Some Improvements of Deep Knowledge Tracing

Abstract

Deep Knowledge Tracing (DKT), along with other machine learning approaches, are biased toward data used during the training step. Thus, for problems where we have few amounts of data for training, the generalization power will be low, the models will tend to give good results on classes containing many examples and poor results on those with few examples. Theses problems are frequent in educational data where for example, there are skills that are very difficult (floor) or very easy to master (ceiling). There will be less data on students that correctly answered questions related to difficult knowledge and that incorrectly answered questions related to knowledge easy to master. In that case, the DKT is unable to correctly predict the student's answers to questions associated with those skills. To improve the DKT, we penalize the model using a 'cost-sensitive' technique. To overcome the problem of the few amounts of data, we propose a hybrid model combining the DKT and expert knowledge. Thus, the DKT is combined with a Bayesian Network (built from domain experts) by using the attention mechanism. The resulting model can accurately track knowledge of students in Logic-Muse Intelligent Tutoring System (ITS), compared to the BKT and the original DKT.