Cognitive Space Time: A Model for Human-Centered Adaptivity in E-Learning
Kevin Fuchs, Peter A. Henning · 2018
We introduce a model for the implementation of human-centered adaptive systems in the field of e-learning. We use ontologies to create a machine-processable representation of learners, learning content and their semantics. We also introduce the concept of “Cognitive Space Time”, abbreviated as CST. This is a multi-dimensional hyperspace the dimensions of which represent arbitrary meta data items describing the properties and semantics of learning material as well as personal traits and the progress of a learner. Recorded over time, each learner draws a trajectory in that hyperspace, which we build and analyze with spatio-temporal data structures and algorithms that are known from the field of spatio-temporal databases. Finally, we explain how our ontologies and the CST can be used to create adaptive systems. We do this on a universal basis that is derived from the concept of the Turing machine. Therefore, our idea provides a technology-independent design strategy that can be implemented in any form of automaton. Both the ontology approach and the CST have been implemented in prototype systems that are presented in this document.