cs.AI updates on arXiv.org 09月03日
考试准备指数(ERI)理论框架构建
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本文提出了一种考试准备指数(ERI)的理论框架,通过聚合六个信号来评估学习者的考试准备情况,包括掌握度、覆盖度、保持度、速度、波动性和耐力,并证明了其在蓝图加权下的单调性、Lipschitz稳定性和有界漂移。

arXiv:2509.00718v1 Announce Type: cross Abstract: We present a theoretical framework for an Exam Readiness Index (ERI): a composite, blueprint-aware score R in [0,100] that summarizes a learner's readiness for a high-stakes exam while remaining interpretable and actionable. The ERI aggregates six signals -- Mastery (M), Coverage (C), Retention (R), Pace (P), Volatility (V), and Endurance (E) -- each derived from a stream of practice and mock-test interactions. We formalize axioms for component maps and the composite, prove monotonicity, Lipschitz stability, and bounded drift under blueprint re-weighting, and show existence and uniqueness of the optimal linear composite under convex design constraints. We further characterize confidence bands via blueprint-weighted concentration and prove compatibility with prerequisite-admissible curricula (knowledge spaces / learning spaces). The paper focuses on theory; empirical study is left to future work.

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考试准备指数 理论框架 学习评估
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