An Incremental Nonlinear Co-Latent Factor Analysis Model for Large-Scale Student Performance Prediction

Predicting student performance (PSP) is critical to intelligent tutoring systems in online education services. Accurate predictions enable data-driven decision making and facilitate the implementation of timely educational interventions. While previous approaches, such as cognitive diagnosis models and data mining techniques, have demonstrated effectiveness with small-scale and static datasets, they face significant challenges in large-scale online learning environments. In these settings, vast volumes of learning responses are continuously generated as students engage with exercises. Those responses are characterized by high dimensionality and incompleteness (HDI) and often manifest as streaming data, which limits the applicability of most existing prediction methods, therefore posing a new challenge to traditional PSP tasks. To remedy the void of PSP in the HDI and incremental scenario, we propose an incremental nonlinear co-latent factor analysis (IN-CoLFA) model, which enhances latent factor analysis – an element-wise learning framework – by incorporating a co-factorization technique and integrating a neural network-inspired structure. To facilitate incremental learning, we develop momentum-accelerated stochastic gradient-based algorithms, which enable the model to perform offline training on historical data and continuously refine its predictions as new student performance becomes available. Experiments on several real-world datasets demonstrate the efficacy and efficiency of our approach in both stationary and incremental PSP tasks under HDI conditions.