Fine-grained Learning Performance Prediction via Adaptive Sparse Self-Attention Networks

Abstract Deep learning (DL) techniques have shown good potential in building favorable predictive models in e-learning environments. It is important to build a DL-based, fine-grained student performance prediction model to predict students’ outcomes and learning status at every stage of their course, rather than merely predicting the students’ final score and drop-out rate, which is referred to as coarse-grained performance. In this paper, we tackle the problem of fine-grained student performance prediction in an online course using DL-based long sequence generation by formulating the students’ feature as a matrix where elements in certain parts are missing. We propose an adaptive sparse self-attention network to generate the missing values and simultaneously predict the fine-grained performance. The matrix representation of the features facilitates position-wise feature selection, which helps to find the most correlated components from the past learning stage, leading to an embedding with both the original features and their spatial relationships. We build a deep neural network model with several sparse self-attention layers stacked together to achieve sequence generation. Experimental studies on three real-world datasets from different e-learning platforms demonstrate the effectiveness and advantages of our proposed method.