Design and evaluation of a case-based system for modelling exploratory learning behaviour of math generalisation

Exploratory learning environments (ELEs) promote a view of learning that encourages students to construct and/or explore models and observe the effects of modifying their parameters. The freedom given to learners in this exploration context leads to a variety of learner approaches for constructing models and makes modelling of learner behaviour a challenging task. To address this issue, we propose a learner modelling mechanism for monitoring learners’ actions when constructing/exploring models by modelling sequences of actions reflecting different strategies in solving a task. This is based on a modified version of case-based reasoning for problems with multiple solutions. In our formulation, approaches to explore the task are represented as sequences of simple cases linked by temporal and dependency relations, which are mapped to the learners’ behaviour in the system by means of appropriate similarity metrics. This paper presents the development and validation of the modelling mechanism. The model was validated in the context of an ELE for mathematical generalisation using data from classroom sessions and pedagogically-driven learning scenarios

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