Cold Start Knowledge Tracing with Attentive Neural Turing Machine

Abstract

Deep learning based knowledge tracing approaches achieve high accuracy in mastery prediction with pattern extraction on a large learning behavior data set. However, when there is little training data available, these approaches either fail to extract the key patterns or result in over fitting. Ideally, we aim to provide a similar learning experience to both the first group of learners, who interact with a new course or a new activity with little learning behavior data to provide personalized guidance, and the learners who interact with the course later. We propose a novel architecture, Attentive Neural Turing Machine (ANTM), to solve the cold start knowledge tracing problem. The proposed ANTM comprises an attentive controller module and differential reading and writing processes with extra memory bank. Accuracy (ACC) and Area Under Curve (AUC) measures are used for model performance comparison. Results show the proposed approach can learn fast and generalize well to unseen data. It achieves around 95% ACC trained with only 3 learners, while conventional deep learning based approaches achieve only 65% ACC with over prediction issues.