Predictive Learning Analytics on Small Educational Datasets

Research Problem

How can machine learning predict student disengagement and learning outcomes from classroom-scale datasets of 25–35 students, where traditional statistical approaches lack power and overfitting is a constant risk?

Technological Innovation

A multi-layer analytics engine combining real-time engagement scoring across time-on-task, activity completion, retry patterns and score distributions; predictive pattern detection for completion drops, grading delays, attendance decline and enrollment trends; LLM-enhanced insight generation producing natural-language explanations; and automated class-assignment optimization based on learning profiles.

Why This Is Hard

A class of 25 generates far less data than consumer applications. Engagement scoring must be calibrated per pedagogy type — music, math and lab experiments produce fundamentally different signals. Models trained on one school may not generalize to another; transfer learning in education is largely unsolved. And misleading predictions damage teacher trust — which is fatal for research validation and institutional adoption.

Success Criteria

Greater than 70% recall on at-risk student identification with a false-positive rate under 15%, validated retrospectively against actual student outcomes across 18 months of pilot-school data. Mitigations include Bayesian priors, hierarchical models, and cross-school data pooling under differential privacy.