跨初始成就谱系的学习贡献表征框架
A Framework for Characterizing Learning Contributions Across the Initial Achievement Spectrum
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- Northwestern University（西北大学）
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中文总结 AI 辅助
本研究提出学习贡献框架，通过曲线和剖面刻画不同初始成就学生的贡献分布，并基于统计模型预测其变化，揭示传统平均指标掩盖的学习模式。
中文摘要 AI 辅助
概念评估被广泛应用于物理教育研究中，以评估学生理解的变化，然而班级平均分数可能掩盖这些变化在不同初始成就水平的学生之间的分布情况。我们引入了一个学习贡献框架，通过学习贡献曲线（LCC）和学习贡献剖面（LCP）来表征这种分布。对于一般的贡献度量G，LCC表示按初始成就排序的学生累积贡献，而LCP则描述了相对于个体G值总体均值的局部平均贡献。我们将该框架应用于个体Hake归一化增益，并开发了一个统计模型，将LCC和LCP行为与前后测成绩的联合结构联系起来。在后续成绩的条件均值是初始成绩的线性函数这一核心假设下，我们识别出一个分数结构参数β和一个临界分数结构参数β_c。β是否大于、小于或等于β_c决定了预期LCP随初始成就增加、减少或保持不变。给定前测分布，该模型进一步产生了完整LCP和LCC的解析预测，这些预测与模拟结果高度吻合。应用于课堂概念评估数据表明，该框架揭示了传统Hake归一化增益所揭示的整体班级平均分数中不明显的局部和累积学习贡献模式。
英文摘要
Conceptual assessments are widely used in physics education research to evaluate changes in student understanding, yet class average measures can obscure how those changes are distributed across students with different levels of initial achievement. We introduce a Learning Contribution Framework that characterizes this distribution through the Learning Contribution Curve (LCC) and Learning Contribution Profile (LCP). For a general contribution measure G, the LCC represents cumulative contribution across students ranked by initial achievement, whereas the LCP describes the local mean contribution relative to the population mean of the individual G values. We apply the framework to the individual Hake normalized gain and develop a statistical model linking LCC and LCP behavior to the joint structure of pretest and posttest scores. Under the central assumption that the conditional mean of the subsequent score is linear in the initial score, we identify a score structure parameter $β$ and a critical score structure parameter $β_c$. Whether $β$ is greater than, less than, or equal to $β_c$ determines whether the expected LCP increases, decreases, or remains constant across initial achievement. Given the pretest distribution, the model further yields analytical predictions for the complete LCP and LCC that closely reproduce simulation results. Application to classroom concept-assessment data illustrates how the framework reveals local and cumulative patterns of learning contribution that are not evident from an overall class average revealed by the traditional Hake's normalized gain.
