Framework of a decision-theoretic tutoring system for learning of mechanics

This paper presents the application of decision-theoretic technique to computer-based tutoring system for elementary mechanics. The technique uses sound probabilistic reasoning and a student model to identify learner's misconception(s). Bayesian belief networks are the building blocks of the student model. The probability values in Bayes' nets are provided by teacher and are based on her judgement, but may be substituted with actual statistics. Evidence on student's mastery of concepts is obtained through her responses to appropriately selected items. Subsequently, Rasch one-parameter model is used to calibrate the item and person parameters (also known as difficulty and ability indices, respectively). The system is able to provide teacher with information for fine-tuning her pedagogical instructions and guide her in coaching students. It is also able to provide students with immediate feedback to improve their proficiencies and ultimately their grades. © 2000 Plenum Publishing Corporation.