Presentation by Mirjam Köck at Lernen, Wissen, Adaptivität (LWA 2008), University of Würzburg, 6.-8. October 2008. Track: ABIS
Motivation: eLearning has become very popular, but still simple approaches of implementing adaptivity dominate. Computational intelligence is under-represented. The authors focus on challenges such as the autonomous knowledge acquisition, autonomous pattern identification at run-time, and expression of patterns in rules.
Adaptive learning guidance includes
- navigation through learning materials
- Guiding communication and cooperation activities (suggestion communication partners, contact persons for questions, …)
Approach: Using communication and collaboration activities (rather than contents/ tags) are used as input for the user models. Identify groups of learners based on communication and learning behaviours (observing the style of learning, the level of activities, activity pattern).
Collecting information such as the user’s online time, actions related to communication (read, write, update, delete), user’s current knowledge, learning activities (time needed for test, time spent on content before taking tests, performance of tests), content of communication items
Relations of interest: How is a user’s time spent on communication related to the learning curriculum?; does the knowledge state influence communication?; what is the degree of similarity between a user’s activity level in the communication area and content area?; …
Promising Technologies: Artificial Neural Networks (can discover activity clusters, can adapt components e.g. change weights, do not depend on continuous human intervention; but: blackbox-syndrome, missing explanation capability, rule extraction is difficult); Combined Neuro-Fuzzy Approaches, Bayesian Networks (combination of domain knowledge and data; derivation of causal relationships) -> Combination allows using Neural Networks e.g. for learning and making the hidden sector of Neural Networks more visible.
Prospect: Improve adaptation; reducing human efforts to ensure quality and up-to-dateness of model data; semi-automatic pattern recognition, classification and evaluation at run-time; predication of behaviour based on correlations; integration of CI approaches into popular learning environment (Sakai; see also Stephan Weibelzahl)