A Data Mining Approach to Construct Production Rules in an Educational Game

One of the most crucial aspects of Intelligent Tutoring Systems in a collaborative serious game is production rules. Given the large number of interactions and conversation between players, it is difficult to follow student questions and reactions in the game environment. Therefore, creating a sophisticated method to construct production rules for handle the students’ interactions will boost the performance of the system. In this paper, we propose a state-of-the-art computational approach to automatically generate production rules using co-occurrences of distinct terms from a corpus of students’ conversations. Moreover, our model is able to generate additional production rules as new data is available. Finally, we also introduce how to transfer extracted co-occurrences into production rules, and how to build these into the game system.