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Higher Education Landscape 2030: A Trend Analysis Based on the AHEAD International Horizon Scanning

SPRINGERBRIEFS IN EDUCATION
Dominic Orr · Maren Luebcke ·
J. Philipp Schmidt · Markus Ebner ·
Klaus Wannemacher · Martin Ebner ·
Dieter Dohmen
Higher Education
Landscape 2030
A Trend Analysis
Based on the
AHEAD International
Horizon Scanning
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Dominic Orr Maren Luebcke
J. Philipp Schmidt Markus Ebner
Klaus Wannemacher Martin Ebner
Dieter Dohmen
Higher Education Landscape
2030
A Trend Analysis Based on the AHEAD
International Horizon Scanning
Dominic Orr
FiBS Research Institute for the Economics
of Education and Social Affairs
Berlin, Germany
Maren Luebcke
HIS Institute for Higher Education
Development (HIS-HE)
Hannover, Germany
J. Philipp Schmidt
Massachusetts Institute of Technology
Cambridge, MA, USA
Markus Ebner
Graz University of Technology
Graz, Austria
Klaus Wannemacher
HIS Institute for Higher Education
Development (HIS-HE)
Hannover, Germany
Martin Ebner
Graz University of Technology
Graz, Austria
Dieter Dohmen
FiBS Research Institute for the Economics
of Education and Social Affairs
Berlin, Germany
ISSN 2211-1921 ISSN 2211-193X (electronic)
SpringerBriefs in Education
ISBN 978-3-030-44896-7 ISBN 978-3-030-44897-4 (eBook)
https://doi.org/10.1007/978-3-030-44897-4
©FiBS - Forschungsinstitut für Bildungs- und Sozialökonomie, and HIS-Institut
für Hochschulentwicklung e.V. (HIS-HE) 2020.
This book is an open access publication.
A German version of this report has been published as Working Paper No. 42 by Hochschulforum
Digitalisierung in 2019 (Orr, D., Luebcke, M., Schmidt, P., Ebner, M., Wannemacher, K., Ebner, M.,
Dohmen, D. (2019). AHEAD Internationales Horizon-Scanning: Trendanalyse zu einer Hochschulland-
schaft in 2030 Hauptbericht der AHEAD-Studie. Arbeitspapier Nr. 42. Berlin: Hochschulforum Digi-
talisierung. https://doi.org/10.5281/zenodo.2677655.
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About This Book
Between February 2018 and January 2019, a systematic analysis of current trends
and requirements in the areas of knowledge and competence was carried out within
the project (A) Higher Education Digital (AHEAD)International Horizon
Scanning/Trend Analysis on Digital Higher Education.One aim of this project
was to examine the latest developments in learning theory, didactics, and
digital-education technology against the background of (increasingly) digitized
higher education. The analysis formed the basis for a horizon scanning of higher
education in 2030, designed to develop future scenarios that would meet future
higher education demands by taking advantage of social and digital innovations.
This study was conducted on behalf of the German Federal Ministry of
Education and Research (BMBF) by the FiBS Research Institute for the Economics
of Education and Social Affairs together with the HIS Institute for Higher Education
Development e.V. (HIS-HE).
The AHEAD study was commissioned to look into the future and reveal what
the higher education landscape could look like in 2030. The study takes account of
technological developments in society, without seeing them as the sole force
driving future higher education. Rather, it assumes that higher education will
change by 2030 as a result of developments in the following areas:
Knowledge and competence requirements from industry and social changes, in
an increasingly digitalized world;
New developments in didactics, reecting current discussions in the eld of
didactics and learning theory;
Digital technologies and new ways of using these technologies, which are likely
to create new forms of learning and environments for learning.
This study was conducted in two phases. First, preliminary studies were carried
out to investigate the three areas mentioned above, using literature evaluations,
surveys, interviews, and subsequent discussions with the AHEAD International
Advisory Board.
vii
A comparative literature analysis at the beginning of the study clearly revealed
thematic focal points by discipline; the ndings are summarized in the following
three core statements, which are central to the research approach adopted here:
The literature shows that the economic view of the future of higher education is
clearly focused on students, in the context of the labor market and labor-market
requirements.
The educational science perspective, on the other hand, emphasizes the role of
learning and the skills and competences that students will need to succeed in the
labor market.
Technology and digitization are central topics only in the eld of computer
science.
A comprehensive view of higher education in 2030 must merge all of these
perspectives into one picture of the future.
An examination of other predictive studies of higher education shows that many
future scenarios focus on institutions of higher education and examine the question
of what such institutions could look like in 2030. However, this question depends
not only on demand, but also on the freedom to shape and reform higher education,
which is determined by governance regulations, including laws, nancing methods,
and quality assurance.
The AHEAD study has therefore adopted a different perspective. The project
team, in cooperation with the Advisory Board, and following discussions with many
experts and stakeholders, decided to put learners at the center of the concept
because higher education exists to support learners. The demands of the labor market
and society have an impact on learners, who remain central to good didactic concepts.
Digital technologies allow more exible learning, as well as opportunities to learn in
very different spaces, blurring the boundary between physical and virtual presence.
In the second phase of the project, four learning pathways were developed to
provide a view of higher education in 2030. These learning pathways and their
elaboration were based on interviews with experts and initiators of innovative
learning opportunities, group discussions, and an international survey conducted by
the team during the project. In addition, innovative use cases were researched to
illustrate these learning paths. The learning pathways are briey described below
(named after toys for ease of recall):
Tamagotchi:Here, as at present, the study program offers basic, compre-
hensive preparation for subsequent employment, with the university functioning
as a closed ecosystem that supports and guides students as they pursue a course
of study. This model is particularly well-suited to people who go (almost)
directly from school to university or college.
Jenga:In this model, the rst-degreeprogram offers a solid foundation of
knowledge and competences, and can take the form of a shortened study pro-
gram. The curriculum builds on this foundation and is constantly expanded by
the learner (student) through new learning blocks. These additional blocks are
made available by various training providers.
viii About This Book
Lego:The course of study is no longer completed as a compact unit at a
university or college, but consists of individually combined modules of different
sizes from different training providers. The learners themselves decide which
learning phases or units they want to complete. In addition to providing the
learning units, the university is responsible for recognizing completed learning
phases by providing formal certicates or documentation.
Transformer:The students in this model do not transfer directly to higher
education as school-leavers, but have already acquired their own professional
identities and life experiences. They attend university or college later in life
integrating their life experience into their studies. They need a exible course of
study that alternates between didactic control by teachers and advisors, and their
own self-determination.
This vision of a higher education landscape that emanates from the learner has
been shown to foster open discussion. As a result of this change in perspective,
questions about institutional support, governance, and quality assurance, as well as
issues involving institutional nancing for restructuring and infrastructure (which
would otherwise have a major impact on any debate about the future form of higher
education or higher education institutions) move into second place. Although the
suggested learning pathways will have a substantial impact on the organization and
activities of universities and colleges, as well as on higher education policy and
governance, the present study will not investigate this topic further.
The use cases described in this study show how technology can be fully
embedded into educational initiatives. The practical examples showcase a new
strategic approach that is not merely additive but highlights daring reform efforts,
avoiding the less promising approach of placing new technology in old structures.
Innovation is not simply based on technology but on the use of new technologies to
achieve (higher) education goals more fully and effectively for all.
The FiBS Research Institute for the Economics
of Education and Social Affairs
The FiBS Research Institute for the Economics of Education and Social Affairs
(Forschungsinstitut für Bildungs- und Sozialökonomie) is an independent institu-
tion that carries out application-oriented research and consulting on lifelong
learning, from early childhood education to continuing education; it is based in
Berlin and interfaces with the labor market, innovation, digitization, social issues,
and demographic development. The institute is active in Germany, Europe, and
worldwide; its mission statement is Enhancing Lifelong Learning for All.
FiBS was founded in 1993 by its owner and director, Dr. Dieter Dohmen, as
interdisciplinary research and consulting institution and think tank, with a focus on
science-based policy advice. For more than 15 years, a key area of focus has been
the impact of digitization on education, learning, and the labor market. In addition
About This Book ix
to the present study, which investigates the implications of digitization on uni-
versities, particularly in relation to vocational education and training (with a focus
on developing countries), we have developed market potentials and business
models for the higher education sector at an early stage. Currently, we are also
working to integrate this topic into curricula.
The HIS Institute for Higher Education Development
(HIS-HE)
The HIS Institute for Higher Education Development (HIS-Institut für
Hochschulentwicklung e. V., HIS-HE) is dedicated to the promotion of science,
research, and teaching. This research-based, independent competence center spe-
cializes in consulting and know-how transfer on topics that relate to university
development and the organization of research and teaching. The federal states of the
Federal Republic of Germany are members of the HIS Institute for Higher
Education Development.
With the HIS Institute for Higher Education Development, the German Länder
maintain an institution whose prole enables the development of basic principles
for the construction, use, and organization of universities, research, and educational
institutions; it also provides planning assistance and policy advice on questions of
strategy, management, organization, and process design, as well as technical and
structural equipment.
x About This Book
Contents
1 A University Landscape for the Digital World ................. 1
2 From Lines of Development to Scenarios ..................... 5
2.1 Background Studies .................................. 6
2.1.1 A Literature Analysis and the Future of Higher
Education .................................... 6
2.1.2 Knowledge and Competence Requirements of a Digital
Society ...................................... 8
2.1.3 University Didactics-Related Challenges for a Digital
Society ...................................... 12
2.1.4 Technological Conditions and Opportunities for Higher
Education in a Digital Society ..................... 16
2.2 Development of Scenarios and Validation Discussions ......... 20
2.2.1 Modeling that Focuses on Institutions and Governance
Issues in Particular .............................. 21
2.2.2 Modeling that Focuses on Technology ............... 21
2.2.3 Modeling that Focuses on Social Developments ........ 22
3 Four Models of Higher Education in 2030 .................... 25
3.1 Brief Descriptions of the Learning Pathways ................ 26
3.1.1 Tamagotchi: Higher Education for a Good Start in Life ... 26
3.1.2 Jenga: Higher Education as a Solid Foundation for
Further Development ............................ 29
3.1.3 Lego: Higher Education as a Kit ................... 33
3.1.4 Transformer: Higher Education as an Opportunity
for Change ................................... 35
3.2 A Detailed Analysis of the Models of Higher Education
in 2030 ........................................... 36
3.2.1 Environmental Requirements and Models ............. 36
3.2.2 Didactic and Technological Features of the Models ...... 39
xi
4 Outlook on a New University Landscape in 2030 ............... 43
4.1 A New Focus on Learning Pathways in the Era
of Digitization ...................................... 43
4.2 The Future Relevance of Learning Pathways for the Higher
Education Landscape of 2030 ........................... 44
Appendix ................................................... 47
References .................................................. 55
xii Contents
About the Authors
Dr. Dominic Orr holds a Ph.D. in Comparative
Educational Science from the Technical University of
Dresden and is a Professor of Educational Management
at the University of Nova Gorica. From 2015 to early
2019, he was the project manager and senior researcher
at FiBS Research Institute for the Economics of
Education and Social Affairs and is currently a senior
researcher at Kiron Open Higher Education. He has
also worked on the relationship between research,
policy, and practice in many international contexts,
including as a consultant for the OECD, UNESCO, and
the World Bank. In addition to leading this AHEAD
project, he is a member of the MIRVA project, which
aims to make the recognition of skills and competencies
through digital badge technologies visible and valuable.
Dr. Maren Luebcke is a research associate in the
Department of University Management at the HIS
Institute for Higher Education Development (HIS-HE)
in Hanover. Her consulting and research focus at
HIS-HE is the digitization of research and teaching at
universities. She holds a Ph.D. in Communication and
Internet Sociology and a Master of Higher Education.
She has worked in various international research
projects on e-learning and e-democracy and is the
author of various publications in this eld.
xiii
J. Philipp Schmidt is Director of Learning Innovation
at MIT Media Lab where he leads, teaches, and develops
ML Learning initiative. He is also a co-founder and board
member of Peer 2 Peer University (P2PU), a nonprot
organization that provides access to higher education
through public libraries.
Markus Ebner is a junior researcher in the Organi-
zational Unit Teaching and Learning Technologies at the
Graz University of Technology. His doctoral thesis deals
with the areas of e-learning, mobile learning, technology-
enhanced learning, and open educational resources. He
focuses on learning analytics in the primary and
secondary school environment and on educational
informatics.
Dr. Klaus Wannemacher is a senior consultant and
project manager in the University Management
Division of the HIS Institute for Higher Education
Development (HIS-HE). As an organizational consul-
tant, he supports universities, nonuniversity research
institutions, and ministries with consulting services and
research projects with a focus on the digital transfor-
mation at universities in research, teaching, and
administration. In 2016, the Society for Media in
Science (GMW) appointed him as a Fellow. In 2017,
the German RectorsConference nominated him for
participation in the Digital Informationinitiative
of the Alliance of German Science Organisations.
xiv About the Authors
Priv.-Doz. Dr. Martin Ebner is the head of the
Department of Teaching and Learning Technologies at
the Graz University of Technology, where he is
responsible for all e-learning issues. Furthermore, he
researches and teaches as a habilitated media informa-
tion scientist (specialist eld: educational informatics)
at the Institute for Interactive Systems and Data Science
around technology-supported learning. His main
focuses are seamless learning, learning analytics, open
educational resources, maker education, and informat-
ics basic education. He blogs at http://elearningblog.
tugraz.at and further details can be found at http://www.
martinebner.at
Dr. Dieter Dohmen is the founder, owner, and
Director of the FiBS Research Institute for
Educational and Social Economics and works as a
scientist and consultant, currently in Germany as well
as in various other European and non-European coun-
tries. He is the Scientic Director of all projects. After
his studies in sports and social sciences at the German
Sport University Cologne and the University of
Cologne, he obtained a diploma in economics and
social sciences at the University of Cologne and a
doctorate at the Technical University Berlin.
About the Authors xv
List of Figures
Fig. 2.1 Frequency of named keywords in the body of literature
studied ............................................. 7
Fig. 2.2 Perception of being unqualied among graduates recruited
by subject area (selected areas), share 2014 (EU-28) .......... 10
Fig. 2.3 Literature analysisthe evaluation of frequencies in the
category learning.................................... 13
Fig. 2.4 New learning spaces that integrate analogue and digital
approaches .......................................... 16
Fig. 2.5 Requirements for higher education from the perspective
of students .......................................... 24
Fig. 3.1 Four learning paths in the 2030 higher education landscape ..... 26
Fig. 3.2 Graduation rate in the 2534 age group (selection of countries),
19902016 .......................................... 37
Fig. 4.1 Assessments on the current and future signicance of the four
learning paths ........................................ 45
xvii
List of Tables
Table 2.1 Various learning arrangements .......................... 13
Table 3.1 Differences in the didactic and technological aspects of the
four models ........................................ 41
xix
Chapter 1
A University Landscape for the Digital
World
Abstract As the digital transformation clearly highlights the role of universities
and institutes of higher education in shaping a higher education system that is more
open and provides education to everyone who can benefit from it, this study seeks to
analyze, in more detail, what developments are having an impact on higher education
and develops future scenarios for education in 2030. The UK study Solving future
skills challenges implies that the linear model of education–employment–career will
no longer be sufficient in the future, requiring new combinations of skills, experience,
and collaboration from educators and employers. This UK study serves as a starting
point for the AHEAD trend analysis for a higher education landscape in 2030. Five
premises ranging from “No naive innovation view” to “Realistic approach,” and
“Diversity in higher education” provide the basis for a search for concepts for the
higher education of the future.
In the future, universities and institutes of higher education will play an even more
central role in managing and shaping the digital transformation.
Higher education fulfills several objectives for society. In the areas of research
and teaching, it primarily creates an educational space to prepare for the future. It
prepares students for their further personal and professional development, which will
be subject to considerable dynamics. It also provides a space for reflexive thinking
about what it means to be a citizen of the globalized, digitized world, ultimately
offering students opportunities to further develop their character and attitudes.
In addition, the higher education system will need to be more open in the future,
providing access to quality education to everyone who can benefit from it.1This
study addresses the relationship between higher education and initial and contin-
uing vocational education and training, which are still strongly separated in most
worldwide education systems (and particularly in Germany).
1Among OECD countries and countries in the European Higher Education Area, higher education
continues to be socially selective (Blossfeld et al., 2017; European Commission/EACEA/Eurydice,
2018).
© FiBS - Forschungsinstitut für Bildungs- und Sozialökonomie, and HIS-Institut
für Hochschulentwicklung e.V. (HIS-HE) 2020
D. Orr et al., Higher Education Landscape 2030,
SpringerBriefs in Education, https://doi.org/10.1007/978-3- 030-44897-4_1
1
2 1 A University Landscape for the Digital World
The potential of digitization for universities lies not only in the function it can
add through e-learning but also in its integrative force, which can improve higher
education as a whole, as the 2018 position paper, “Bologna Digital,” makes clear
(Gibb, Hofer, & Klofsten, 2018; Orr, van der Hijden, Rampelt, Röwert, & Suter,
2018a, 2018b). The present study incorporates this idea.
Digitization will lead to changes in the higher education landscape; indications
of such changes are presented here. This study does not assume that the university
landscape as a whole will be a victim of destructive innovation (“disruption”). The
high expectations associated with innovations developed in the Silicon Valley envi-
ronment (keyword: MOOCs) have not yet revolutionized higher education. Instead,
universities have adopted these innovations and integrated them into existing degree
programs (Jansen & Konings, 2017; Reich & Ruipérez-Valiente, 2019). However,
digital developments can also help universities redefine and better fulfill their role.
The emergence of innovative new models and organizations will enrich the higher
education landscape. Future progress is not just a matter of retrofitting longstanding
higher education approaches (Kelly & Hess, 2013), but also of extending them to
foster sustainable changes.
What is meant by “Digitization” as a Process?
According to the Oxford English Dictionary, digitization is the conversion of
a text, image, or sound into a digital form that can be processed by a com-
puter. This material process in itself has little influence. To achieve a significant
impact, digitization must be integrated into a far-reaching processand a cor-
responding ecosystemthat uses digital materials for digital transformation
(in short: digitalization) (Brennen & Kreiss, 2016). The Internet and digital
networks are means to connect different types of information, to generate new
data flows, and to structure communication channels for improved interac-
tion between people and processes. The new information nodes and networks
enable a new form of process organization (Castells, 2010; Cerwal, 2017).
The application of new digital technologies is therefore not only a question of
what technology can do but also how it interacts with other established prac-
tices and individual and organizational routines. The particular challenge of
the twenty-first century is to ensure that all sectors benefit from the increasing
digital transformation of society.
The aim of this study is to analyze in more detail the developments that are having
a major impact on the environment of higher education, and to develop scenarios
for higher education in 2030 on this basis. The present study thus meets a central
demand of the position paper by Baumgartner (2018) on the future role of higher
education: “We need more creative scenarios with which we can think about the
future of social developments and their possible consequences for our institutions
(such as universities).”
1 A University Landscape for the Digital World 3
The organization that represents universities in the UK has recently carried out a
study of higher education requirements in a digital, networked world (Universities
UK, 2018). The conclusion of this study provides a suitable starting point for the
AHEAD study:
The linear model of education–employment–career will no longer be sufficient. The pace of
change is accelerating, necessitating more flexible partnerships, quicker responses, different
modes of delivery and new combinations of skills and experience. Educators and employers
need to collaborate more closely, and develop new and innovative partnerships and flexible
learning approaches. (ibid.)
Thus, concepts are sought for the higher education of the future, which must
become stronger and stronger, while building on the current structure of higher
education. Such concepts could have an evolutionary and transformative effect on
today’s higher education system.
This search is based on the following five premises:
No naive innovation view: It can be assumed that some parts of the (institution-
alized) system will resemble the current one, while innovations will emerge both
within this system and through new organizations.
Transfer and renewal through digitization: Digitization is expected to have an
impact on many areas of higher education provision and beyond. In addition, new
forms of higher education will become increasingly sustainable and scalable.
Realistic: The scenarios should, where possible, have points of contact with cur-
rent systems of higher education, allowing their potential, including the tensions
inherent in the models, to be demonstrated on the basis of exemplary develop-
ments. The year 2030 has been chosen as a future endpoint to ensure that innova-
tions are linked to the current situation and the perspective does not become too
speculative.
The perspective of the learner: The learner’s path through the educational system
is the focus of this investigation. The educational provisions offered by universities
depend on learner requirements.
Diversity in higher education: In contrast to other future-oriented studies, this
paper does not assume that there will be one model of higher education in the
future. Instead, we assume that the higher education landscape will continue
to become more diversified and that alternative learning and higher education
paths will develop in response to various challenges and ultimately coexist. For
this reason, the study refers to “higher education” in general, and not simply to
institutes of higher education.
4 1 A University Landscape for the Digital World
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate
credit to the original author(s) and the source, provide a link to the Creative Commons license and
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The images or other third party material in this chapter are included in the chapter’s Creative
Commons license, unless indicated otherwise in a credit line to the material. If material is not
included in the chapter’s Creative Commons license and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder.
Chapter 2
From Lines of Development to Scenarios
Abstract After examining the current developments in the field of knowledge and
competence requirements, university teaching and technology, and their effects on
a digital society through various background studies, this chapter focuses on mod-
eling and developing different scenarios and discussions with regard to technology
and social developments. Different economic and social requirements as well as
new forms of didactics and learning environments will lead to necessary changes
in higher education. It should provide a link between continuing and higher edu-
cation by identifying new ways of recognizing skills acquired informally. Strong
support most notably for new students, should combine performing, developing, and
explorative teaching and learning situations. Meanwhile, it will be essential for the
didactics of the future to be sensitive to the needs of learners and offer individual-
ized support for student-learning paths, making education independent of time and
place. Finally, selected approaches to developing future scenarios in higher education
focusing on institutions and governance issues, technology, and social developments
are discussed in more detail.
The study assumes that higher education will change by 2030 as a result of
developments in the following areas:
Knowledge and competence requirements emerging from the economy, as well
as social changes in an increasingly digitalized world;
New developments in didactics, arising from didactic discussions of the subject;
Digital technologies and new uses of technology that enable new forms of learning
and learning environments.
As a first step, this study used methods of systematic analysis, based on the lit-
erature review, data analysis, interviews, and expert discussions, to identify likely
potential changes in the future higher education landscape. To scan the higher edu-
cation horizon (Amanatidou et al., 2012), these analyses have been condensed into
future scenarios in the second step; they have been validated and further developed
© FiBS - Forschungsinstitut für Bildungs- und Sozialökonomie, and HIS-Institut
für Hochschulentwicklung e.V. (HIS-HE) 2020
D. Orr et al., Higher Education Landscape 2030,
SpringerBriefs in Education, https://doi.org/10.1007/978-3- 030-44897-4_2
5
6 2 From Lines of Development to Scenarios
through a broad discussion with experts from the university sector, politics, and stu-
dents. In addition, innovative practical examples have been sought from all parts of
the world and incorporated into the developing scenario, as possible future models.
Detailed information on all of these areas can be found in the appendix.1The
following section presents the most important results of the investigation, which
have significantly influenced the scenario-development process.
2.1 Background Studies
2.1.1 A Literature Analysis and the Future of Higher
Education
The Big Data approach was initially used to carry out a literature and citation anal-
ysis, with specialist literature2identified via the Web of Science database. The cen-
tral search terms were as follows: higher education/universit[y/ies], futur[e], digital,
work, competenc[y/ies], and labo[u]r [market/force]. A total of 15,249 predomi-
nantly English-language articles, published during the last 40 years, were included
in the analysis (83% were published during the last ten years).
This data set was analyzed thematically to determine the importance of certain
topics in the literature. Ten thematic terms were used for the analysis; these were
searched for in titles, abstracts, and keywords. The thematic terms covered the fol-
lowing areas: learning; knowledge; skills (competency, skills, learning); teaching;
students; the labor market; work; technology (technology, digital); other aspects of
digitization (digital divide, data security); and higher education. A meta-analysis of
the main topics by discipline provides the first glimpse into discussions about the
future of universities. This analysis, however, has focused on selections in which the
words “future” and “university” appear together (n=8359). Figure 2.1 compares
the priorities of the educational sciences, psychology, business studies, and computer
science.3
This comparative analysis clearly shows the thematic focus of the contributions
by discipline; the findings can be summarized in the following three core statements:
1. The economic view of the future of universities is clearly focused on students,
within the context of the labor market and labor market requirements.
1The appendix is only available in German.
2This database holds and provides an index of published literature (in particular, articles from sci-
entific journals) in a wide range of disciplines, including medicine, the natural sciences, humanities,
the social sciences, and economics.
3Individual contributions can also be assigned to several disciplines.
2.1 Background Studies 7
Fig. 2.1 Frequency of
named keywords in the body
of literature studied (The
terms “digital divide” and
“data security” do not appear
in all of the illustrations
because they occurred so
rarely). Source Own
illustration
Literature from the ield of computer sciences (n=441)
Literature from the ield of education sciences (n=2686)
Literature from the ield of psychology (n=607)
Literature from the ield of business studies (n=629)
8 2 From Lines of Development to Scenarios
2. By contrast, the educational science perspective emphasizes the role of learning
and the skills and competences that students must acquire to succeed in the labor
market.
3. Technology and digitization are thematic focal points for computer science only.
This insight leads to the conclusion that a comprehensive view of higher education
in 2030 must unite all perspectives into one picture of the future. The following
sections present the findings on and expectations of future higher education obtained
from the literature and data analysis, as well as from expert interviews on the three
perspectives mentioned.
2.1.2 Knowledge and Competence Requirements of a Digital
Society
According to the German Rectors’ Conference, “Universities are the ‘engines’ of
economic and social innovation in Germany and a key sector for the road to ‘Indus-
try 4.0’” (HRK, 2018). They are characterized by the promotion of professional
development, the transfer of knowledge, and practical education. Accordingly, it is a
priority for higher education to prepare for central trends and movements in society,
but also to shape such developments. It is not enough to focus solely on the new gen-
eration of university graduates. Technological progress in a digital world—coupled
with demographic change—means that higher education must finally be opened to
all. With regard to 2030, the “Action Council on Education” (Aktionsrat Bildung)
writes: “In view of the accelerating pace of technological progress, however, it will
be less and less sufficient in future to cope with the structural change in occupations
through the arrival of graduates with new qualifications” (Blossfeld et al., 2017).
Older workers will also need new skills.
The particular challenge of the twenty-first century is to ensure that all parts of
society benefit from the increasing integration of digitization into society. Discus-
sions about future requirements of the labor market, due to the effects of automation,
artificial intelligence, and Big Data-based algorithms, point to massive changes. It is
expected that this dynamic will result in the majority of graduates changing career
paths several times during their lives (Manyika et al., 2017; OECD, 2017a). In many
sectors of the labor market, employees will require retraining and new learning to
reposition themselves as capable of implementing the technologically improved pro-
cesses that will increasingly define their workplaces. It is the task of business, interest
groups, and politicians to promote and facilitate this process of change.
Many recent studies of labor market developments have addressed the polarization
expected as a result of increasing digitization. The trend is toward tasks that require
more advanced professional skills, coupled with social, and emotional skills as the
study of selected OECD countries has shown (Nedelkoska & Quintini, 2018). In
addition, the labor market is eroding. Professions that require mid-level qualifications
(i.e., high-level technical training but no academic degree) and involve moderately
2.1 Background Studies 9
difficult routine tasks, appear to be declining. Such professions are costly enough
to justify investing in their replacement but routine enough to be susceptible to
replacement by automation (OECD, 2016; Zenhäusern & Vaterlaus, 2017).
However, another OECD analysis has shown that, in most sectors of the economy,
the decline in employment at the intermediate-qualification level is fully offset by
growth at the high-qualification level (OECD, 2017b). To date, the two sectors that
have experienced the greatest changes in this direction are the paper and publishing
industry and the financial and insurance sectors. In the wholesale and retail trade and
hotel and restaurant sectors, employment by skill level has declined, contrary to the
general trend (i.e., jobs are being cut in these sectors). Even when such transforma-
tions do not lead to job losses, an analysis of job markets in Germany and Austria has
shown that wages for employees unable to make this change are declining (Südekum,
2018).
Where these analyses are broad in scope, they conceal differences between occu-
pations that require an intermediate level of skill. An analysis based on the US data
has shown the same decline in medium-skilled jobs, with weak growth in some sec-
tors. Holzer has identified “new medium-sized jobs” that are currently being created
in the labor market (Holzer, 2015). The professions involved include specialized
health technicians (e.g., phlebotomists, X-ray technicians), paralegals, security ser-
vices, cooks, managers of food and beverage companies, retail managers, and field
representatives. In contrast to the “old middle,” most of these modern workplaces
expect their employees to carry out relatively complex technical, administrative, or
communicative tasks. An expanding and differentiating working population needs
more opportunities to engage in higher education at different phases of life; learners
from this group also have very different educational biographies.
The central role of economic institutions is to find new forms of organization,
production, and supply processes to ensure their economic survival and success. As
learning also takes place within business enterprises, it makes sense to integrate
learning experiences more effectively through exchanges between companies and
universities.
It is the responsibility of the education system to educate and train future
and current workers, ensuring that they acquire appropriate knowledge and
skills. The education system must ensure that current workers can benefit from
new developments, while also enabling new generations of entrepreneurs to become
reflective and innovative and to create new businesses that operate sustainably in a
global world.
Workers must be resilient enough to cope with change; they must be able to reposi-
tion themselves throughout their careers. They must also be creative enough to solve
problems and develop new ideas for future progress. Many people are expected to
work in jobs that do not exist today. A work report proposed 21 such jobs, includ-
ing Human–Machine–Teaming Managers, Big Data Detectives, AI-based Personal
Health Technicians, Digital Tailors, and Personal Data Brokers (Pring, Brown, Davis,
Bahl, & Cook, 2017). Although such jobs are unlikely to represent a large section
of the future labor market in 2030, all employees will need to be tech-savvy. A cen-
tral aspect of many workplaces will revolve around enabling people (with different
10 2 From Lines of Development to Scenarios
backgrounds and specializations) and machines to work together in teams to exploit
the possibilities of personal data securely while protecting personal identity. What
is certain, therefore, is that the mix of standardized knowledge, new knowledge,
and transversal skills in all training programs will have to be reviewed regularly in
the future (OECD, 2018b; Universities UK, 2018).
The demand for university graduates in the labor market, both in terms of employ-
ment levels and relative wage premiums (European Commission/EACEA/Eurydice,
2018), indicates that university graduates are already acquiring some of these com-
petences through their studies or as students. However, this is not the whole truth.
A European survey of new recruits found that graduates were much less likely to
feel underqualified in their new jobs (i.e., that their current skills were below their
job demands in self-assessments) than were employees whose formal education was
below university level (CEDEFOP, 2018). Nevertheless, the same study also showed
that more than a fifth of all graduates felt poorly prepared for their new jobs.
As shown in Fig. 2.2, graduates were most likely to feel underqualified in the
fields of engineering, medicine, and agriculture. The authors of the present study
have assumed that this finding reflects (among other things) a constantly changing
qualification context, due to the continuing development of new technologies, work-
ing methods, and techniques (CEDEFOP, 2018). Another study, based on the same
dataset, has argued that the lack of standard knowledge in these specific areas is a less
significant issue than deficits in soft skills, such as patient-communication skills and
teamwork preparation (Livanos & Nunez, 2015). These deficits in the preparation
and support of medicine are already widely discussed in Germany (Kuhn, Jungmann,
Deutsch, Drees, & Rommens, 2018).
These data initially reflect the transition from education to working life. In an
innovative environment, such learning curves are likely to be repeated, as jobs are
reorganized and practices changed to make the best use of digital opportunities over
Fig. 2.2 Perception of being unqualified among graduates recruited by subject area (selected areas),
share 2014 (EU-28). Source Cedefop European skills and job survey (ESJS)
2.1 Background Studies 11
the course of a career (Bessen, 2015). As the question of the optimal knowledge and
competence profile for employees continues to arise and be debated, new learning
options seem necessary.
Conclusion: Requirements for Higher Education in 2030
Higher education can contribute to meeting the challenges posed by changes
in the labor market through the following measures:
All higher education programs should review their learning objectives
to ensure that they explicitly address learning that combines disciplinary
knowledge, basic skills, transversal skills, and digital skills.
As multiple skills will need to be combined and applied simultaneously
in an (often international) teamwork environment, authentic learning that
establishes a strong link to future workplaces will become an increasingly
important didactic tool.
As changes in the labor market increase, employees will require more fre-
quent learning processes and experiences. To meet this need, opportunities
to begin and leave degree programs should be made more flexible (e.g.,
through modules and credits). Learning opportunities should be provided
in ways that allow people to complete aspects of learning alongside their
careers.
In the future, employees without a university degree will tend to work in
occupations in which a high degree of automation can be expected. Their
skill profiles are more likely to be deficient in basic, transversal, and digital
skills; they are also less likely to receive further training over the course
of their careers. Higher education providers can help to reintegrate such
employees into formal education.
Since informal learning (at least) takes place continuously throughout most
people’s lives, one way to activate further learning paths is to identify new
ways of recognizing skills learned informally, as an aspect of formal learning
paths, both during and potentially through higher education. Universities
could establish themselves as important actors by providing accreditation
and learning support to the whole population. To achieve a highly responsive
higher education sector, it will be essential to strengthen the cooperation
between continuing and higher education, as the current structure lacks clear
linear pathways from higher education to career development. Supplements
from continuing education alone are unlikely to resolve this challenge in
the future.
12 2 From Lines of Development to Scenarios
2.1.3 University Didactics-Related Challenges for a Digital
Society
This section investigates the university from an internal perspective, identifying
the trends expected to shape university didactics in the year 2030. The term “di-
dactics” denotes the relationship between content (What is to be taught?), activa-
tion/motivation (How do learners succeed in being motivated to learn?), and support
(How are learners accompanied in learning?) (Reinmann, 2015).4For the period up
to 2030, didactics are likely to focus on activating learners, rather than the range of
courses on offer. Although this so-called “shift from teaching to learning” is not new
(Barr & Tagg, 1995; Cedefop, 2009), it is likely to remain a dominant paradigm in
the context of digitally supported learning arrangements that offer effective learning
scenarios to heterogeneous groups of learners.
An analysis of the relevant educational and pedagogical literature, carried out
within this study, confirms that the question of learning is prominent in higher educa-
tion.5The topic includes student learning, student engagement, and students’ capac-
ity for self-efficacy and self-regulation. Even the assessment of learning outcomes
is offered to students as individuals or in their role as “peers.” The teachers and
teaching disappear almost completely behind them.
The textual evaluation of relevant articles shows that a wide range of terms is asso-
ciated with the topic “learning,” corresponding to the new didactic triangle between
active learning, technology, and network structures (see Fig. 2.3). New technologies,
coupled with high user competence and acceptance and the network effects of social
platforms, can support a more inductive and collaborative form of learning.
Expert surveys and interviews carried out during the investigation of this complex
of topics also reflect the diversity of future forms of learning. From the expert point
of view, the question of how learning spaces can be structured, sometimes collab-
oratively and sometimes autonomously, will be relevant at least until 2030 (Schön,
Ebner, & Schön, 2016).
The question of whether digitally supported methods should be used for learning
is suppressed. Instead, a “fusion” of forms of learning can be observed, carried out
more frequently on-campus and online. This structure requires flexibility in the
roles of teachers and students and in the configuration of their interrelationships
and learning content (Miyazoe & Anderson, 2013; Moore, 1993) (see Table 2.1).
This poses a significant challenge for the future.
4During an early phase of project development, the authors of the study were advised on university
didactics-related challenges by Sandra Hofhues, whose suggestions were incorporated into this
chapter and the in-depth report “A3 University Teaching Challenges within a Digital Society” (see
Annex 6.1.3). The authors thank Sandra Hofhues for her support.
5Articles published in the following journals in 2017–2018 were evaluated (n=509): Internet and
Higher Education, Research in Higher Education, Journal of Higher Education, Studies in Higher
Education, Review of Higher Education, Community College Review, Assessment and Evaluation in
Higher Education, Active Learning in Higher Education, Higher Education Research and Develop-
ment, Journal of Computing in Higher Education, and Perspectives: Policy and Practice in Higher
Education.
2.1 Background Studies 13
2
4
4
5
7
8
10
11
12
16
20
21
28
0102030
Learning Community
Interdisciplinary Learning
Learning Networks (ties, networks, relations)
Service Learning
Interaction
Self-regulated Learning
Learning Activity, Objectives, Strategies, Styles
Active Learning
Learning Evironment
Learning Outcome
Collaborative / Cooperative / Peer Learning
Student Engagement
Learning and Technology
Frequency
Fig. 2.3 Literature analysis—the evaluation of frequencies in the category “learning.” Source Own
illustration
Table 2.1 Various learning arrangements
Learning
arrangement
Presenting Moderating Exploring
The teaching
procedure is …
teacher-led,
deductive
teacher-led, inductive learner-led, inductive
The role of teachers
is …
leading, guiding developing, guiding stimulating, advising
The role of the
learner is …
receiving
comprehensively
participating,
thinking, instructing,
working
working
independently
The learning content
is provided by
teachers and received
by learners
is determined by
learners and teachers
together and worked
on by learners under
guidance
is worked on by
learners
independently
Source Schön et al. (2016)
The expert survey particularly emphasized the need to reorient didactics, in the
context of digitization. The standard model of classroom teaching needs further devel-
opment. Presence learning will be combined with web-based learning processes. In
addition, new institutional formats for didactic self-reflection and the develop-
ment of teaching and learning cultures will be needed to keep pace with increasing
14 2 From Lines of Development to Scenarios
processes of change. Bottom-up developments, resulting from the active practice of
teachers and learners, must be embraced.
By contrast, important trend reports on this topic highlight the qualitative changes
that are influencing the demand for study programs. Demand will increase for life-
long learning courses, online and blended-learning courses, credential unbundling,
and courses that add the greatest value to professional careers. These demands will
ultimately lead to new types of offers being made in the field of higher education.
Sensitivity and openness in higher education will be necessary, especially in rela-
tion to learning content. Research shows that the development of “studyability” is a
long-term process that usually starts in school but continues through the initial phase
of education. In Germany, as in other countries, most universities have introduced
support and bridge courses to meet this demand. The expert interviews emphasized
the central importance of such support measures, which can respond to the differing
needs of learners. In particular, attention must be paid to the future development
and support of student-learning empowerment, i.e., students’ competence at self-
regulated learning, which is central to both “working” and “explorative” learning
arrangements. As students from underrepresented groups are often uncertain about
their choice of field (Hauschildt, Vögtle, & Gwos´c, 2018), too much flexibility in
educational design could exacerbate this uncertainty.
Digitization may offer some solutions. It has been shown that digital bridge and
support programs can help to reduce student concerns by offering better study
orientation (Bidarra & Rusman, 2017; Ubachs, Konings, & Brown, 2017). Accord-
ing to the experts, learning processes in higher education are individualized; more
effective learning is achieved through learning analytics—for example, when the
data generated in learning-management systems are evaluated and used to optimize
learning processes. This also means that the higher education system must increas-
ingly rely on the enhanced competence of teaching staff, who must understand how
this information can be used to promote learning.
Openness in higher education is needed to provide learning plans, objectives,
and curricula. In addition to enabling students to acquire general skills (including soft
skills and “learning to learn”), higher education teaches specific bodies of knowledge
and skills required for particular fields of work or specializations (e.g., engineering
or law); these build the foundation for workplace effectiveness. To identify and
transmit such knowledge and skills, stakeholders must reach a consensus on the
abilities needed in particular areas. In an era of digitization, this consensus will be
subject to constant review (Eckert et al., 2018). Analogous to “Industry 4.0” (see
Sect. 2.1.2), higher education needs a “Curriculum 4.0”.
As a Curriculum 4.0, we understand a curriculum that takes up the process of digital trans-
formation in a targeted manner, both in terms of content and at the level of the skills and
competences to be taught. (…) [We] view digital change in the context of curriculum devel-
opment holistically as a technical, didactic, and content-related challenge. (Michel et al.,
2018)
Effective and individualized university didactics must be based on educational
research, which examines and improves learning and educational processes and
2.1 Background Studies 15
investigates the impact of learning arrangements. Both the literature and expert
discussions revealed deficits in this area that must be resolved by 2030 if higher
education is to become more effective and inclusive. In addition, the educational
mandate must be increasingly reflected in society.
Michael Feldstein, a well-known expert from the American educational technol-
ogy sector, published a pointed presentation on this situation at the beginning of
2019. In his view, new technological developments will only improve learning if
educational research can establish a basic consensus on the central dimensions of
the learning system:
This is not something that could be ‘overhauled’ by the magic of machine learning. (…) We
investigate complex processes that we largely cannot see. When we develop tools that give
us visibility, we often lack the theoretical foundation (…) to understand what we see. With
many things we learn, we do not yet know how to apply them, and much of what we can
apply is separate from our still blurred picture of how learning works. (Feldstein, 2019)
Conclusion: Requirements for Higher Education 2030
The further development of higher education didactics will play a central role in
creating effective and inclusive higher education for all. The following factors
are particularly important:
The provision of flexible higher education depends on didactics that are
sensitive to the needs of learners and open to the needs of society and the
labor market.
Higher education is based on the didactic triangle between active learning,
technology, and network structures; this triangle mediates, appropriates,
and explores learning materials. Digitized solutions can support learning
processes and interactions between learners.
Up-to-date didactics for higher education in 2030 will include new institu-
tional formats for didactic self-reflection; they will increasingly incorporate
bottom-up developments from teaching and learning practice.
Most learners need strong support, at least at the beginning of their study
careers. This is particularly true for learners who finished school many
years earlier. Learning arrangements should, therefore, combine perform-
ing, developing, and explorative teaching and learning situations that offer
more or less support to learners, depending on their career and educational
biographies. Digital and attendance phases are both needed, intertwined
throughout the learning strategy or curriculum.
During the learning phase, an open system of higher education will observe
and react to developments outside the university or formal learning setting.
One particular challenge will be to find didactic methods that bring structure
and control to this open system, creating a learning path that remains trans-
parent to students and teachers alike. Learning analytics and other methods
of observing learning are recommended.
16 2 From Lines of Development to Scenarios
Research on universities and education will be needed to underpin, critically
question, and improve these processes.
2.1.4 Technological Conditions and Opportunities for Higher
Education in a Digital Society
In its recommendations on the differentiation of universities in 2010, the Council
of Science and Humanities emphasized the importance of universities as physical
places and studies as social practices. Digitization was seen as a marginal topic,
related to e-learning (Wissenschaftsrat, 2010). In the future, the contrast between
physical and virtual space will become less and less important—in fact, the two
spaces will “merge” (Schön et al., 2016) (see Fig. 2.4).
In 2030, higher education will be characterized by digital opportunities, digital
technologies, and infrastructures, as well as support structures. To better understand
these opportunities and challenges, two groups of experts were interviewed on the
basis of these guidelines: The first group was composed of technical experts from
“classical” universities in Germany, Austria, and Switzerland (11 interviews). The
results of these interviews are summarized in the section, “Views from the main-
stream higher education sector.” The second group was composed of program lead-
ers of innovative initiatives in or adjacent to higher education (11 interviews in six
countries); these are discussed in the section, “Operational and strategic benefits of
technology in higher education.”
2.1.4.1 View from the Mainstream University Sector
Most experts agreed that video-based courses could be offered in supplementary
or exclusively online formats. Through control questions and tracking, each indi-
vidual’s learning progress can be monitored and adapted to his or her needs, using
Fig. 2.4 New learning spaces that integrate analogue and digital approaches. Source Schön et al.
(2016)
2.1 Background Studies 17
learning analytics. The availability of a range of online channels and materials makes
it possible to reach students outside traditional teaching units. This enables learn-
ing, independent of location and time. Individual study (of specialist or less popular
subjects) could become the norm.
Digitally supported scenarios, which previously featured text-based operations
and limited learning environments, are now becoming more open. Voice control, for
example, opens up completely new ways of interacting with learning environments.
In the future, exchanges with teachers and other learners will become more fluent
and natural for students. People with physical disabilities, who may find text-based
operations difficult, will benefit from this format.
Big Data approaches that combine learning analytics and artificial intelligence
(AI) can use chatbots and e-tutors to accompany students along the learning path. In
such ways, the learning environment will adapt to the needs of individual students.
As this can be done using models developed in the field of AI to predict learn-
ing performance, new learning environments will offer students improved adaptive
learning.
New technologies can also open and plasticize spaces via virtual reality and
augmented reality. In three-dimensional space, products, machines, and processes
can be experienced and manipulated, even if they do not yet exist. Thus, research-
based learning can be implemented in practical ways and making use of all senses
during a course of study (cf. DeYoung & Eberhart, 2018).
Of course, the idea of such learning arrangements is nothing new. To a large extent,
the technology already exists (Altieri, 2018; Zick & Heinrich, 2018). However, such
practices seem to be at the stage of practical testing and prototype implementation
(proof-of-concept).
To make effective use of various forms of online teaching, augmented and virtual
reality, and artificial intelligence, it will be necessary for technical infrastructure
and organizational processes to interact. Teaching staff will also need training and
support. Currently, the study respondents feel that bottlenecks have obstructed the
provision of necessary resources and the will to plan, develop, and establish new
university administrative, spatial, and learning scenarios.
For example, traditional university lecture halls will recede into the background,
to be replaced with spatial planning concepts that meet the needs of modern students
and teachers. Multifunctional rooms with flexible uses will enable new learning
scenarios. It is possible to imagine students meeting in rooms outside the university
grounds, such as “learning cafes” and “fablabs” (cf. Taddei, 2018).
Digital platforms, algorithms, and content can be developed together, taking
advantage of national and international networking. Open licenses for products
and services can promote the exchange and sharing of services, supporting the
implementation of new learning scenarios (Ebner & Schön, 2018).
Nevertheless, the first institutional initiatives will be more expensive than previous
programs, at least during the first conversion and implementation phase. The cost
of the technical infrastructure will naturally increase, as will technology costs per
student, which are incurred by institutions. It is important to prioritize digitization
strategies at an early stage and to establish an innovation-friendly environment at
18 2 From Lines of Development to Scenarios
each university, enabling educators to experiment with implementing new teaching
scenarios, and support the development of new learning paths for students.
Some of the experts warned against assuming that all students owned the nec-
essary hardware for learning (e.g., a laptop or mobile phone). Appropriate support
programs should be established to ensure that less financially well-equipped learners
are given equal opportunities to become part of the educational landscape. Bar-
riers can arise from the availability or nonavailability of Internet access (keyword
“broadband expansion”), essential hardware (e.g., technical equipment for students),
and suitable platforms (e.g., “guidelines for barrier-free web content,” WCAG). The
experts thus addressed the important issue of the “digital divide” (Hess et al., 2016)
and the danger that digitization could lead to a new set of social disadvantages if
such questions are neglected.
Finally, with a view to the future, the experts stressed that, although online teach-
ing and virtual space will be more central and important in the university of the
future, attendance phases will remain important. The experts assumed that some
universities would continue to concentrate primarily on campus-based learning in
2030. Online universities would also establish themselves. This could lead to coop-
eration between the two types of universities, enabling them to achieve their goals
as economically as possible. Such developments could present challenges for the
recognition of learning achievements, especially if parts of the learning process took
place outside the higher education sector.
2.1.4.2 The Operational and Strategic Benefits of Technology in Higher
Education
During the expert discussions, it quickly became clear that true innovation rarely lies
in technology alone, but reflects the way in which technology is used to consistently
redesign educational experiences. The programming school 42, for example, uses
a classic intranet to provide educational content, which is not, in itself, particu-
larly innovative. What is new about 42 is the fact that its entrance examination is
accessible to candidates with no prior qualifications; during “study” periods, any
examination can be repeated until a student has achieved his or her learning objec-
tive. Although this approach can only be implemented with technology, technology
alone is not enough. Another essential element is openness, which makes it possible
to try something new and to question the old.
In the present analysis of higher education in 2030, the influence of digital technol-
ogy has to be considered on two levels. On the one hand, traditional higher education
institutions will increasingly integrate digital technology into their existing processes
(the “operational” approach).6On the other hand, technology will enable entirely new
6A mirror image of this approach can be seen in most responses to the survey of German university
digitization strategies (Gilch et al., 2019), in which digitization is used mainly to improve the
administration of existing processes and to increase efficiency.
2.1 Background Studies 19
models, most of which will emerge outside or on the fringes of traditional universi-
ties; these will represent a digital transformation of higher education (the “strategic”
approach) (Evans & Wurster, 1997; cf. Sollosy, Guidice, & Parboteeah, 2015).
Within the framework of operational use of digital technology in existing univer-
sities, technology-adoption theory provides a useful orientation framework. It states:
“The most important thing in observing [the adoption of technology] is that, at all
times, the choice is not between adoption and non-adoption, but between immedi-
ate adoption and postponing the decision until later” (Hall & Khan, 2003). Perhaps
no profound changes have been needed so far because environmental pressures on
higher education are not yet strong enough and requirements are not yet heteroge-
neous enough. A key question for the future of higher education is how long this
situation will persist. Like other institutions with a long tradition, the higher educa-
tion system is innovation-resistant. This is not necessarily negative. It makes no sense
to follow every new technology trend. On the other hand, resisting innovation may
ensure that important and positive changes are driven by others, putting pressure on
existing higher education structures. Although universities can use innovations from
the edge to drive their own transformations, this will require an ambitious strategic
reorientation.
The potential of the strategic approach becomes clear when considering initiatives
and institutions outside existing institutions. Some education providers have emerged
outside the traditional higher education sector (e.g., 42); some have developed as
start-ups (e.g., Minerva) and are not subject to the usual planning processes (e.g., MIT
MicroMasters); they may exist in new, separate units within a university. This is where
new models will emerge that force stakeholders to question and creatively rethink
many things. Radical changes are likely to affect almost all aspects of universities,
from campus design to ways of undertaking, testing, and accrediting learning, and
the relationship between business and education. Relevant cases are presented in the
following sections of this study as explorative examples. Common to all cases is the
fact that their educational provision embeds the potential of digitization.
Conclusion: Opportunities for Digitally Supported Higher Education
2030
Technological development means that future learning scenarios are possible,
but will require institutional and organizational innovation, not merely the use
of new technologies. The following considerations must be taken into account:
The impact of digital technology can be considered on two levels. On the
one hand, traditional universities will increasingly integrate digital technol-
ogy into existing educational processes. On the other hand, digital technol-
ogy will be used to develop fundamentally new educational providers and
programs. By the year 2030, these may supplement and partly replace the
offerings of traditional universities.
20 2 From Lines of Development to Scenarios
Technical development means that the contrast between analogue and digi-
tal learning scenarios can be dissolved. This offers opportunities to provide
individualized support for student-learning paths. Learning can be inde-
pendent of time and place; individual study (the study of specialist or less
popular subjects) could become the norm for many students.
With technology-based solutions, care must be taken to ensure that all stu-
dents have access to technology and the technical support they need to use
it. Otherwise, the digital divide may promote a new social divide.
Through the use of digital technology, higher education providers can
increasingly benefit from cooperation and exchange, jointly developing
successful concepts and suitable learning materials.
The effective use of these technologies within traditional higher education
institutions will depend strongly on the capacity of institutions to imple-
ment innovation processes. Universities must be willing to make neces-
sary resources available and to question existing administrative, spatial,
and learning scenarios—or to replace them with new approaches.
Furthermore, support will be provided for new, innovative education
providers and models that can supplement the role of traditional universities.
As a rule, innovations need spaces outside the organizational and planning
processes of universities. They develop where they are protected from the
“immune system” of traditional organizations. They can also be separate
units within higher education institutions.
2.2 Development of Scenarios and Validation Discussions
Higher education in 2030 will be determined by the parameters listed in Sect. 2.1.
Labor market requirements for new knowledge and competence will have an external
impact on higher education. The reaction in higher education will be shaped by
didactic models and digitally supported learning scenarios.
This complex structure of effects means that higher education will not have a single
form, becoming, instead, more differentiated (Davey et al., 2018). To develop future
scenarios in higher education, a literature search has provided the three approaches
briefly described below:
2.2 Development of Scenarios and Validation Discussions 21
2.2.1 Modeling that Focuses on Institutions and Governance
Issues in Particular
After examining global developments in higher education, the OECD developed a
four-field matrix based on two opposing pairs: the extent of globalization (global
versus local) and the influence of the state (administration versus market). This
resulted in the following four scenarios (OECD, 2008):
Higher education Inc.—higher education with an international catchment area
and market-oriented offerings. According to van der Wende, this model was the
most likely future model at the time (van der Wende, 2017).
Open networking—a form of higher education that focuses on stronger inter-
national cooperation (networking) and supply-oriented care. This approach has
been strongly influenced by the Bologna Process, taking place in the Euro-
pean Higher Education Area and extending to 48 countries (European Com-
mission/EACEA/Eurydice, 2018). A greater harmonization between systems and
more use of digitization is expected to promote this process further.
New public responsibility—a form of higher education that focuses on the
national market and on market-oriented provisions, which must be accountable
to the state. This approach reflects the increasing focus on the new management
model; it includes, among other things, a performance-related allocation of funds
(Orr & Jaeger, 2009).
Serving local communities—a form of higher education that focuses on the
national market and supply-oriented provision at the local level. This has been seen
as a likely scenario in the event of a possible counter-attack against globalization
(van der Wende, 2017).
2.2.2 Modeling that Focuses on Technology
The Holon IQ analysis has focused on the (expected) impact of technology on higher
education (Holon IQ, 2018). It has proposed five models: Education-as-usual, Global
giants, Regional rising, Peer-to-peer, and Robo Revolution. The first three models
anticipate domestic changes in the higher education sector and roughly reflect the
OECD models mentioned above. These contrast with the last two models, which can
exist without conventional higher education. It is worthwhile to briefly present these
two models:
Peer-to-peer—This scenario is the other side of the OECD scenario, “open net-
working,” since it does not involve institutions, but people, who build their own
learning and cooperation networks. It proposes a module-based learning path that
allows learners to collect “micro credits” as they pursue their own interests and
build careers.
22 2 From Lines of Development to Scenarios
Robo Revolution—The OECD did not consider this scenario because it paid
little attention to the impact of digitization on higher education. In fact, the “Robo
Revolution” is a sophisticated version of the peer-to-peer model, in which artificial
intelligence and machine learning allow for better identification and presorting
of learning materials, making it easier to identify relevant learning resources.
Scalable personalized support can be provided by social bots.
2.2.3 Modeling that Focuses on Social Developments
The “Beyond Current Horizons” study in the UK has carried out an environmental
analysis to develop three complete scenarios of future societies, from which six
educational models have been extracted (Facer, 2009). For each societal scenario, two
alternative models have been proposed for the education system—one with positive
and the other with negative characteristics. The three scenarios bear the names:
“Trust yourself,” “Only connect,” and “Loyalty points.” It is worth presenting these
scenarios and their corresponding models in more detail.
Trust yourself—In this society, citizens take responsibility for themselves. There
are two educational models: informed choice and the independent consumer.In
the case of an “informed choice,” the educational model is based on the personal
learning journey of an individual supported by mentors. The focus is on the indi-
vidual’s journey, within a process of lifelong learning. Educational outcomes are
assessed in the context of the learner’s previous and subsequent learning experi-
ences. In the case of the “independent consumer,” the focus is on the independent
selection of standardized learning materials. This leads to two tensions. The first
tension is a tendency for learners to accept materials provided by well-known
“brand names.” In addition, some learners lack the support to navigate this rel-
atively complex system, especially if their social networks are unfamiliar with
it.
Only connect—This society is focused on the shared task of overcoming great
environmental challenges, which can only be solved collectively. It has two edu-
cational models: integrated experience and service and citizenship. In the case
of “integrated experience,” the educational model is more inclusive than before,
with learning taking place everywhere—at work, in care, during leisure time, and
in educational institutions. This model sees education as integrated; learning is
a collaborative and contextual open process that extends throughout life. In the
case of “service and citizenship,” the dominant view is that individuals must be
taught to be good citizens. Learning is increasingly seen as something that hap-
pens outside people’s social context, providing necessary input for employment,
work, and well-being.
Loyalty points: In this society, the relationship between individuals and busi-
nesses of all kinds is increasingly codified and formalized over time. Individuals
are subject to a network of memberships and associations. These cover all areas of
2.2 Development of Scenarios and Validation Discussions 23
life, controlling, and limiting the behavior of groups and individuals: work, per-
sonal interests, healthcare, family, leisure, and consumption. In this context, the
state focuses on promoting social sustainability, ensuring that the many different
perspectives and priorities within society do not pull strongly in different direc-
tions. This society has two educational models: discovery and diagnosis.Inthe
case of “discovery,” the model for education involves learners moving between
different groups and associations, interacting with and contributing to the various
knowledge communities they encounter. Through this process, learners build a
portfolio of skills and contributions that are digitally captured, authenticated, and
shared. In the case of “diagnosis,” the educational model analyzes each individ-
ual’s skills at an early stage and predicts which links and associations will fit that
person best. As a result, people make fewer efforts to develop larger networks or
affiliations; instead, they aim to be successful within a limited circle of associa-
tions. This leads to a less dynamic society with a high dependence on proximal
networks.
The approach that was chosen for this study also begins with learners and their
learning pathways. As the analysis above has shown, learning will be the central
feature of the digital world and the key to social participation for a wide range of
people.
This approach also ties in with an idea promoted by Barnett University, which
calls its concept of open higher education the “ecological university” (Barnett, 2011).
Barnett distinguishes between three visions of the university: the research univer-
sity—which exists “in itself,” i.e., for science; the entrepreneurial university—which
exists “for itself,” i.e., to support a company; and the ecological university—which
exists “for others,” being open to all and open to the world.
Figure 2.5 places students at the center of the system, surrounded by appropriate
higher education resources that meet their learning needs. This perspective avoids the
“digital-first” approach, which was prominent in the age of e-learning—namely, the
idea that education should begin with technology, rather than with users and benefits
(Andersson, Alaja, & Buhr, 2016; Buhr, 2015; Howaldt & Jacobsen, 2010; Rüede
& Lurtz, 2012). By contrast, this approach emphasizes the idea that social contexts,
such as education, are always about social innovation—how social processes can be
reconfigured to achieve goals more effectively.
According to this approach, in 2030 the higher education landscape will be formed
around various learning paths taken by students. As Fig. 2.5 shows, the AHEAD
concept is based on four ideal learning paths in the university landscape of 2030.
The resulting models of higher education are not exclusive but will coexist because
they address different needs.
The AHEAD models have been further developed and validated in various cycles
by different groups of experts7:
Initial development by the AHEAD team in August 2018;
Presentation of the models and discussion, in the context of the German Higher
Education Forum on Digitization topic week, in September 2018;
7See the Methods section in the Appendix.
24 2 From Lines of Development to Scenarios
Fig. 2.5 Requirements for higher education from the perspective of students. Source Own
illustration
Further development and assessment from an international perspective, provided
by the AHEAD Advisory Board, October 2018;
An online survey of international experts from the higher education sector. The
results of the survey are listed as exemplars in “marginal notes” in the model
descriptions below.
These models are described and then characterized on the basis of their social
drivers, didactic and technological solutions, and innovation potential in the next
chapter.
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate
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the copyright holder.
Chapter 3
Four Models of Higher Education in 2030
Abstract This chapter provides four models of higher education for the year 2030,
namely the Tamagotchi, Jenga, Lego Set, and Transformer models. The Tamagotchi
model represents the classic approach to higher education, starting right after sec-
ondary school and leading up to a bachelor’s or master’s degree and then transi-
tioning into employment, finishing the path of higher education. The Jenga model,
while similar to Tamagotchi, appeals to nontraditional students because of its shorter
learning span and focuses on later phases of self-learning and -organization. The
Lego Set model is fittingly named after the individually combined modules of dif-
ferent sizes, making for a self-reliant and non-standardized learning path rather than
one compact unit. The Transformer model represents learners whose initial phase of
education may have long passed, but who return to higher education to acquire new
basic knowledge or upskill their formal education. It relies on the idea that everyone
must have opportunities to leave their current professional paths and change course.
Figure 3.1 shows the four learning paths in individual career. The blocks represent
the main learning phases of higher education. Of course, learners may be working
while they learn or pursuing other societal commitments.1Phases without blocks
are outside the higher education system and characterized by work or other social
commitments. Each learning path is named after a toy that roughly represents the
main characteristics of this learning path. However, these names should not be taken
too seriously; they are simply intended to help readers remember the core properties
of the four models.
1Currently, approximately half of all students work at least a few hours a week during their studies
(Maseviˇci¯ut˙e, Šaukeckien˙e, & Ozolinˇci¯ut˙e, 2018).
© FiBS - Forschungsinstitut für Bildungs- und Sozialökonomie, and HIS-Institut
für Hochschulentwicklung e.V. (HIS-HE) 2020
D. Orr et al., Higher Education Landscape 2030,
SpringerBriefs in Education, https://doi.org/10.1007/978-3-030-44897-4_3
25
26 3 Four Models of Higher Education in 2030
Fig. 3.1 Four learning paths in the 2030 higher education landscape. Source Own illustration
3.1 Brief Descriptions of the Learning Pathways2
3.1.1 Tamagotchi: Higher Education for a Good Start in Life
Tamagotchi
A closed ecosystem that is built around individual students. The focus is on the
beginning of the learning path.
In this model, students are beginning their careers. Secondary school education
is completed with the acquisition of higher education entrance qualifications. The
transfer to the university takes place immediately afterward. Students study full time,
until their three- or five-year courses end, depending on whether they are aiming for a
bachelor’s or master’s degree. After graduation, the graduates begin their careers. The
purpose of higher education is to enable graduates to obtain work-related skills and
to create a knowledge base that enables them to make the transition to employment.
When students graduate, learning within the higher education system is essentially
finished. Most further learning is nonformal, informal, or demand-oriented, guided by
2Footnotes in the following section reproduce some comments from the international survey (see
Sect. 6.1.3) on the respective models.
3.1 Brief Descriptions of the Learning Pathways 27
each individual’s professional situation. Further education is undertaken but without
an explicit connection to previous study phases.
This model assumes that graduates will continue to be offered a future-proof
education; they will not be trained simply to meet the requirements of the current
labor market but will acquire skills that enable them to help shape their environment.3
The didactic concept of the Tamagotchi model supports learning and personal
development through a learning path that has clearly defined steps and results. This
path continues the school-system approach previously followed. Ideally, secondary
and tertiary education are well-coordinated, allowing the transfer to higher education
to occur without major discontinuities. The concept supports academic orientation
on the one hand and a certain degree of self-organization and independent learning
on the other.
The university remains the central teaching and learning space. Students are
part of a community that promotes the social inclusion of individual students. In
addition to exchanging information on campus, students also learn with the support
of global communication networks, simulations, and augmented reality techniques,
which expand the physical learning environment. Future learning experiences in the
professional world will be integrated through innovative learning spaces, such as
makerspace and fablabs, but also through traditional internships.
In this model, each university is responsible for control and coordination,as
well as the design of the degree program. The introductory phase of studies and thus
the change from school to university are important points in the design.
The Tamagotchi model follows the traditional concept of higher education. It
assumes that the knowledge and skills acquired at university give learners a future-
proof competence profile and enable them to adapt flexibly to future requirements.
One central factor that influences the success and attractiveness of this model
is the diversification of the student group. So far, learning has taken place in cohorts
(relatively age-homogeneous groups) which generally need a certain educational
background to be successful.
If universities recruit more alternative target groups, such as older students,
this may lead to a fundamental change that will not reflect the Tamagotchi model
approach. However, universities will have to react to the growing permeability of the
higher education system by meeting the needs of diversified, often (partly) employed
students more fully and precisely. As universities respond by offering more flexible
courses and student-centered teaching, this model will come under pressure.
To help degree programs become more flexible, governmental steering regimes
will have to adapt by reconsidering key figures relevant to the distribution of funds,
such as graduates within the standard period of study. They will also have to develop
clearly defined control approaches.
3Moreover, this model remains relevant to the process of preparing young scientists for academic
careers.
28 3 Four Models of Higher Education in 2030
The example of Minerva (see Case: Minerva) shows how the Tamagotchi model
can be developed through innovation; here the model offers networked, campus-
independent higher education in bachelor’s programs, consistently exploiting tech-
nological possibilities and removing spatial restrictions. At the same time, this case
succeeds in maintaining the care and support promised by the Tamagotchi model.
Case: Minerva—The World as a Campus
Relevant for the model: Tamagotchi
At first glance, Minerva looks like an ordinary university, and that’s what
it’s meant to be. But if you look under the surface, you discover a whole new
approach to university-organized education. Instead of a traditional campus,
Minerva has a network of seven satellite locations around the world. All courses
are offered online, to small groups of 20 students. Students live in shared dor-
mitories, even though classes are held online. Minerva reveals the possibilities
that exist when digitization is understood and realized in a transformative way.
In this context, traditional ways of organizing education can be presented in
completely new forms.
The private university was founded by Ben Nelson in 2012, with the aim
of offering “Ivy League” quality education, in combination with a different
concept of the learning community. Despite the central role of video-based
teaching, Nelson does not believe that Minerva is innovative because of its tech-
nology. For him, the innovation began with a new pedagogy, built around 100
important ideas, which can be taught, applied, and evaluated (the list includes
both patterns of critical thinking and scientific concepts). The technology is
not decisive, although this approach could not be put into practice without it.
At Minerva, innovation does not end with a new video system but involves
a continual questioning of what role the campus can and should play in this
model. Initially, Nelson and his colleagues did not want to replace the social
experience of living and learning together, but to improve it. To do this, they
did not need their own canteen, lecture halls, library, or fitness facilities, as
these are available in every major city and can be shared. Students develop a
bond with their cohort, but not with a particular location. The university gives
them opportunities to get to know about different cultures and environments.
What does teaching at Minerva look like? All courses are conducted live
via video by professors working with small groups of up to 20 students. In this
seminar-like approach, instruction, discussion, group work, and assessment
are freely mixed—professors have access to “real-time” information on the
students’ learning progress and can thus adjust the pace and content. Although
Nelson mainly talks about the higher quality teaching that can be achieved,
another advantage is the flexibility of physical learning spaces. It is no longer
necessary to invest in large lecture halls—students can log in from a café or
from home—and intelligent technologies can take the strain off tutors.
3.1 Brief Descriptions of the Learning Pathways 29
Minerva is an example of how digital technology can extend a model like
Tamagotchi, which is based on familiar technology. The distributed-learning
approach could also be applied to the Lego and Jenga models.
3.1.2 Jenga: Higher Education as a Solid Foundation
for Further Development
Jenga
Universities offer a solid foundation of knowledge to build on; this foundation
can be constantly expanded by teachers.
As in the Tamagotchi model, students are expected to begin their studies immedi-
ately after obtaining university entrance qualifications. As a rule, students study full
time for up to three years,4acquiring basic knowledge and skills. The initial univer-
sity period is shorter than that in the Tamagotchi model, appealing to nontraditional
students, for whom four or five years of study would be too long. However, this
assumes that learners will expand their knowledge through additional modules over
the course of their lives and after interruptions. Depending on each individual’s pro-
fessional situation, these modules can provide upskilling opportunities or sideways
skills acquisition.
The central idea is that university studies, in the traditional model, are not flexible
or integrative enough to be future-proof in a highly dynamic environment. Courses of
studies must, therefore, be conceived more broadly, with a longer perspective. In the
initial study phase, individuals learn the basics; these skills are then supplemented
later in life. In this model, a didactic decision must initially be made to define the
educational foundation needed to begin a specific career and the content that should be
provided later, in shorter phases of continued education. Whether the basic foundation
includes general or transversal competences or specific basic knowledge depends on
the discipline and university.
It is important, however, that the didactic concept initially focuses on a basic
phase (basic study), which supports later self-learning and self-organization. In this
phase, students’ learning and personal development proceed along a clear learning
path, with fixed steps and clear results. In the first block phase of this model, learning
takes place mainly on campus, with the support of global communication networks,
simulations, and augmented reality techniques that extend the learning environment
through online experiences. Through internships, makerspaces, and fablabs, early
4Like junior colleges in South Korea and so-called “accelerated degrees” in the UK.
30 3 Four Models of Higher Education in 2030
connections to the future world of work are established. After successfully complet-
ing their studies, students leave university and enter professional life. Universities
help to prepare for this transition and focus on this task within the Jenga model. The
second learning phase consists of several learning units, which the learners them-
selves choose, often taking into account the changing competence requirements of
the labor market. The short study blocks can be offered by various training providers;
they can take place either on-campus or online and can also be combined.
Formal recognition of the first learning block is guaranteed. The recognition of
other learning units depends on how such studies are organized within the higher
education landscape. Learners will have opportunities to reach outcome-based agree-
ments with individual higher education institutions, covering both the initial learning
block and additional units. In this way, Learning Phase 1 and Learning Phase 2 can be
integrated into a single study program. However, the two phases can also be accessed
independently.
The Jenga model consistently responds to the needs of students and the labor
market. This study design can prepare for and respond to new needs from the world
of work without abandoning the basic structure of a university course of study.
One example, “MIT MicroMasters,” represents an innovative variant of the Jenga
model. After students acquire a bachelor’s degree in Phase 1, their MicroMasters
learning can be organized very flexibly during the second phase. MIT thus offers an
innovative variant within the existing system.
One major innovation could involve developing an entire study program that
would be provided by different providers during different study phases. Students
would be accompanied throughout the study program, even if only the first part took
place at their own universities. Under this system, universities would require digi-
tal student-administration systems and “stackable” individual digital certificates,
which could later be used to recognize a complete course of study. The question
remains whether preparatory colleges and other providers would collaborate with
traditional universities to create partnerships of this type, or whether they would
rather develop their own overall study/training programs.
Case: 42—Focus on Project-based Learning and Peer Evaluation
Relevant for the models: Tamagotchi,Jenga
Olivier Creuzet (Head of Pedagogy at 42): “We actually lie to our students.
We say they will develop technical skills, but we want to develop adaption,
self-learning, creativity, and other soft skills.”
One characteristic of the Jenga model is direct access to the labor market.
This was also the goal of “42,” an innovative school for software developers
in Paris (with an offshoot in the U.S.), founded in 2013 by Xavier Niel, a
French multimillionaire. Access to 42 is free and organized like a computer
game. Interested learners must first pass the “Piscine” (swimming pool), a kind
of four-week entrance exam, which mainly tests their ability to co-work with
others and apply new knowledge. Success in the Piscine is independent of
3.1 Brief Descriptions of the Learning Pathways 31
existing programming skills. Each student then works on a consecutive series
of projects and simultaneously provides feedback on other students’ projects.
As in a computer game, each project can be improved as often as necessary
before students advance to the next level. This all sounds very modern, but
Olivier Creuzet attributes it to a classic constructivist approach developed by
Piaget and Montessori. What’s new about 42 is that this approach can now be
implemented cost-effectively in larger groups with the help of technology.
Most learners do not yet have a university degree; through 42, they find a
direct path from secondary education to their first jobs. There are exceptions,
however. Some students enter 42 to learn practical programming skills, after
completing a traditional degree. Others are already working as professionals,
but want to reorient themselves; a course of study at 42 may help them enter
university later, as we have outlined in the Transformer model (see below). In
the didactics of 42, learning processes are modeled on the work activities of
programmers. For example, students use the tools and platforms they are likely
to encounter in their first jobs. This approach blurs the strict separation between
work and study. 42 is a direct reaction to the growing demand for software
developers, which traditional universities cannot meet. As technologies are
evolving rapidly, specific programming languages quickly become obsolete.
The careful and therefore slow process of university-curriculum development
cannot keep pace with such applications. However, for many jobs, companies
do not expect a degree in computer science, but simply solid, basic knowledge
(the “craft” of programming) and the ability to collaborate with others and
continue learning.
42 enables students to acquire these key competences. In addition to pro-
gramming, students develop skills such as self-learning and self-organization.
Although these are not directly related to software, they will benefit the students
in their professional lives and further studies. Although 42 aims to provide an
innovative programming education, it also attaches great importance to skills
such as adaptability, self-learning, creativity, and various other nontechnical
social skills. These are exactly the skills that learners in the Jenga model need
to create their own learning paths.
Case: MIT MicroMasters—Flexibility after the First Study Phase
Relevant for the models: Jenga, Lego, Transformer
Since 2016, students who have successfully completed a series of online
courses and then passed an exam under supervision have been able to spend
slightly more than USD 1000 to acquire MicroMasters from the Massachusetts
Institute of Technology (MIT). The first MicroMasters was developed for the
supply-chain-management sector, where there was a growing need for experts
32 3 Four Models of Higher Education in 2030
that traditional universities could not meet. For example, MIT offers only 30
students the option to take a master’s degree per year on campus; this number
cannot easily be increased (or decreased) from one year to the next. So MIT
professors decided to offer their courses online, building a new type of degree.
Although the MicroMasters is not an “official” university degree, it is recog-
nized as a learning achievement by some large companies and 22 universities
in 16 countries. Overall, 40% of MicroMasters students have more than 5 years
of work experience. MicroMasters students are in their early thirties, on aver-
age; approximately half of them already have a university degree. However,
more than 20% come directly to the MicroMasters program without a previ-
ous degree. Completing the full MicroMasters program takes time, initiative,
and motivation. For this reason, few students successfully complete all of the
courses. To date, about 1300 students have received MicroMasters from MIT.
However, this total is 20 times more than the number of students on the MIT
campus who are working on supply-chain master’s degrees. In addition, more
than 30,000 students have completed at least one online module.
The aim of the MicroMasters program was to give more people access to
knowledge and to create a new form of access to the traditional MIT Mas-
ter’s program. However, the results have been much more interesting. Today,
not only does MIT accept MicroMasters when considering applications from
potential students, other universities and even employers do the same. The
MicroMasters program has simply put into practice something that was diffi-
cult to organize in theory—the mutual recognition of course achievements. For
example, an MIT MicroMasters can be used to apply for 69 different master’s
programs at 22 universities around the world, while the online courses are cred-
ited. In just a few years, a global network has emerged that combines MOOCs
with traditional university degrees in this way. Since two of the universities
are located in Europe, this program automatically gives students access to the
European Credit Transfer and Accumulation System (ECTS), as well as eli-
gibility in many of the 48 countries of the European Higher Education Area
(EHEA). Further study is not necessarily the goal of every student, and com-
panies have also noticed the MicroMasters. For example, General Electric, one
of the largest employers in the U.S., guarantees all applicants an interview if
they have a MicroMasters. This is true whether they have a (regular) university
degree or not.
In the Jenga model, a MicroMasters could be one of the study blocks needed
to acquire and carry out a job. However, the case is also relevant to the Lego
model, as a single study block among others, and to the Transformer model as
an alternative path into higher education.
3.1 Brief Descriptions of the Learning Pathways 33
3.1.3 Lego: Higher Education as a Kit
Lego
The course of study is not completed as a compact unit but consists of
individually combined modules of different sizes.
In this model, students are highly motivated and self-reliant and prefer an indi-
vidual, non-standardized learning path that meets their learning needs and interests
fully.5They combine various learning units, which are offered online and on-campus
by different universities and new educational providers. The chain of learning units
forms each student’s personal study process. This model is also characterized by
frequent changes between phases of employment and learning.
The central idea of Lego is to cater to a group of learners who are strongly self-
motivated and able to create individual study programs that meet their own needs.
This approach can succeed, at least for the time being, in professions in which
specific skills, such as software development, are in more demand than professional
qualifications. The primary aim is to acquire knowledge and skills that can be used
directly for personal purposes. Learners may have different motivations for adopting
this approach.
Lego students build their own individual study programs out of various learning
units. They are supported by employers, representatives of occupational groups (who
define occupational standards), and (where available) universities (and other service
providers), which design learning paths, even for learners who may not be enrolled.
In the best case, the didactic design of learning units takes into account the students’
practical experience, appreciating that times spent not learning may have a significant
impact on students’ learning behavior.
The recognition of learning units depends on the general structure of recognition
within the higher education landscape. For example, students can enter into a learning
agreement with a single institution, based on learning outcomes that combine various
learning units. However, it is also possible to combine learning achievements into an
academic degree and to have them recognized, if necessary with certain conditions.
In this way, people who cannot or do not want to make a long-term commitment
in advance, for family or professional reasons, as in the Tamagotchi model, can
nevertheless complete their university studies.
5At present, several models follow a similar approach. In Austria, for example, students can engage
in “irregular studies” (“Studium irregulare”). However, these studies must correspond to a diploma,
bachelor’s or master’s degree and “be equivalent to a relevant course of study;” see: https://www.uni.
at/studium/individuelle-studien/. In Great Britain it is possible to obtain a so-called “open degree”
from the Open University UK (Cooke, Lane, & Taylor, 2018).
34 3 Four Models of Higher Education in 2030
Case: DNB—Learning Culture as the Central Strategy of a Company
Relevant for the models: Lego, Transformer
Universities do not play a major role in the ambitious (further) education
strategy of DNB, Norway’s leading financial enterprise. In the past, DNB sent
a few hundred employees per year to bachelor’s degree programs at traditional
universities. Today, DNB has more than 9000 employees, who have constant
free access to a vast amount of digital-educational content. Most employees
decide for themselves what content they want to learn and how much time
they want to invest in training. Instead of investing a relatively large amount of
money in training a small number of employees, DNB uses digital technologies
to reach out to all employees with a wide range of educational opportunities.
DNB shows how the Lego model can be supported within a large business
enterprise. At the same time, DNB also provides employees with many years
of work experience and an introduction to a new type of higher education (the
Transformer model—see below).
Almost all aspects of traditional financial business are changing rapidly,
due to the use of digital technology. In future, sales staff will collaborate with
chatbots to advise customers. The customers will be better informed and able
to approach the company with clear ideas and wishes. To achieve this, employ-
ees must learn to use digital technologies for consulting and communication.
However, it is no longer enough to have a single learning phase in the course
of a whole life. Many fields of activity change continuously—faster than uni-
versities can develop suitable educational offers. Furthermore, DNB is not at
all interested in its employees being able to obtain new university degrees; it
wants them to be able to apply new competences and skills.
For DNB, learning is a strategic priority and part of its corporate culture.
Educational innovation begins with technology—a user-friendly mobile learn-
ing platform, on which all DNB-selected learning opportunities are accessi-
ble—strategically anchored within the organization. DNB’s Senior Vice Presi-
dent for Learning & Development meets with the management every six weeks
to present results and approve new projects. In addition, the firm encourages
workers to suggest new learning opportunities. Some meeting rooms can be
quickly converted into “lounges,” where employees can meet to learn together.
If such innovative openness is lived across all levels of an organization, a new
learning culture can develop. It is worth the effort since companies that are
constantly learning are better able to benefit from the digital transformation of
their industries.
The example of DNB shows that the increasingly narrow demarcation
between work and higher education is likely to become a major driving force
for change in the higher education system. DNB’s strategic focus on education
is still somewhat unusual. If Norway’s leading financial institution is success-
ful in its ambitious education strategy, however, other large companies can be
expected to follow. Traditional higher education can support these processes by
3.1 Brief Descriptions of the Learning Pathways 35
providing flexible programs (while closely observing and exploring the latest
developments in finance), but this requires more flexible educational provision
and a new and open relationship with the economy.
The Lego model closes gaps in the conventional range of training offered by
higher education institutions which, due to the dynamics of social change, are not
covered by traditional bachelor’s degree programs. The small-scale combination of
different courses makes it possible for learners to respond to short-term demands and
to acquire very individual qualifications. Although the DNB case (see Case: DNB)
shows how this can be achieved from an entrepreneurial perspective, learning within
the DNB system has not yet been recognized by the formal education system.
3.1.4 Transformer: Higher Education as an Opportunity
for Change
Transformer
The students in this model do not enter universities directly as school-leavers
but have already acquired their own professional identities and life experiences,
which they bring to their studies.
In this model, schooling and the initial phase of education (possibly including
higher education) have long since passed. Learners return to higher education either
to acquire new basic knowledge and skills (side-skilling) or to improve their level
of formal education (upskilling). They may be motivated by the need to prepare
for a career change or to acquire higher qualifications. In this model, learners study
relatively intensively over a period of three to five years and complete their tertiary
education with the expectation of returning to or re-entering the labor market. The
Transformer model enables individual learners to take advantage of opportunities to
adapt their knowledge and skills profiles.
The central idea of this model is that, in the future, everyone must have opportu-
nities to leave their chosen path in life and to change course. Options to participate
in higher education and educational aspirations should not be determined by age or
biography.
The didactic concept behind the Transformer model supports learning and per-
sonal development, through clearly defined steps and results. As learners begin higher
education many years after leaving the formal education system, they need consider-
able support. At the same time, these learners have acquired knowledge, skills, and
experience through their previous roles and can apply them to their learning. A careful
36 3 Four Models of Higher Education in 2030
balance is therefore needed between academic support, guidance, and independent
learning to achieve individual goals.
Universities are responsible for the control and coordination, and also the design,
of the study program. The didactics take into account the knowledge, competence,
and experience profiles of learners before they begin the course. However, credit for
or recognition of previous achievements is rarely provided. Once progress has been
made in this field, far shorter study courses should be possible. During the course of
their studies, students acquire increasing control over their own learning paths; after
an initial phase of the study, the proportion of self-regulated learning increases.
Learning takes place mainly on campus, with the support of global communication
networks, simulations, and augmented reality techniques, which extend the learning
environment through online experiences. Further learning spaces can be integrated
into the learning experience through internships, makerspaces, and fablabs. Com-
patibility with the demands of work-life is achieved, above all, by extending the
standard (maximum) period of study and by offering online course units.
Changes in the labor market represent an essential driver of the Transformer
model, as they make it necessary for learners to expand their competence and knowl-
edge profiles or to look for new fields of activity. Ultimately, this model offers a basic,
work-life-oriented course of study that meets the needs of an older target group; its
flexible study organization and didactic approach respond effectively to learners who
may not have experienced active learning practice for many years.
3.2 A Detailed Analysis of the Models of Higher Education
in 2030
The following section describes the models in more detail, exploring central aspects
identified in preliminary studies (see Sect. 2.1).
3.2.1 Environmental Requirements and Models
Tamagotchi corresponds to the current model of higher education. In relation to
individual learner biographies, it fits between the completion of secondary education
and the beginning of tertiary (higher) education. This model will still be relevant in
2030, primarily because of increasing demand for highly qualified employees. This
will continue a trend that has been observed since 1990. Members of our society have
an ever-higher level of education, with a graduate rate of over 40% in the OECD
average for people in the 25–34 age group in 2016 (see Fig. 3.2).6Higher education
remains a good investment for the state and for graduates who, among other things,
6Initial FiBS forecasts assume that the growth trend among first-year female students in Germany
will continue until 2030.
3.2 A Detailed Analysis of the Models of Higher Education in 2030 37
Fig. 3.2 Graduation rate in the 25–34 age group (selection of countries), 1990–2016. Source OECD
database, population with higher education, ISCED 2011 5-8. KOR South Korea, CAN Canada, GBR
United Kingdom, NOR Norway, NLD Netherlands, OAVG OECD average, GER Germany
earn better incomes and are less likely to become unemployed than nonacademics
(European Commission/EACEA/Eurydice, 2018; OECD, 2018a). This pattern is
likely to persist in a digitized world.
The Tamagotchi model focuses on providing basic knowledge and skills. If we
assume that this is the only way to achieve higher (academic) qualifications, courses
of study and programs must provide the knowledge and skills that learners need to
transition to most high-level professions. Since this function can only be fulfilled to a
limited extent if study programs change slowly, while the economy changes rapidly,
debates about the “qualification deficit” and the “employability” of graduates will
continue to challenge this form of higher education.
Economic developments and the interplay between economic dynamism in times
of digitization and demographic shifts toward older populations suggest that access
to higher education will need to be expanded. This model will not serve people who
wish or need to study later in life. This is not a new challenge for higher educa-
tion; it represents a line of action in the Bologna Process. However, the Tamagotchi
model has not yet found an effective solution (Orr & Mishra, 2015). This model is
therefore likely to cause further tensions, related to the question of whether university
38 3 Four Models of Higher Education in 2030
providers can afford to ignore societal expectations to offer extended learning oppor-
tunities. New and more innovative formats must focus on didactics, the commitment
of learners, and flexible learning paths (Unger & Zaussinger, 2018).
Although Jenga fills the same gap between secondary and tertiary education,
the Jenga model addresses a slightly different future problem. There is already a
trend toward academization in the health sector and education, among other fields.
This trend will become stronger in the future, as occupational profiles are created
in fields that correspond to intermediate or higher level qualifications. The Jenga
model addresses this problem by offering a shorter initial period of study, while
also considering “further education” from the perspective of the profession. In this
way, it enables a learner with a bachelor’s degree in nursing to acquire an additional
master’s degree in health management by completing study modules and blocks. The
demand for employees with higher levels of professional competence, in addition to
social and emotional competence, is sure to increase. Jenga provides a solution to the
qualification problem. Complete study programs can be designed to develop basic
levels of professional competence; learners can then acquire or enhance context-
related competences in a reflective way while working.
For industries that are already knowledge- and research-intensive, Jenga addresses
the growing need to constantly update knowledge and competences during periods
of professional activity. Continuing education takes place in close cooperation with
the Alma Mater and continues to have an academic character, which is characteristic
of this labor market field.
Lego responds to the small but important segment of the labor market that is pow-
erfully driven by innovation and new developments. Traditional courses of study are
too slow in this area; this model is demand-oriented and cross-disciplinary, allowing
learners to acquire knowledge and skills efficiently. This sector is likely to become
more important in the future. Through additive manufacturing (such as 3D printing),
it will soon be possible to design very lean and efficient production processes, which
will enable even small companies to compete effectively. Such companies, which
operate smaller and faster in the market, will value opportunities for their employees
to gain selective internal qualifications. In addition, the development of products
and services will place increasingly specific demands on knowledge and skills that
can no longer be provided by individuals but must be provided by teams of people
working together. Partial knowledge and competence gaps in such teams can be filled
selectively, using the Lego model. The same requirements also apply to freelancers,
who frequently work in virtual teams. In fact, some co-working spaces already offer
educational programs (Horn, 2018).
Transformer addresses two major developments. On the one hand, career changes
are becoming more frequent; on the other hand, demographic changes mean that older
citizens need new educational opportunities in order to keep pace with changes in
their roles and careers. Although Transformer is intended for learners who need
close didactic control and coordination during their studies, it recognizes that older
learners can draw on life and work experiences.
3.2 A Detailed Analysis of the Models of Higher Education in 2030 39
3.2.2 Didactic and Technological Features of the Models
The didactic starting points for Tamagotchi are a set of defined learning goals, which
shape the curriculum and are taught to students. The first phase of education and
the transition from school to university are important in this model. Future didactic
support for learning processes can be improved through digitization. In the future, the
selected teaching and learning methods will be evidence-based and congruent with
learning objectives, in line with constructive alignment. By closely monitoring the
learning process of the students, through many guidelines, handouts, and an optimal
orchestration of different methods, Tamagotchi reduces dropout rates and increases
success rates. The teaching is largely uniform and geared toward average students.
Learning as a specific competence has already been acquired in school.
This model builds on the learning style prevalent in schools. New educational tech-
nologies are mainly used to develop optimal teaching/learning processes. As a result,
digital media are added to regular teaching events, such as lectures, seminars, and
exercises. Online versions of the bridge offer support learners during the introductory
phase of their studies. Learning environments become the central control instrument.
Models that predict learning outcomes, developed using artificial intelligence, offer
improved adaptive learning experiences. However, the challenge remains to embed
such innovations in the existing and restrictive framework of university governance
and institutional culture.
From a didactic point of view, Jenga has two phases. The first phase is similar to the
Tamagotchi model, although it focuses more on the transition between education and
profession. In the second phase, learners search actively for offers, after successfully
completing courses of study that have met their needs, both in terms of content
and time flexibility. Higher education providers can thus build on a foundation of
knowledge in the second phase, while also relying on the learning style learned
during the first degree.
In contrast to the first learning phase, second-phase learning content is provided
through differentiated and specialized modules, which become increasingly frag-
mented. However, the type of learning undertaken is based on Phase 1 of this
model.
Tension arises in this model when higher education institutions want to attract their
own alumni into Phase 2 studies (as with the MIT MicroMasters), but must respond
to their former students’ individual needs by providing knowledge and competence
levels and more flexible forms of provision. While Tamagotchi offers a foundation
of knowledge and competence, here learners want knowledge that is relevant to their
current activities. Didactic offers from nonuniversities are likely to be competitive
or even to have an advantage over those of established universities.
It seems logical that Phase 2 educational offers should move into virtual space,
given the changed framework conditions of former students, who now have jobs and
families. In these learning phases, attendance times must be reduced or even bundled.
As a result, technical requirements will increase. Institutions must prepare teach-
ing and learning content for the virtual space, providing systems that enable online
40 3 Four Models of Higher Education in 2030
learning phases and opportunities. Webinars, interactive videos, and virtual reality
scenarios will be as commonplace as opportunities to virtually book, consume, and
conclude these offers. Didactically, this model will open up new scenarios. Virtual
tutoring and peer support will become far more important. This model will also
require completely new organizational measures, to cope with digital certificates,
digital payment systems, and a completely digital student-administration framework.
The predominant didactic principle in the Lego model is self-regulated learning.
Learners actively seek offers that meet their needs, both in terms of content and
methodology. Learning content is offered through more differentiated, specialized,
and fragmented modules.
The predominant didactic principle in this model is each student’s own identity
and sense of “self.” Students choose their own learning paths and compile individual
curricula that reflect their own needs. As the research findings of distance-learning
and continuing education studies have shown, time and time again, this model is a
didactic prerequisite, since learners must have an established learning competence,
as well as a willingness to learn.
At the same time, this model poses a challenge by relegating higher education
institutions to the background. Educational providers are, above all, providers of
individualized and individualizable learning spaces; they are also educational con-
sultants. Digital tools will help students choose and organize their studies, and mon-
itor their learning performance. This places the methods of learning analytics in
the foreground. Digital platforms offer opportunities for national and international
networking and exchanges with other students.
Certificates and digital proofs of competence (such as open badges) provide impor-
tant documentation of learning performance (Orr & Buchem, 2019). The desire for
security may foster the use of institution-independent storage locations, such as
blockchain technology, for storing documents (Grech & Camilleri, 2017).
In the Transformer model, students have a wide range of prior knowledge that
they can apply to their studies, and for which they may want recognition and appre-
ciation. At the same time, the experience of learning in formal contexts is a distant
memory; in most cases, this type of learning practice is no longer available. This
model must, therefore, create a uniform ability to study, while also taking greater
account of individual learning interests. It makes sense to engage participants in
helping to define their own learning goals, adapted from the group. The exchange
of and reflection on other experiences and backgrounds play an important role in
this model. Individual lessons are tailored less to individual needs than to the inter-
ests of the group. The didactics must find a suitable balance between control and
self-responsibility.
Since the content can be adapted to suit the current group of students, this model
places the highest demands on the didactic competence of teachers. The Trans-
former model must also accommodate changes in learning by providing multifunc-
tional learning spaces. Such spaces can be used flexibly, as traditional lecture halls,
workshops, and group workrooms.
Given the average age of students, a high proportion of part-time learners can be
expected. Attendance times must, therefore, be shorter than those in the Tamagotchi
3.2 A Detailed Analysis of the Models of Higher Education in 2030 41
model. In addressing technical challenges, a combination of the Tamagotchi and
Lego models is likely to be formative.
Table 3.1 provides an overview of the didactic and technological features of the
models.
Table 3.1 Differences in the didactic and technological aspects of the four models
Differentiation
criteria
Tamagotchi Jenga Lego Transformer
Instructional
design
Provided by the
teacher
Provided by the
teacher
Self-organized Mixed, adapted
to students but
designed by
teachers
Orientation of
teaching content
Designed for the
average student
Highly
individual, but
with a uniform
starting point
Highly
individual, with
no uniform
starting point
Collective,
teaching content
is adapted to a
specific group of
students
Student/teacher
ratio
Students expect
teachers to set
and control the
learning process
Students still
expect
significant input
from teachers,
but more in their
professional role
as experts than
as classroom
teachers.
Students have
greater personal
responsibility
for learning
Students control
the learning
process
themselves and
seek help from
teachers when
they feel it is
necessary
Initially, the role
of the teacher in
the learning
process is
stronger; later
the teacher is
more important
as an expert
Student group Homogeneous Heterogeneous Extremely
heterogeneous
Extremely
heterogeneous
Technology Enrichment in
the classroom,
educational
data-mining,
learning
analytics for
evidence-based
learning
Enrichment
model with 1:1
mirroring into
the virtual world
Highly digitized Hybrid form,
high demands
on
multifunctional
learning spaces
Digital learning
scenarios
(according to
Wannemacher
2016)
Enrichment,
game, and
simulation
Integration,
interaction, and
collaboration,
self-study,
online learning
Personalization,
self-study,
online learning,
open
educational
practice
Interaction and
collaboration,
open
educational
practice
Source Own illustration
42 3 Four Models of Higher Education in 2030
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate
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The images or other third party material in this chapter are included in the chapter’s Creative
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the copyright holder.
Chapter 4
Outlook on a New University Landscape
in 2030
Abstract Reflecting the change in perspective taken in this book, our survey put
questions about institutional support, governance, quality assurance or financial
issues aside. Moreover, digitization is not only a technical innovation but always
a social one as well. This fundamental change of perspective leads to questions such
as “What does the learner need?” that universities will have to face in the future.
Within the survey, international experts were requested to assess the quantitative
success of the different learning pathways, distributing current and future students
among the four models. Unsurprisingly, the “new” learning paths were expected to
become more important, although the actual prospective importance of these learn-
ing paths will depend on the supply and demand for academic studies, allowing
decision-makers to rethink the educational designs based on the AHEAD modeling.
4.1 A New Focus on Learning Pathways in the Era
of Digitization
To Lego: “I think it is an interesting, innovative, and sustainable model! How-
ever, the German university landscape is neither prepared nor positioned for
this in the short nor medium term.”
On the whole approach: “We need more and more of such approaches in
higher education.
Thinking outside of the existing, firm, and relatively standardized systems.”
Source: anonymous quotes from the international survey.
The four learning paths of the AHEAD study were developed during the sum-
mer of 2018; they have subsequently been discussed, in various discussion groups,
by university representatives, students, political decision-makers, and business peo-
ple. The vision of a higher education landscape designed around the learner spurred
© FiBS - Forschungsinstitut für Bildungs- und Sozialökonomie, and HIS-Institut
für Hochschulentwicklung e.V. (HIS-HE) 2020
D. Orr et al., Higher Education Landscape 2030,
SpringerBriefs in Education, https://doi.org/10.1007/978-3- 030-44897-4_4
43
44 4 Outlook on a New University Landscape in 2030
discussions in a very constructive way. Questions about institutional support, gov-
ernance, and quality assurance, as well as the institutional financing required for
restructuring and infrastructure, would normally shape any debate about the future
of higher education or higher education institutions. In this case, these topics were
moved to second place, reflecting the change in perspective.
Questions of digitization also benefit from a change of perspective. The cur-
rent debate on digitization in social processes aims to shift from a technology-first
approach, which originates with technology and then searches for applications, to
the view that digitization is always a social innovation. Daniel Buhr emphasizes
this distinction in his position paper “Social Innovation Policy for Industry 4.0” (cf.
Andersson et al., 2016; Buhr, 2015):
Social innovations have a decisive influence on whether a technical invention becomes a
widespread innovation (according to Schumpeter), along which paths and channels it spreads
(diffuses), and what effect it unfolds in the process. A social innovation is a targeted recon-
figuration of social practices with the aim of better solving or satisfying problems or needs
than is possible on the basis of established practices, and thus contributing to social progress.
The Vice-Presidents of IT systems and the so-called CIOs (Chief Information Offi-
cers) of universities now emphasize this changed perspective: “When implementing
digitization, we must focus on the users and no longer on departmental silos. What
does the individual need?”—says Hans Pongratz from TU Munich (Kaufmann, 2019,
p. 6f.). The present study goes one step further, asking: What does the learner need?
The practical examples detailed above (Minerva, 42, MIT MicroMasters, and
DNB) show how technology can be fully embedded in educational initiatives. They
provide examples of a strategic approach that is not additive and does not attempt to
embed technology in old structures, without significant reforms.
4.2 The Future Relevance of Learning Pathways
for the Higher Education Landscape of 2030
In the various expert interviews, participants were asked what proportion of students
was following these learning paths today, as well as will be in 2030. There were major
differences of opinion in each group, but the experts showed particular interest in the
Jenga and Lego models, which many saw as offering future promise. At the same
time, many people commented that the classical model of higher education (in this
case, Tamagotchi) would change considerably during the next decade.
The survey of international experts carried out between November 2018 and Jan-
uary 2019 also asked participants how to assess the quantitative significance of var-
ious learning pathways. Experts and stakeholders were asked to distribute current
students and the future students of 2030 among the four learning paths.1Figure 4.1
shows the trend of responses. These initial assessments are not surprising. They
1Unfortunately, few survey participants were willing to answer this question. Obviously, many
people found it difficult to allocate students to different learning paths, in accordance with this new
4.2 The Future Relevance of Learning Pathways for the Higher … 45
52
10
3 3
82
25
17
12
-10
10
30
50
70
90
110
Tamagotchi Jenga Lego Tr ansformer
32
7 6
10
60
25 22
30
-10
10
30
50
70
90
110
Tamagotchi Jenga Lego Transformer
Fig. 4.1 Assessments on the current and future significance of the four learning paths. Source Own
illustration
show that all three of the “newer” learning paths (Jenga, Lego, and Transformer)
are expected to become more important, while Tamagotchi will lose some of its
relevance.
However, the actual importance of each learning path will depend on both supply
and demand. This means that political decision-makers, university authorities, and a
range of other educational institutions should rethink the design of their educational
offers and accompanying services, on the basis of AHEAD modeling. In this way,
they can develop new strategies. Of course, educational institutions will be free to
perspective. More than 800 people visited the survey website and 28 people completed the survey,
but only 14 were willing to answer this particular question. Their assessments are nevertheless
important for explorative studies such as this one. In addition, these subjective assessments roughly
reflect the opinions that project team members expressed during personal discussions.
46 4 Outlook on a New University Landscape in 2030
provide a range of different learning paths.2For this purpose, the AHEAD learning
paths offer initial goal-orientation patterns.
Over the past 20 years, the new public management model has become the focus
of higher education policy measures and strategies (Jongbloed, 2015). The processes
used to make courses more flexible and individualized have been partly restricted
by this focus (Henderikx & Jansen, 2018, p. 78 ff; Orr & Usher, 2018). At this
point, it may be helpful to consider the United Nations’ educational goal 4.3 as a
touchstone for higher education in Germany and Europe: “To ensure equal access for
all women and men to affordable and high-quality technical, vocational, and tertiary
education, including university education, by 2030” (United Nations, 2015) and to
use digitization to effectively achieve this goal.
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate
credit to the original author(s) and the source, provide a link to the Creative Commons license and
indicate if changes were made.
The images or other third party material in this chapter are included in the chapter’s Creative
Commons license, unless indicated otherwise in a credit line to the material. If material is not
included in the chapter’s Creative Commons license and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder.
2For example, Hamdan Bin Mohammed Smart University (HBMSU), investigated in the OOFAT
study, concentrates on four groups of students to provide orientation values for study program
planning: casual learners, concentrated learners, committed learners, and continuing learners (Orr,
Weller, et al., 2018).
Appendix
Methodological Notes
Cooperation with the Advisory Board
The Advisory Board1consisted of the following seven members:
South Korea, Ki-Sang Song, Director of the Institute “Computers in Education,
Korea National University of Education.
Prof. Song is a member of UNESCO’s expert group on educational technology
and, with the support of the Korean research community, is conducting a project
on learning analysis in education.
Norway, Elisabeth Hovdhaugen, Senior Researcher at the Nordic Institute for
Studies in Innovation, Research, and Education.
Dr. Hovdhaugen is an international expert in higher education and participates in
the Norwegian Research Council funded project “BRAIN - Barriers and Drivers
in Adult Education in Acquiring Competences and Innovative Activities.”
Netherlands, Fred de Vries, Program Director Digital Education at Saxion
University of Applied Science.
Dr. de Vries is also involved in supporting the development of strategies for the
digital university and the European project “Higher Education Online - MOOCs
the European Way.”
USA and Canada, Alex Usher, President of Higher Education Strategy Asso-
ciates.
Alex Usher is an expert in the development of higher education in Canada and
the USA and a founding member of the Academic Ranking and Excellence of the
IREG International Observatory.
1External link: https://ahead.tugraz.at/en/our-team/.
© FiBS - Forschungsinstitut für Bildungs- und Sozialökonomie, and HIS-Institut
für Hochschulentwicklung e.V. (HIS-HE) 2020
D. Orr et al., Higher Education Landscape 2030,
SpringerBriefs in Education, https://doi.org/10.1007/978-3-030-44897-4
47
48 Appendix
Great Britain, Prof. Martin Weller, Professor of Educational Technology, Teach-
ing and Learning Innovation and Director of the Open Education Research Hub
at the Open University, UK.
Prof. Weller has recently become a Principal Fellow of the Higher Educa-
tion Academy in Great Britain, which thereby honors his outstanding teaching
activities.
World Bank, Nina Arnhold, Senior Education Specialist, World Bank.
The focus of Dr. Arnhold is on Europe and Central Asia with topics such
as competence requirements for higher education in the context of university
administration, strategy development, and digitization.
Germany, Prof. Ingo Rollwagen, Professor of Management in the Creative and
Science Industry at the Fresenius University of Applied Sciences.
Dr. Rollwagen previously worked as an expert for Corporate Foresight, Tech-
nology and Education for Deutsche Bank Research and the Alfred Herrhausen
Gesellschaft, Deutsche Bank’s international forum.
In the course of the study, the members were advised via video-conferencing
about the task of the study and the fine-tuning of the content.
Following the development of the four learning paths, a two-day workshop with
the experts was held in Berlin on October 15/16, 2018. The aim of the workshop
was the validation and further elaboration of the concept. The meeting was very
fruitful. The advisors found the four learning paths good and supported us in further
elaboration.
Appendix 49
“Fragmented Worlds” Event
The “Fragmented Worlds” event2took place as part of the “Shaping the Digital
Turn”3topic week of the “Hochschulforum Digitalisierung” on September 26, 2018,
and was very well attended. We had over 150 registrations and sent 110 confirmations
(40 people on the waiting list). About 70 people took part. At the event, we introduced
the topic and then presented the four models in small groups.
2External link (in German): https://hochschulforumdigitalisierung.de/de/zersplitterte-welten-
projektvorstellung-ahead.
3See: https://hochschulforumdigitalisierung.de/de/zersplitterte-welten-projektvorstellung-ahead
(last viewed 08.05.2019).
50 Appendix
The participants had the opportunity to listen to short “Lightning Talks” on the
respective models of the AHEAD study and to discuss them with a team member. In
these group discussions, it was also asked to indicate the proportion of students that
the participants expect for the respective model in 2030. In each group, there was a
dispersion, but the tendency was toward Jenga and Lego—see next page.
Appendix 51
The pictures above (Source: own photographs) show a survey conducted with
participants of the group discussions. The participants were asked to estimate the
demand each of the four different models will generate in the future, ranging from
52 Appendix
low (“wenig”) to high (“viel”). While “Tamagotchi” and “Transformer” lean more
toward low demand, “Lego” and particularly “Jenga” are estimated by the participants
to be the models in high demand in the future.
Background Information on the Anonymous International
Survey
An anonymous international survey in English and German was launched on
November 24, 2018. An international social media campaign accompanied the
launch.
The survey presented the respective models in some paragraphs. Participants were
asked for their assessment of their suitability for certain student groups (free text).
Subsequently, the participants were asked to distribute all students currently and in
the year 2030 among the four learning paths.
The perception of the survey was relatively high. About 800 people have visited
the survey home page. However, the answers to the questions with 28 completed
questionnaires were significantly lower (including in the English [German] version:
542 [43] visits to the introductory page, 63 [27] aborts of the survey, 18 [10] complete
participants). These persons came from China, Colombia, Germany (12 persons), the
Netherlands, South Korea, Norway, Scotland (UK), Slovenia, and the USA.
Students took part in the German survey and international university experts
including a former Minister of Science took part in the English survey.
Detailed Descriptions of Developments in Individual Areas
(Background Studies)
A1 Literature Analysis on Higher Education and its Future
Authors: Katrin Schulze, Dominic Orr
Please follow this link (in German):
https://cloud.tugraz.at/index.php/s/A9RPHWeFHFJaoHg
A2 Knowledge and Competence Requirements Within a Digital
Society
Author: Dominic Orr
Please follow this link (in German):
https://cloud.tugraz.at/index.php/s/Tq7ZL8L3cRqbZkJ
Appendix 53
A3 University Teaching Challenges Within a Digital Society
Authors: Klaus Wannemacher, Maren Luebcke
Please follow this link (in German):
https://cloud.tugraz.at/index.php/s/fx8ALPYRF9DMCfR
A4.1 Technological Requirements for Higher Education
Authors: Markus Ebner, Martin Ebner
Please follow this link (in German):
https://cloud.tugraz.at/index.php/s/TPW9476aYM5SAR2
A4.2 Digital Technology—The Outward View
Author: J. Philipp Schmidt
Please follow this link (in German):
https://cloud.tugraz.at/index.php/s/824o9qigcyzNDJq
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Chapter
The development of the higher education system is the subject of a wide range of prognostic and exploratory studies that provide an outlook on potential future lines of development for the higher education sector. A dozen of those international future and foresight studies and trend reports on a future state of higher education covering a time corridor until about 2060 were identified and evaluated by means of literature research. However, the very dynamic change forced upon higher education institutions that all at once had to use digital technologies and to organise lessons exclusively online due to the COVID-19 pandemic since 2020 could not be anticipated in those foresight studies. It was therefore assumed that the future scenarios for higher education needed to be reassessed in light of the significant changes having occurred in higher education teaching due to the pandemic. To that end, a selection of international and national surveys on the impact of the pandemic on universities was used to operationalise the changes in higher education teaching in the course of the pandemic. Based on an overview of all the data material, a category-based contrastive analysis a) of a selection of foresight studies and b) of COVID-19-related surveys was carried out. Subsequently, the results of the respective analyses were confronted with each other and conclusions drawn which of the developments predicted in the foresight studies have become significantly more likely as a result of the pandemic-related transformation of teaching and learning emerging since 2020. The results should provide decision-makers in higher education and science policy, university management, deans’ offices, administrations or central institutions with a better understanding of potential medium- and long-term development trends in higher education teaching and learning. KeywordsForesight studiesHigher educationTechnology-enhanced learning
Article
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Asynchronous online courses are popular because they offer benefits to both students and instructors. Students benefit from the convenience, flexibility, affordability, freedom of geography, and access to information. Instructors and institutions benefit by having a broad geographical reach, scalability, and cost-savings of no physical classroom. A challenge with asynchronous online courses is providing students with engaging, collaborative and interactive experiences. Here, we describe how an online poster symposium can be used as a unique educational experience and assessment tool in a large-enrollment (e.g., 500 students), asynchronous, natural science, general education (GE) course. The course, Introduction to Environmental Science (ENR2100), was delivered using distance education (DE) technology over a 15-week semester. In ENR2100 students learn a variety of topics including freshwater resources, surface water, aquifers, groundwater hydrology, ecohydrology, coastal and ocean circulation, drinking water, water purification, wastewater treatment, irrigation, urban and agricultural runoff, sediment and contaminant transport, water cycle, water policy, water pollution, and water quality. Here we present a is a long-term study that takes place from 2017 to 2022 (before and after COVID-19) and involved 5,625 students over 8 semesters. Scaffolding was used to break up the poster project into smaller, more manageable assignments, which students completed throughout the semester. Instructions, examples, how-to videos, book chapters and rubrics were used to accommodate Students’ different levels of knowledge. Poster assignments were designed to teach students how to find and critically evaluate sources of information, recognize the changing nature of scientific knowledge, methods, models and tools, understand the application of scientific data and technological developments, and evaluate the social and ethical implications of natural science discoveries. At the end of the semester students participated in an asynchronous online poster symposium. Each student delivered a 5-min poster presentation using an online learning management system and completed peer reviews of their classmates’ posters using a rubric. This poster project met the learning objectives of our natural science, general education course and taught students important written, visual and verbal communication skills. Students were surveyed to determine, which parts of the course were most effective for instruction and learning. Students ranked poster assignments first, followed closely by lectures videos. Approximately 87% of students were confident that they could produce a scientific poster in the future and 80% of students recommended virtual poster symposiums for online courses.
Article
University continuing and distance education is at a crossroads. This article asserts that a radical disruption in the competitive landscape for university-based continuing education (CE) is on the immediate horizon. University CE is threatened by external trends beyond its control and will likely not survive in its present form, or survive at all, without adapting to the changing marketplace. This disruption is occurring at the same time and is partially due to the increasing university demands on CE units to produce more income in part by scaling new markets and increasing customers in new ways. The signals and drivers of that change will be detailed, followed by a description of the competitive landscape that university CE will soon face. Universities will experience barriers in combating this competition, which, if not overcome, will spell the decline of many CE units and disruptions will also manifest themselves into major problems for the university as a whole. This article concludes with recommendations for CE and university leaders that may allow them to thrive and prosper as a part of that dynamic landscape.
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Higher educational institutes today need to focus on identifying the requirements of industry as well as the market, so that they can help students develop the necessary skills and enable them to work with intelligent machines in today's era of the 4th industrial revolution which is also termed digitalization. Digitalization has increased pressure on educational institutions to update their existing curricula and course contents. It is important to note that, while industry as well as educational institutions in the developed world are rather quick on embracing such trends, developing economies often lag behind. Universities in developed countries are mostly on the path towards a hybrid way of teaching, while those in developing countries, such as Pakistan, frequently struggle to make these changes. This chapter seeks to provide suggestions and recommendations for the higher education sector, including universities and policymakers. It identifies the role that the higher education sector must play in preparing and upskilling future employees for Pakistan's digital future.
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The COVID-19 pandemic has presented an opportunity to rethink higher education. This study focused on analysing experiences from three higher education institutions (HEIs) in the United Arab Emirates (UAE) since the onset of the crisis and explored how university leaders and professors in these institutions imagine post-COVID-19 higher education. The study aimed to find out whether the pandemic has been a factor that has helped to legitimize online teaching and learning as a universal mode of delivery across different fields of studies, or if the Zoom fatigue has shown its limitations. In addition, the research investigated what transformations university experts predict and their vision for the future of higher education. The study found that many lessons learnt during the period of forced adoption of distance education will be used by universities to enhance and expand online learning provisions. This shift will be driven by the investments the universities have made in distance education and the increased familiarity of the students, staff and institutions with e-learning. The study participants foresee that more sophisticated forms of hybrid campuses will be a more appropriate model for the future, if face-to-face (F2F) classrooms do not return.
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A transformação digital é um tipo de mudança de larga escala, algo que a pandemia deve acelerar nas Instituições de Educação Superior (IES), cujas causas já eram antevistas e estudadas nas últimas décadas. Os sistemas educacionais permanecerão expostos e vulneráveis, se pensamos em simplesmente reverter ao que fazíamos antes da crise do campus vazio ou se nos basearmos diretamente no que estamos emergencialmente praticando. Nesse sentido, faz-se necessário que as IES se alinhem de forma crítica e construtiva às demandas da Sociedade de Informação em termos do ensino, da pesquisa e da extensão. Caso assim não procedam correm o risco de tornarem-se irrelevantes.
Article
Las dinámicas del mundo complejo que enfrentamos necesitan de la educación como pieza clave para el desarrollo de las personas, sociedades del presente y del futuro. El presente documento recoge las principales consideraciones de algunos actores globales sobre cómo debe ser la educación del futuro. El objetivo es mostrar tendencias a nivel mundial, luego del fenómeno que vive la humanidad.
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Reflecting the change in perspective taken in this book, our survey put questions about institutional support, governance, quality assurance or financial issues aside. Moreover, digitization is not only a technical innovation but always a social one as well. This fundamental change of perspective leads to questions such as “What does the learner need?” that universities will have to face in the future. Within the survey, international experts were requested to assess the quantitative success of the different learning pathways, distributing current and future students among the four models. Unsurprisingly, the “new” learning paths were expected to become more important, although the actual prospective importance of these learning paths will depend on the supply and demand for academic studies, allowing decision-makers to rethink the educational designs based on the AHEAD modeling.
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As the digital transformation clearly highlights the role of universities and institutes of higher education in shaping a higher education system that is more open and provides education to everyone who can benefit from it, this study seeks to analyze, in more detail, what developments are having an impact on higher education and develops future scenarios for education in 2030. The UK study Solving future skills challenges implies that the linear model of education–employment–career will no longer be sufficient in the future, requiring new combinations of skills, experience, and collaboration from educators and employers. This UK study serves as a starting point for the AHEAD trend analysis for a higher education l