This work is like testing whether a student-success prediction tool that works for one class or group of students will still work well for a different class or a different course, and under what conditions it breaks down.
Universities increasingly use predictive models to flag students at risk of poor academic performance, but models often fail when applied to new cohorts or courses. This research analyzes the boundary conditions of when such models generalize well (within a cohort vs within a course) and when they do not, helping institutions avoid miscalibrated interventions and misplaced trust in analytics dashboards.
Domain-specific understanding of generalization behavior for academic performance models, tied to real educational data and course/cohort structures, which is hard to replicate without similar longitudinal datasets.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Model drift and performance degradation when student populations, course design, or assessment practices change across cohorts or courses.
Early Majority
Focuses explicitly on the generalization limits of academic performance prediction across cohorts and courses, rather than just building a single high-accuracy model; provides guidance on when and how models can be reused or must be retrained in educational settings.