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 theory to be part of a university program. And for some, the 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:

• 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.

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 research, potentially spurring technological advancement 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 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).

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 organization in converting public and private 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 to how it can serve society better in education and development. The university ecosystem has been 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.

Introduction

Employers seek certain characteristics when hiring new employees. These characteristics are “signaled” 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 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. 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.

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.

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. 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 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.

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).

• 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 USA.

• 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 reasons.

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.

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. 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.

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 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.

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.

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”).

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, software agents that appear capable of fluid conversations, or the creation of deep fake videos. However, we know less about how AI has infiltrated our daily lives. AI helps unlock your smartphone with face ID, provides personalized feeds in your social media, and monitors your whereabouts as you walk about town. Increasingly, while it rarely makes the headlines, AI is also being used in educational contexts, for example to automatically generate timetables, to adapt tutoring technologies to individual competencies, and to monitor whether students are concentrating in class. 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.