Adaptation Schemes for Question's Level to be Proposed by Intelligent Tutoring Systems

The main challenge in developing a good Intelligent Tutoring System (ITS) is, surely, the question of how to adapt the hardness level of questions and tasks to the current student's capabilities, assuming these are dynamically changing over the tutoring period. According to state of art, most ITS systems make use of the Q-learning algorithm for this adaptation task. Our paper presents innovative results that compare performances of several methods not applied before for ITS to handle the above challenge. In particular as far as we know this is the first attempt to apply the Bayesian inference algorithm for question level matching in ITS. Next, as this is a groundwork research done to identify the best adaptation scheme, we propose to use an artificial environment with simulated students for the evaluation phase. The results were benchmarked with the optimal performance of the system assuming the user model (abilities) is completely known to the ITS. The results show that the method that outperforms the other is based on a Bayesian Inference which achieves more than 90% of the optimal performance. Our conclusion is that it may be worthwhile to integrate Bayesian inference based algorithms to adapt to student's level in the ITS. Future work is required in to confront these empirical results with those of real students.