Intelligent Diagnosing and Learning Agents for Intelligent Tutoring Systems

Abstract

This research suggests two cooperating intelligent agents with new approaches for Intelligent Tutoring Systems (ITSs): one (a diagnosis agent) for correctly diagnosing the student's answers and another one (a learning agent) for intelligently learning from the student's responses. The diagnosis agent incorporates the classification tree concepts to identify the student's misconceptions and to do score assignment that includes the score assignment of partial correctness. The learning agent is a blackboard multistrategy machine learning model. The main purpose of the learning agent is to learn the features of the student's inconsistent behaviors for the system to take effective strategies to prevent the happening of the student's inconsistent behaviors. Importantly, accurate diagnosis on the student's answers is the prerequisite of a successful student model. Then an effective and efficient ITS is required to deal with the student's inconsistent behaviors.

• Intelligent Tutoring Systems (TTSs)
• Machine Learning
• Similarity-Based Learning (SBL)
• Explanation-Based Learning (EBL)
• Case-Based Reasoning (CBR)