Hybrid model for learner modelling and feedback prioritisation in exploratory learning

Individual and/or hybrid AI techniques are often used in learning environments for well-structured domains to perform learner diagnosis, create and update a learner model and provide support at the individual or group level. This paper presents a conceptual model that employs a synergistic approach based on Case-Based Reasoning (CBR) and Multicriteria Decision Making (MDM) components for learner modelling and feedback generation during exploration in an ill-defined domain of mathematical generalisation. The model uses a CBR component to represent and match the learners’ constructions and the strategies adopted during exploratory learning activities. The CBR component is used to diagnose what students are doing on the basis of simple and composite cases that allow a detailed diagnosis of the learners’ constructions and facilitate contextualised and personalised feedback. Simple cases represent parts of the models that the learners could possibly construct during an exploratory learning activity, while composite cases, which are assembled from simple cases, correspond to strategies that learners may adopt to construct their models; similarity measures are used to identify how close/far are the learners from solutions pre-specified and stored in the knowledge base. This information is then fed into the MDM component that is responsible for prioritising types of feedback depending on the context. This is based on a formulation of the feedback generation problem in terms of a multicritiria decision making: a set of criteria are considered together with a set of alternatives, the later corresponding to different types of feedback. The operation of the two components and the effectiveness of the synergistic approach are validated through user scenarios in the context of an exploratory learning environment for mathematical generalisation.