Learning task-related strategies from user data through clustering

In exploratory learning environments, learners can use different strategies to solve the same problem. Not all these strategies, however, are known to the teacher and, even if they were, they need considerable time and effort to introduce them in the knowledge base. In this paper we propose a learning mechanism that extracts strategies from user data and presents them to the teacher for further authoring. To this end, a clustering approach is used in which the strategies of learners are grouped into clusters and the teacher is presented with a representative strategy for each cluster. The teacher can then decide whether to store the proposed strategies or to author them further. This approach allows populating the knowledge base using user data, thus saving authoring time for the teacher.