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Since completing my PhD where I explored the understanding of the nature of an object-oriented program, I have increasingly focused on the use of variation theory to promote learning improvement and transformative learning around threshold concepts.

Variation Innovation To Enhance Learning (VITEL)

Is it possible that we can have a learning environment in which the learner immerses themselves in solving problems by learning the skills and knowledge necessary? Is it possible that this environment could adapt based on the understandings being expressed by the learner and within their potential zone of development (Vygotsky, 1978, 1986)? We recognize that what engages learners and the path of learning may not be the same for all learners and that people will be explore different knowledge domains based on their motivational interests. As a result, the learning environment needs to be able to determine key characteristics of the learner’s interests and to open up variations in their awareness (Marton, 2015) that they will inspire them to further exploration, that is those aspects and features that are within their vision to examine and possibly accept (Vygotsky, 1978, 1986).

Adaptive or smart learning environments, game-based or scenario-based learning

As I prepared to do my PhD, my interest was in developing a learning environment to improve the learning of programming. At that time, I proposed a scenario-based learning environment (Thompson, 2004) based on work that I had done preparing distance learning materials (Thompson, 2003). Many of the principles in these two papers have not yet reached maturity. With the increase in emphasis on adaptive learning environments, I have prepared a proposal for research into smart learning environments utilising machine learning and variation theory (Thompson, 2015). A PhD student has accepted the challenge to explore this work further.

This proposal lays the foundations for an adaptive learning engine that endeavours to present variations in understanding to the learner based on hierarchies of understanding developed by the learning facilitator, teacher, or lecturer. The system is aimed to identify the way the learner understands a concept and then present alternative variations in a way that opens the learner to new possibilities.

If a learning engine can be built around this approach to learning then the next step is to build scenario-based or game-based environments that use this adaptive learning engine to foster conceptual change in subject areas that are critical to the survival of the planet and humanity.


Marton, F. (2015). Necessary conditions of learning. New York and London: Routledge.

Thompson, E. (2003). Giving a context to learning. In E. Errington (Ed.), Developing Scenario-based Learning: Practical insights for tertiary teachers (pp. 74-82). Palmerston North: Dunmore Press. ISBN: 0-86469-443-1

Thompson, E. (2004) Design issues for a scenario-based learning environment . (Technical Report No. 4/2004), Palmerston North: Department of Information Systems, Massey University. ISSN: 1175-1738

Thompson, E. (2015) Smart Learning Environment Research . Birmingham, UK: Computer Science, Aston University.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes . In M. Cole, V. John-Steiner, S. Scribner, & E. Soberman (Eds.). Cambridge, MA: Harvard University Press.

Vygotsky, L. S. (1986). Thought and language (A. Kozulin, Trans.). Cambridge, MA: The MIT Press.