Transformer-based convolutional forgetting knowledge tracking

Knowledge tracking is to analyze the mastery of students' knowledge through the learning track. This is very important for online education, since it can determine a learner’s current knowledge level by analyzing the learning history and then make recommendations for future learning. In the past, the commonly used model for knowledge tracking is the convolutional neural network, but it has long-term sequence dependencies. With the invention of Transformer, it has excellent performance in long-sequence modeling by virtue of the attention mechanism, and is gradually introduced into the field of knowledge tracking. However, through our research, some knowledge tracking data sets have a large number of continuous and repetitive training, which will cause Transformer model to ignore the potential connections between some knowledge points. To overcome this problem, we introduce a convolutional attention mechanism to help the model perceive contextual information better. In addition, we simulate the forgetting phenomenon of students during the learning process by calculating the forgetting factor, and fuse it with the weight matrix generated by the model to improve the accuracy of the model. As a result, a Transformer-based Convolutional Forgetting Knowledge Tracking (TCFKT) model is presented in this paper. According to the experimental results conducted on the real world ASSITments2012, ASSISTments2017, KDD a, STATIC datasets, the TCFKT model outperforms other knowledge tracking models.