Input-Aware Neural Knowledge Tracing Machine

Knowledge Tracing (KT) is the task of tracing evolving knowledge state of each student as (s)he engages with a sequence of learning activities and can provide personalized instructions. However, exiting methods such as Bayesian Knowledge Tracing (BKT) and Deep Knowledge Tracing (DKT) either cannot capture the relationship among different concepts or lack of interpretability. Although Knowledge Tracing Machines (KTM) makes up for these shortcomings, it only uses a linear function to model students' knowledge states, which cannot capture more information contained in each feature. To solve above problems, this work introduces a novel model called Input-aware Neural Knowledge Tracing Machine (INKTM) which can enhance the interpretability to some extent and capture more complex structure information of real-world data to improve prediction performance. Unlike standard FM-based methods that focus on the feature interactions, our model focuses more on the information contained in each feature itself and retains all 2-order feature interactions. By converting weights of each feature to a multidimensional vector, our model can use the vectors to learn a unique attention weight of each feature in different instances by an attention network, so as to highlight important features and then enhance interpretability. At last, we input re-weighted features to a deep neural network to capture the non-linear and complex inherent structure of data. Experiment results show our model can consistently outperform existing models in a range of KT datasets.