Future Skills Delphi 2019
How will higher education institutions have to position in order to prepare future graduates for the changing society and future work place? The Future Skills Report 2019 is based on a number of prior research studies on Future Skills – future learning and future higher education. It presents validated concept and elaborates a model of Future Skills, data on future learning and consolidated scenarios for future higher education.
With fundamental changes in the job market and challenges in our societies due to a global and technological drivers, research on Future Skills becomes increasingly relevant. However, many studies fall short on capturing the effects which technological advancements and global cooperation have today and will have in the future on higher education systems, skill development demands and labour market changes. They often reduce Future Skills directly to digital skills, which – as important as they are – only represent one side of the Future Skill coin.
The results presented from this Delphi survey are taking a broader view and go beyond digital skill demands. The approach elaborates on an experts’ informed vision of future higher education (HE), taking into account the demand for Future Skills, outlines the four signposts of change which will shape the learning revolution in higher education and presents a first model of Future Skills for future graduates.
Emergence is like the stage on which the development of organisations, processes and social coexistence in modern societies takes place. It is, so to speak, the key to understanding systems and their properties. Emergence provides information on whether and on which rules self-organisation is based in social systems. If processes are no longer predetermined or rule-based, the question arises whether there are other than the acknowledged regularities that make it possible to foresee and understand developments. Emergence as a concept provides the basis for this.
The point is that emergent properties of a system cannot – or at least not obviously – be traced back to the isolated properties of the individual elements of the system. For example, in the field of brain research and in the philosophy of the mind, some scientists hold the opinion that consciousness is an emergent characteristic of the brain (Stephan 2016).
Stephan (ibid.) explains that emergent phenomena are described in physics, chemistry, biology, mathematics, psychology or sociology. Thus, emergence theorists would clearly deny that a full description of the world is possible solely on the basis of knowledge of the elementary particles and general physical laws. However, the recognition of emergent phenomena does not have to lead to a renunciation of scientific explanation. On the contrary, the developments in synergetics, systems theory and chaos research show that emergent-related phenomena such as self-organisation and their formation conditions are accessible to systematic and objectively comprehensible explanations (see also Greve & Schnabel 2011). However, due to a hierarchical derivation from universal laws, the unity of science is replaced by a transdisciplinary dialogue whose aim is to compare analogous structures of complex systems on different emergence levels. In most cases, emergence occurs on the basis of spontaneous self-organisation. The term Emergence describes the appearance of system states that cannot be explained by the properties of the system elements involved. In a sense, at higher levels, newly emerging qualities derive from previous conditions. It should be noted that the newly emerging qualities should not to already exist but have to occur for the first time. It is commonly expressed as follows: The whole is more than the sum of its parts. The concept of emergence stands for this more and its genesis.
The phenomenon of emergence can be illustrated by the example of temperature. If you look at a single chemical molecule, such as the water molecule, then you cannot determine a temperature for that molecule. However, if you have a large amount of these single molecules, then it is possible to determine a temperature. Temperature only occurs when many molecules collide, so temperature can be seen as an emergent property of many molecules. Thus, the temperature of the water is an emergent property of the water molecules.
According to Stephan (ibid., also Stein 2004), emergence describes a specific transformation process between two system states in systemic terms. If a system has the current system state A and this system is transferred to a new system state B, a transformation from system state A to system state B takes place. The transformation is the result of a transformation process. The transformation process is called emergent if the system state B does not result directly from system state A and its particles or subsystems (Stein 2004). This consideration of emergence in the context of a transformation process also contributes to the scientific clarification of the concept. It can now be asked which transformation rules actually work. If no transformation rules are recognisable or known, one would no longer speak of emergence. During the transformation process, new qualities emerge which cannot be attributed to the summation of the individual properties.
This raises the question of whether the emergence phenomenon can be reduced to simple transformation rules at all. Emergence focuses on two principles:
- Principle 1 – Irreducibility: the new state of a system cannot be (historically) linearly reduced back to the old state but represents a qualitatively new state.
- Principle 2 – Unpredictability: neither in terms of time nor content the transformation of the new system can be predicted.
In the following, the transformation process will be discussed further. How does it take place – which explanatory models for the transformation exist, which rules work and are there systematics recognisable? We will address these questions in detail below. The centre of the transformation process is the phenomenon of self-organisation, which plays the essential role in explaining the emergence phenomenon.
Modern self-organisation theories come from physics and biology and increasingly permeate scientific thinking. They form the basis for the emergence of new needs in the labour market, which we call Future Skills in this book. We won’t fully introduce the large areas of emergence, self-organisation, synergetics and more or less radical constructivism. Instead, we will concentrate on a few limited examples from the fields of synergetics, the ecosystem approach, media theory and autopoiesis.
The Delphi resulted into hallmark indications on the shift from academic education and teaching to active learning of choice and autonomy. Higher education institutions in the future will provide a learning experience which is fundamentally different than the model of today. Timeframe for the time of adoption vary but for many aspects a close or mid-term timeframe has been estimated through the Delphi experts.
The dimensions of future learning in higher education will comprise (1) structural aspects, i.e. academic learning as episodical process between biographical phases professional and private episodes throughout life, learning as institutional patchwork instead of the current widest-spread one-institution-model of today, supported through more elaborated credit transfer structures, micro-qualifications and microcredentials, as well as aspect of (2) pedagogical design of academic learning, i.e. changing practices of assessment, also peer-validation, learning communities, focus on future skills with knowledge playing an enabling role in interactive socio-constructive learning environments).In general experts estimate structure changes to become relevant much later than changes related to academic learning design.
Drivers for Change in Higher Education
Four key drivers in the higher education market can be described. Each driver has a radical change potential for higher education institutions and together they mutually influence each other and span the room in which higher education likely will develop.
There are 2 content and curriculum related drivers (i.e. (1) personalized higher education and (2) future skill focus) and 2 organization-structure related drivers (i.e. (1) multi-institutional study pathways, (2) Lifelong Higher Learning)
Four Scenarios for Future Higher Education
Three out of four scenarios score with a time of adoption of more than 10 years from today with the majority experts. Only the lifelong higher learning scenario scored for a time for adoption within the next 5 years with the majority of experts.
1 – The ‘future skill’ university: The ‘future skill’ scenario suggests that higher education institutions would leave the current model that focusses on knowledge acquisition. Instead, new profiles would be developed that emphasize graduates’ future skill development. In this scenario, HE would mainly be organized around one key objective: to enable the development of graduates’ future skills, i.e. complex problem solving, dealing with uncertainty or developing a sense of responsibility, etc. This would not replace but go beyond the current emphasis of knowledge acquisition and studying based on defined curricula for fixed professions.
2 – The networked, university: This scenario views higher education as a networked study experience. It will not be down to a single institution providing a student with a certain program, but that this role would be split among multiple institutions. This means that ‘digital import’ and ‘digital export’ of parts of the curriculum would play a significant role. The standard HE study structure and experience would shift from a “one-institution” model to a “multi-institutional” model.
3 – The “My-University” scenario: This scenario describes HEIs as spaces where the elements of choices enlarge, and students can build their own curricula based on their personal interests. The curriculum of academic programs in this scenario would move from a fully predefined and ‘up-front’ given structure to a more flexible, personalized and participatory model in which students actively cooperate with professors/ teachers/ advisors in curriculum building of HE programs.
4 – The lifelong higher learning scenario: In this scenario, seamless lifelong higher learning would be as important as initial higher education. Learners in the workplace would be the main type of student, choosing their portfolio of modules according to their personal skill needs and competence demands with high autonomy throughout their lifetime. Institutions thus would offer micro-credentials, which students assemble individually based on their own interests. Recognition of prior study achievements and practical experience would enable permeable shifting between different providers, which offer to bundle prior learning experience into larger certifications.