Generative Data Imputation for Sparse Learner Performance Data Using Generative Adversarial Imputation Networks

DV ANCEMENTS in AI-driven technologies have significantly enhanced modern education through personalized tutoring and adaptive learning strategies on online platforms [1], [2]. Intelligent T utoring Systems (ITSs) exemplify this progress by leveraging advanced machine learning and natural language processing models to create interactive learning environments that improve outcomes across domains like literacy [3], mathematics [4], language learning [5], biology [6] and other STEM fields [7]. As human learners interact with ITSs, often through question-and-answer scenarios with immediate responses, their performance data becomes crucial for learner modeling, enabling systems to track progress, predict future performance, and adapt instruction accordingly [8]. Learner models like Bayesian Knowledge Tracing (BKT) and other knowledge tracing variants utilize the learner performance data to uncover learning characteristics, estimate knowledge states and acquisition [9]. However, in real-world scenarios, missing learner performance data is prevalent due to factors, such as learner dropout or disengagement [10], technical issues or incomplete data logging [11], biased sampling within experimental groups [12], and more. These challenges often lead to sparse data, where items (i.e., questions or problems) remain unattempted (e.g., learners may bypass the question, leave it unanswered due to a lack of response initiation, or make no attempt to engage with it), alongside limited learner interactions [13], [14]. As shown in Figure 1, missing performance records can occur along both the attempt and question dimensions during learner-ITS interactions. In the right portion of the figure's two matrices, entries marked with "?