ADKT: Adaptive Deep Knowledge Tracing

Liangliang He, Jintao Tang, Xiao Li, Ting Wang

Abstract: Deep Learning based Knowledge Tracing (DLKT) has been shown to outperform other methods due to its strong representational ability. However, DLKT models usually exist a common flaw that all learners share the same model with identical network parameters and hyper-parameters. The drawback of doing so is that the learned knowledge state for each learner is only affected by the specific learning sequence, but less reflect the personalized learning style for each learner. To tackle this problem, we proposes a novel framework, called Adaptive Deep Knowledge Tracing (ADKT), to directly introduce personalization into DLKT. The ADKT framework tries to retrain an adaptive model for each learner based on a pre-trained DLKT model and trace the knowledge states individually for each learner. To verify the effectiveness of ADKT, we further develop the ADKVMN (Adaptive Dynamic Key-Value Memory Network) model by combining the ADKT and the classic DKVMN, which has been widely referred as a state-of-the-art DLKT model. With extensive experiments on two popular benchmark datasets, including the ASSISTments2009 and ASSISTments2015 datasets, we empirically show that ADKVMN has superior predictive performance than DKVMN.