Schumann, C.-A., Kling, N.
Hybridity as result of holistic thinking
Hybridisation has increasingly become the focus of education thanks to Pandemic, even though we have been living and learning in hybrid systems since time immemorial. Hybridity was used in the 19th century to describe the differences between various ethnic groups. Today, an intercultural process in which an individual’s identity undergoes a reorientation is called hybridity. In this respect, there are interesting parallels with hybridity in education today, as the identity of origin and that of a new environment, increasingly common in the globalisation and internationalisation of learning, lead to new, combined identities of the individuals going through this process (Gugenberger, 2010).
In times of the pandemic, hybridisation has been an appropriate solution to mitigate the abrupt and forced transition from face-to-face teaching to online forms of knowledge acquisition, especially for inexperienced users of e-learning. In the current discussion on hybrid learning with times of pandemic and afterwards, the focus is mainly on the interplay between virtual and face-to-face learning. Depending on the perspective and the field of study, other interrelationships such as print and digital media, form of cooperation, networking of learners, degree of mechanisation of learning, etc. are included in the analysis, development, and implementation of new theoretical and practical approaches. It is often overlooked that interesting theoretical and practical approaches to hybrid and holistic thinking have existed for many years, the use and further development of which can be helpful in optimising educational formats in the current phase of expanding and stabilising hybrid learning.
Already in 2012, a series of articles in the journal “Hybrid Pedagogy” reported on the interplay of virtuality and empiricism, the subject matter and impact of Hybrid Pedagogy. The listing of numerous intersections already made clear at that time how complex hybrid models in pedagogy can be in the context of the further development of promising education systems. It was already apparent that digitalisation will have a decisive influence on the further design of learning and lead to fundamental transformations in education (Stommel, 2012) The foundations for this development were already created in the eighties and nineties of the last century with the mass introduction of computer workstations in all areas of learning, work, and life as well as multimedia applications in networked systems.
Multidimensional approach to hybrid learning
Theoretical and practical models of hybrid education have existed for years (Olapiriyaku & Scher, 2006). Hybrid approaches assume a change in the relationship between two combined states, whereby the change in state can be discrete, i.e., in fixed stages, or continuous. Due to the multitude of influencing factors and their various continuous or discrete characteristics, hybrid approaches are becoming increasingly important. To conceptualise the combination of two states, the term dimension was introduced (Kraidy, 2002). Multiple dimensions are combined to successfully solve complex teaching and learning scenarios for knowledge transfer.
The degree of multidimensionality is constantly growing. The influences are manifold. They range from pedagogical, ethical, methodological, didactic, digital, technical, and scientific views to content-related issues that education must deal with, which includes sustainability, for example. In its programme on education for sustainable development, UNESCO lists the learning goals for achieving the 17 Sustainable Development Goals in unity of social, economic, and ecological aspects, which should lead to transformative action, structural changes, and technological developments in education by 2030 (UNESCO, 2017). This fact alone includes a huge potential of dimensions to be combined and shows the high complexity of the task.
To be able to understand, model and optimise this diversity of dimensions and states in the future, the theory of hybrid systems is used. This involves the concepts of controllability, stability, and observability as well as the attainability of any state starting from a certain initial state and the controllability of any state into the final state. Based on these theoretical foundations, specific theories for multidimensional hybrid models in education are to be developed. Methodical preliminary work using mathematical procedures such as fuzzy logic as well as hybrid systems and automata in the field of education is under development (Nayaka et al., 2018; Schumann et al., 2021).
There are already many forms in the areas of instructional materials, learning planning, type of learning, etc. that can adopt different hybrid states for the benefit of learners or can be adapted accordingly. Complex investigations lead to hybrid systems in education, e.g., hybrid learning configurations, hybridity in learning spaces, hybrid learning courses, hybridity in recognition management, hybrid degree programmes, hybrid home study, etc. What they all have in common are holistic studies that make the connection between complexity, diversity, and hybridity (Schumann et al., 2021).
Connectivism as learning theory for the network society
Learning involves both experience and interaction of the learner with the world, generating permanent changes in performance potential as well as behavioural patterns. Behaviourism, cognitivism, and constructivism are thus related. From the epistemological point of view, objectivism, pragmatism and interpretivism are still relevant. All these learning theories together form a pool of hypotheses and models that can be used to describe and explain learning processes. This approach to learning theories is limited by its focus on internal, person-cantered learning, or how persons learn in a social context as individuals. Insufficient consideration is given to the influence of the learner’s environment, such as through organisational development, digitisation, and networking. The latter, for example, influence not only the actual learning process, but how learning occurs and the value of what is learned. Determining the value of what is learned is relevant to the orientation of knowledge transfer processes and learning in the face of information overloads on the one hand and fluctuating knowledge potentials on the other. The interplay of formal, non-formal and informal forms of learning in combination of interconnected educational, work and life worlds require the expansion of learning theory concepts. Based on these considerations, the learning theory of connectivism was developed, which should consider technology and connection in the digital age more than before. It includes principles of chaos, network, complexity, and self-organization theories (Siemens, 2005)
Connectivism brings learning theories closer to socially and technologically determined knowledge, network, and communication theories, thus serving to advance knowledge for learning in a digitized and globalized world. The recognition of the influence of interconnectedness, technological progress, states that do not follow fixed rules (chaos), and disruptive developments complements the possibilities of learning theory reflection of real learning processes in today’s world. However, it also implies new challenges due to the growing complexity of the areas of observation and investigation, possible disruptive changes, fuzziness, and imponderability in chaotic situations.
Hybridism on the way to a new learning theory
The influences on learning and teaching are becoming more and more complex and the associated order more and more chaotic due to the enormous growth of knowledge, the dynamics of science and technology, diversification of subject areas, variety of methodological possibilities, disruptive changes of organizations and processes, lifelong learning as well as digitalization, socialisation, economisation and ecologisation, extensive automation, merging of artificial and natural intelligence, etc. On the other hand, an ever better understanding of and for systems, processes, functions, data, information, knowledge, competencies, expertise is developing in networked, digitalized and hybrid worlds. Science theory, methodology, modelling and systematization as well as artificial intelligence and quantum technologies offer good opportunities for mastering chaotic orders, as they occur more and more frequently not only on the Internet, but also in logistics, knowledge transfer, social communities, etc. due to complexity.
Many influencing factors are bidirectionally related to each other, or their characteristics can be changed between two states. This is referred to as hybridity. If the variety of factors and their hybrid states is understood as dimensions, then a multidimensional model is created. The property to be hybrid or to generate hybrids is called hybridism. It is precisely this combinatorics of different influencing factors in the form of hybrid states (dimensions) that is characteristic of learning in the future. The associated modelling opens the opportunity to make chaotic and interconnected systems of learning more transparent, to model them and to be able to regulate them to a certain extent. There is much to suggest that hybridism will give rise to a new learning theory that will help to better master increasingly complex processes of knowledge acquisition and transfer via targeted and systematic learning despite information overload (Schumann et al., 2021). It is encouraging that in many cases scientific and methodical previous achievements, as for example by the theories of hybrid systems as well as by hybrid automata, exist, which can be transferred purposefully to the world of learning. Not to be forgotten are the new drivers for hybrid learning from artificial intelligence, whose effect will be significantly strengthened by the fusion of artificial and natural intelligence, which can also be modelled as a hybrid state in the context of hybridism.
Gugenberger, E. (2010). Das Konzept der Hybridität in der Migrationslinguistik. Sprache, Identität, Kultur, 8 (1). 67–92.
Kraidy, M. (2002). Hybridity in Cultural Globalization. Communication Theory, 12 (3). pp. 316-339.
Nayaka, J., Naikb, B. Kanungoa, D.P., Beheraa, H.S. (2018). A hybrid elicit teaching learning based optimization with fuzzy c-means (ETLBO-FCM) algorithm for data clustering. Ain Shams Engineering Journal Volume, 9 (3). pp. 379-393.
Olapiriyaku, K., & Scher, J. M. (2006). Ein Leitfaden zur Etablierung hybrider Lernkurse: Einsatz von Informationstechnologie, um eine neue Lernerfahrung zu schaffen, und eine Fallstudie. Das Internet und die Hochschulbildung, 9(4), pp. 287-301.
Schumann, C.-A., Nitsche, A.-M., Reuther, K., Tittmann, C. (2021). Hybridism – Theoretical Learning Response to the growing Diversity in Higher Education. EDEN Annual Conference (Online): Lessons from a pandemic for the future of education, Madrid.
Schumann, C.-A., Nitsche, A.-M., Reuther, K., Tittmann, C. (2021). Hybridx Higher Education – A Multidimensional Overlay of Hybrid Forms of Learning and Teaching. The Learning Ideas Conference 2021 (Online), New York.
Schumann, C.-A., Nitsche, A.-M., Schützner, F., Tittmann, C. (2021). Fuzzy-based approach for multidimensional hybrid models in higher education. TAKE Conference 2021 (Online): Theory and Applications in the Knowledge Economy 2021, Porto, pp. 77-83.
Stommel, J. (2012). What is Hybrid Pedagogy? Hybrid pedagogy: a digital journal of learning, teaching, and technology. Hybrid Pedagogy Inc. https://hybridpedagogy.org/hybridity-pt-2-what-is-hybrid-pedagogy/; accessed 25.11.2021, 21:17.
UNESCO. (2017). Education for Sustainable Development Goals. Learning Objectives. pp. 9-46.