Predicting student grades via adaptive multi-level learning models.

Educational institutions increasingly rely on intelligent systems to extract actionable insights from student data. One critical application is the early prediction of student performance in specific courses, which can inform academic advising, course selection, and targeted interventions. This paper proposes an adaptive multi-level prediction framework that segments student-course data into homogeneous groups and assigns a temporally validated specialist model to each group. The framework is model-agnostic: it accepts any learner implementing standard fit/predict interfaces, including linear regression, ensemble methods, neural networks, and collaborative filtering. To combat temporal data sparsity common in volatile or block-cohort curriculum structures, the framework incorporates an automated data-density fallback guard that dynamically transitions isolated data slices to localized validation pools. Systematic validation on two large-scale, real-world higher education datasets demonstrates strong cross-institutional generalizability. On a dense traditional university dataset, the framework yields an 18.6% RMSE improvement over global baselines, with model selection converging heavily on tree-based ensembles. Conversely, on a volatile sparse course dataset, the pipeline automatically uncovers an extraordinarily diverse model ecosystem-selecting neural layers for 48% of clusters and triggering the collaborative filtering 22% of chronological windows. Backed by asymptotic significance testing (p-val. [Formula: see text]), these results prove that the framework effectively shifts the configuration burden from manual heuristics to self-correcting, data-driven optimization.