A Unified 5-Dimensional Framework for Student Models

This paper defines 5 key dimensions of student models: whether and how they model time, skill, noise, latent traits, and multiple influences on student performance. We use this framework to characterize and compare previous student models, analyze their relative accuracy, and propose novel models suggested by gaps in the multi-dimensional space. To illustrate the generative power of this framework, we derive one such model, called HOT-DINA (Higher Order Temporal, Deterministic Input, Noisy-And) and evaluate it on synthetic and real data. We show it predicts student performance better than previous methods, when, and why.