Unsupervised MDP Value Selection for Automating ITS Capabilities.
John C. Stamper, Tiffany M. Barnes · Educational Data Mining · 2009
We seek to simplify the creation of intelligent tutors by using student d ata acquired from standard computer aided instruction (CAI) in conjunction with educational data mining methods to automatically generate adaptive hints. In our previous work, we have automatically generated hints for logic tutoring by constructing a Markov Decision Process (MDP) that holds and rates historical student work for automatic selection of the best prior cases for hint generation. This method has promise for domain-independent use, but requires that correct solutions be assigned high positive values by the CAI or an expert. In this research we propose a novel method for assigning prior values to student work that depends only on frequency of occurrence for the component steps, and compare how these values impact automatic hint generation when compared to our MDP approach. Our results show that the utility metric outperforms a classic MDP solution in selecting hints in logic. We believe this method will be particularly useful for automatic hint generation for ill-defined domains.