Exercise Generation by Group Models for Autonomous Web-Based Learning
Michael Sonntag · 2009
Abstract — Creating exercises for learners requires signifi-cant time. This is one reason, beside difficulties of discussing individualized tasks in a classroom setting, why often only few exercises are created and posed to all learners alike. In web-based autonomous learning the setting problem is re-moved, while simultaneously demand for individualized ex-ercises, comparable to adaptive learning material, increases. A model is proposed for assembling exercises from inde-pendent elements and creating an exemplary solution along-side. Here the cold-start problem is especially problematic, as opposed to learning material no sensible default-view ex-ists. This difficulty can be reduced by integrating a group model, i.e. the history of results or actions of other learners. This paper presents an implementation of the model for gen-erating cases for learning in the legal area. The web-based user interface of such an online system is very important to render comparing the learners ' solution with the exemplary one simple, allowing correction by peers as well. The current implementation state as well as planned extensions is de-scribed. Adaptivity, web-based learning, case generation I.