🤖 AI Summary
Adaptive learning systems often achieve precise diagnosis but suffer from weak pedagogical interventions, leading to delayed or mismatched instructional responses. To bridge the gap between diagnosis and teaching, this paper proposes a teacher-led feedback闭环 system that transforms knowledge-component–level assessment evidence into empirically validated micro-interventions. We introduce three novel safeguards—complete closure of ability gaps, cognitive load control under attention constraints, and anti-redundancy diversity preservation—formulated as a constrained binary integer programming problem. Our hybrid solver balances the richness-latency trade-off by jointly modeling ability estimation, prerequisite dependencies, difficulty windows, and diversity constraints. Evaluated in a large-scale physics course (N ≈ 1,000), the system achieves near-universal mastery across skills, reduces redundant recommendations by 12 percentage points, improves task difficulty distribution uniformity, maintains low computational overhead, and ensures subgroup fairness.
📝 Abstract
Adaptive learning often diagnoses precisely yet intervenes weakly, yielding help that is mistimed or misaligned. This study presents evidence supporting an instructor-governed feedback loop that converts concept-level assessment evidence into vetted micro-interventions. The adaptive learning algorithm contains three safeguards: adequacy as a hard guarantee of gap closure, attention as a budgeted constraint for time and redundancy, and diversity as protection against overfitting to a single resource. We formalize intervention assignment as a binary integer program with constraints for coverage, time, difficulty windows informed by ability estimates, prerequisites encoded by a concept matrix, and anti-redundancy enforced through diversity. Greedy selection serves low-richness and tight-latency regimes, gradient-based relaxation serves rich repositories, and a hybrid method transitions along a richness-latency frontier. In simulation and in an introductory physics deployment with one thousand two hundred four students, both solvers achieved full skill coverage for essentially all learners within bounded watch time. The gradient-based method reduced redundant coverage by approximately twelve percentage points relative to greedy and harmonized difficulty across slates, while greedy delivered comparable adequacy with lower computational cost in scarce settings. Slack variables localized missing content and supported targeted curation, sustaining sufficiency across subgroups. The result is a tractable and auditable controller that closes the diagnostic-pedagogical loop and delivers equitable, load-aware personalization at classroom scale.