Mining Relevant Examples for Learning in ITS Student Models

An Intelligent Tutoring System (ITS) provides direct customized instruction or feedback to students while they perform a task in a tutoring system without the intervention of a human. One of the main functions of an ITS system is to present its students with course materials that are most appropriate to their current knowledge of domain concepts, example being one of the course materials. ITS systems typically compare and analyze student model (SM) components for student´s current knowledge of concepts (main topics, e.g. Scanf in C programming) that are required to understand the next example (e.g. Codes for scanf) suitable for learning a task (e.g. Write C code to read 2 integers from the keyboard). Existing systems such as NavEx and PADS perform an exhaustive matching of student knowledge level with all examples in the database. This research proposes a task-based technique for managing and classifying examples for more effective retrieval of relevant examples for learning a task. We propose a system called EASK for translating task and example solutions into concepts for similarity matching, which is more readily available, easily extendible and adaptable to other domains. Examples and tasks are represented as vectors of weights computed with term frequency measure TFIDF that signify the importance of a concept for an example. Examples most similar to a task are found by using a classification method called k-NN, which finds the closeness between different objects such as examples and tasks using cosine similarity measure and selecting the k objects (examples) with highest similarity scores. As a by-product, k-NN also predicts the class label (difficulty level) of the task. Our proposed model achieves this prediction with 89% accuracy.