. See B. della Chiesa et al. (2009): “How Many Brains Does It Take to Build a New Light: Knowledge Management Challenges of a Transdisciplinary Project”, Mind, Brain and Education 3 (1), March 2009., pp. 17-26. 6. As all reasonable people (should) know, at the origin of the ever-growing malaise in the teaching profession lays the discrepancy between the overwhelming social responsibility that behooves teachers and the lack of social recognition granted to them. 7. This trend had been spectacularly anticipated 70 years ago by William Tenn in his story “Null-P” (Worlds Beyond #2, Jan. 1951). 8. “A world without facts means a world without truth and trust. And if you don’t have any of those things, you certainly can’t conquer coronavirus, you can’t conquer climate change.” (Nobel Peace Prize laureate Maria Ressa, in an interview posted on the Rappler site and quoted in the Harvard Gazette on October 8, 2021.) 9. Debunking a rumor is a very difficult task; it has even been suggested (rumored?) that the fast spread of false news can be explained by the fact that, on average, debunking a fake rumor requires seven times more time than confirming a justified rumor. 10. Taber, S. (2019). “The Problem with Sugar-Daddy Science”, The Atlantic, September 18, 2019. 11. Produced by the “brain team” within OECD’s CERI (Center for Educational Research and Innovation) and its networks of experts around the world. I had the honor of leading this effort throughout (1999-2007). 12. “An infodemic is too much information including false or misleading information in digital and physical environments during a disease outbreak. It causes confusion and risk-taking behaviours that can harm health. It also leads to mistrust in health authorities and undermines the public health response. (…) With growing digitization – an

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expansion of social media and internet use  –  information can spread more rapidly. This can help to more quickly fill information voids but can also amplify harmful messages.” (WHO, https://www​.who​.int​/health​-topics​/infodemic​#tab​=tab_1). This infodemic is to be observed first and foremost through the proliferation of conspiracy theories, each of them crazier than the previous ones. 13. “Alternative facts” has been elected the “un-word of the year” in 2017 by a panel of German linguists. This Unwort des Jahres is a “recently popularized term that violates human rights or infringes upon democratic principles” (Wikipedia). The context in which the phrase “alternative facts” materialized in January 2017 was an indication that disinformation/misinformation attained a very worrying level, unknown until then; arguably, this astonishing oxymoron was predicting the years to follow. 14. A development on critical thinking would blow the limits of this chapter, sorry. Suffice to say that the obsession with figures in education (it starts at policy level with the overreliance on statistics: see Sorokin’s quantophrenia) and the excessive emphasis on math and hard sciences at high school level is necessarily done, consciously or not, voluntarily or not, at the expense of the acquisition of critical thinking skills (this may, in some places at least, match with voters’ choices, as a survey conducted end of May 2022 in seven battleground US states has shown: https://s3.documentcloud.org/documents/22086577/education-poll.pdf; #5, p. 4). Not that hard sciences are alien to critical thinking, of course, but the level of expertise required to reach such skills via math or physics (epistemological issues, ideological questioning, etc.) is so advanced that it is light-years away from teenagers. STEM-centered schooling is doomed to give up on training for critical thinking and to promote acatalepsy in young people. Who benefits from this? Who are the victims? 15. Deconstructing dichotomies such as true/false or right/wrong is actually necessary as well, and not only because they can only fuel extreme polarizations. Even conspiracies are to be ranked, in terms of falsehood, on a continuum. If only students could learn more about the infinite variations of degrees and colors on the human spectrum! 16. Often mistakenly attributed to Joseph Goebbels, it is in fact to be found in the OSS psychological profile of Hitler. 17. Cf. Todorov, T. (1988/1993): On human diversity. Harvard University Press; Maalouf, A. (1998/2012): In the Name of Identity. Arcade; della Chiesa, B. (2012): “’Expansion of our own being’: Language learning, cultural belonging and global awareness” in Languages in a Global World – Learning for Better Cultural Understanding; OECD. 18. On the other hand, accusing someone of indulging in conspiracy theories can also be, and in fact quite often is, too easy a way of dismissing upfront any attempt at criticism. Nothing is easy … 19. Arendt, H. (1963) Eichmann in Jerusalem: A Report on the Banality of Evil. NYC: Viking Press. 20. Thus echoing the African proverb: “Not to know is bad; not to want to know is worse.”. Or, in other words, “Don’t Look Up”! 21. “Is there empathy and social intelligence left somewhere?,” a voice asked after having heard, in the summer of 2021, antivaxxers still promoting hydroxychloroquine and ivermectin against COVID-19. 22. My most sincere thanks to the young and talented humanists Hélène Hilton and Pierre Duval, for their invaluable help. 23. Orwell, G. (1949): Nineteen Eighty-Four, chapter 7.

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Chapter 17 1. 2. 3. 4. 5.

https://www​.demetleuven​.be​/hoe Schumpeter column, School for frugal innovation, The Economist, December 12th, 2015. pz​.ha​​r vard​​.edu/​​sites​​/defa​​ult​/f​​i les/​​A sses​​sment​​Reima​​g ined ​​_ B​ook ​​let ​_ 0​​.pdf E-ducation, The Economist, June 19th, 2013. E-ducation, The Economist, June 19th, 2013.

INDEX administration 15, 198, 220, 237 Al-Azhar University 199 Al Qarawiyyin University 199 alumnus/alumni 213, 236, 237, 239 artificial intelligence 3, 12, 16, 125, 165–77 assessment 8, 143, 158, 160, 162, 169, 175, 244, 249 attitudes 10, 20, 42, 44, 55–57, 59, 81, 106, 154, 218, 219, 246 Baghdad 198 Bait ul Hikmah 198 behavior 19, 20, 43, 44, 46, 59, 74, 76, 81, 93, 97, 104, 129, 130, 143, 167, 169 Bill Hewlett 213 biotechnology 3, 221 brain 8, 12, 13, 15–21, 23–29, 33–43, 45–48, 67–78, 80, 100, 101, 103, 104, 131, 183, 185–88, 190, 243, 245 research 8, 12–21, 33–48, 67–80, 100, 101, 104, 131, 167, 182–91, 243–50 Brain Learning Sciences (BLS) 15–29 Caliph Al Ma’mun 198 Cambridge University 26, 199 case study 11, 56, 58, 235–40 collaboration 13, 22, 26, 45, 68, 103, 114, 127, 130, 168, 172, 193, 204–7, 210, 211, 215–22, 226 commercialization 6, 199, 200, 204, 215, 219, 220 competence 19, 49, 67, 75, 85, 88, 104, 115, 121, 203, 212, 218–20 computer 4, 7, 12, 13, 16, 21, 23, 28, 31, 125, 131, 142, 153, 154, 161, 166, 168, 169, 172, 226–28, 230, 237, 245 David Packard 213 digital story telling 139, 246

diploma 7, 10, 58, 199, 209–11, 226, 227, 244, 250 dyslexia 38, 39 educational institutions 9, 119, 171, 201, 203, 209, 214 science 16–20, 27, 28, 40, 74, 115, 225, 231 system 8, 79, 140, 182, 195–97, 223, 225 emotional learning 43, 44, 67–82 employers 7–11, 55–64, 163, 203, 214, 244–46, 248–50 endogenous growth theory 202 Entrepreneurial University (EntU) 202, 216 entrepreneurs 142, 202, 235–37, 239, 249 entrepreneurship 13, 129, 140, 142, 213, 235, 236, 239, 249 experience 10, 18, 23, 34, 38, 47, 56, 57, 59–62, 67, 69–71, 75, 78, 79, 84, 94, 95, 99, 101, 103, 111–19, 121, 125, 126, 129, 131, 132, 140, 147, 149, 156, 162, 175, 211, 213, 215, 218, 219, 228, 231, 232, 237–39, 245–47, 250 experimentation 12, 18, 125–35 factory-based education model 196 Finnish education system 113, 121 Frederick II 196 Frederick Terman 211–13 Frederick William I 196 free compulsory public education 196 gaming 12, 125–35, 143, 153–63, 183, 246 Grandes Ecoles 200 Habits of Mind 93–109, 247 Hewlett-Packard 213

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higher education 4, 7, 10, 14, 115, 129, 134, 135, 171, 195, 199, 223, 244 humanities 18, 27, 28, 169 Humboldt University 5, 199 Ijazat 199 immersion 125–35 industrialization 19, 201, 202, 204, 216, 220 industrial layer 202, 205, 212, 220 park 212 Revolution 7, 9, 28, 126, 195, 196, 199, 201 University (IndU) 13, 202, 209–20 industry 13, 55, 56, 59, 61, 200, 202, 204, 211, 213, 215–20, 223–29, 231, 232, 248, 249 Industry/University Cooperation 223–33 innovation 15, 131, 134, 135, 139, 140, 142, 176, 181, 182, 195, 200, 203, 205, 210, 211, 213–15, 217, 220, 240 Johan Julius Hecker 196 knowledge 80, 99, 125, 215, 216, 247 learning classical 6 industrial 7–10 phenomenon-based 11, 13, 111, 120, 246, 249, 250 management 9, 12, 13, 43–45, 60, 77, 80, 81, 119, 131, 140, 154, 158–62, 169, 235, 237 manufacturing 55–57, 59, 197, 209, 210, 213, 216, 220, 224, 230, 248 master–apprentice model 199 mathematics 40–51, 83–89, 169, 227, 245 Napoleonic academic system 199 neurogenesis 34, 49, 51 neuroplasticity 34, 69, 70, 77, 78, 86, 87 neuroscience 3, 13, 20, 21, 25, 28, 33, 34, 36, 43–48, 67–82, 112, 170, 181–89, 243 Nizamiyah University 199 Oxford University 199 Palo Alto 213

Paris 199 pedagogy 17, 68, 74, 112, 113, 119, 130, 137, 150, 169, 173, 175, 249 phenomenology 112, 116, 121 Pope Silvester II 199 Prussian education system 196 psychology 11, 15–29, 80, 112, 120, 170, 190 public education 73, 195–205, 248 reading 37–51, 93, 119, 145, 147, 157, 159, 196 reality augmented 12, 126, 127, 132, 247 virtual 12, 73, 125–35, 144 Realschule 196 regional development 216 The Rensselaer School 197 Russ Varian 213 Scientific Revolution 73, 182, 201 screen time 46, 47, 128, 245 Sigurd Varian 213 Silicon Valley 176, 213 skilled workers 3, 4, 61, 63 skills 3, 4, 7, 8, 10, 15, 18, 20, 25, 34, 36–41, 43–47, 55–64, 68, 73, 80, 81, 100, 102, 104, 106, 109, 120, 126, 130, 131, 135, 139–41, 143, 158, 160–62, 170–72, 175, 184, 187, 190, 195, 196, 198, 203, 210, 211, 214, 218, 219, 225, 236, 237, 239, 243–46, 250 communication 56, 73, 81, 100, 130, 172, 175, 184, 210, 245 emotional 36, 43–47, 73, 80, 81, 100, 130, 172, 175 social 7, 10, 43, 45–47, 68, 80, 81, 120, 244–50 soft 10, 57, 80, 244, 250 social learning 18, 20, 68, 75, 117 media 23, 28, 47, 73, 120, 142, 165, 184 networks 61–63, 71, 184 Stanford Research Park (SRP) 212, 213 Stanford University 211 start-up 133, 200, 211, 212, 219, 239 STEM 75, 131, 211 talent 6, 58, 78, 211–13, 215, 248 teamwork 10, 45, 55, 64, 160, 161, 231, 245, 250 technical Education 142, 196, 227

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269

technology 19, 21, 23, 46, 73, 120, 127, 142, 143, 231, 249 Triple Helix 195, 200

University of Kairouan 199 University of Paris 199 University of Zaytuna 199

University First Generation (1GU) 4 Industrial 13, 202, 209, 210 Second Generation (2GU) 5 Third Generation (3GU) 5 University of Bologna 4, 199 University of Cordoba 199

values 76, 78, 80, 95, 149, 155, 168, 171, 173, 174, 176, 184, 216, 218, 219 Varian Associates 213 Vocational Education 7, 56, 60, 127, 197, 201 Yale University 199