Muti-behavior features based knowledge tracking using decision tree improved DKVMN

Knowledge tracing is a significant research topic in student modeling. It is a task to model students' mastery level of knowledge points by mining their historical exercise performance. Literature has shown that Dynamic Key-Value Memory Networks (DKVMN) which has been proposed to handle the knowledge tracing task generally outperform traditional methods. However, through our experimentation, we have noticed a problem in the DKVMN model that it ignored behavior features collected by intelligence tutoring system (ITS) but only regard the exercise and the correctness as input. Behavior features, such as the response time, the hint request and the number of attempt, can be used to capture the student's learning behavior information and are very helpful in modeling the student's knowledge status. Therefore, the performance of the model can be improved. This work aims to improve the performance of the DKVMN model by incorporating more features to the input. More specifically, we apply decision tree classifier to preprocess the behavior features, which is an effective way to capture how the student deviates from others in the exercise. The predicted response concatenated with the exercise tag to train a DKVMN model, which can output the probability that a student will answer the exercise correctly. The experiment results show that our adapted DKVMN model, incorporating more combinations of behavior features can effectively improve accuracy. On the ASSISTments 2009 education dataset, the AUC value of our experiment is eight percent higher than the original DKVMN model.