日志追踪参与度是否足够？通过特质层面的流和阅读策略指标扩展阅读分析
Is Log-Traced Engagement Enough? Extending Reading Analytics With Trait-Level Flow and Reading Strategy Metrics
AI总结：
本研究通过引入特质层面的流和阅读策略指标，扩展了传统阅读分析，发现这些指标能更有效地解释学习成果并提升干预的公平性和个性化。
摘要：
学生参与度是学习分析中的核心概念，但通常通过从日志中提取的持续性指标来操作化，忽略了情感-认知状态。聚焦于阅读日志的分析，本研究探讨了特质层面的流——操作化为体验深度无意识集中（DEC）的倾向——以及从电子书交互数据中提取的阅读策略痕迹，如何扩展传统参与指标以解释学习成果。我们从两个工程课程中的100名学生收集数据，结合DEC的问卷测量与细粒度阅读日志。相关性和回归分析显示，（1）DEC和阅读策略痕迹在解释成绩方面比日志追踪参与度解释了更多的方差（ΔR2 = 21.3%超过基线25.5%），（2）DEC调节阅读行为与成果之间的关系，表明在特质敏感性差异下，日志衍生指标如何转化为表现。这些发现表明，为了支持更加公平和个性化的干预，阅读日志的分析应超越一刀切的解释，并整合个人特质与包括行为和策略阅读测量的指标。
英文摘要：
Student engagement is a central construct in Learning Analytics, yet it is often operationalized through persistence indicators derived from logs, overlooking affective-cognitive states. Focusing on the analysis of reading logs, this study examines how trait-level flow - operationalized as the tendency to experience Deep Effortless Concentration (DEC) - and traces of reading strategies derived from e-book interaction data can extend traditional engagement indicators in explaining learning outcomes. We collected data from 100 students across two engineering courses, combining questionnaire measures of DEC with fine-grained reading logs. Correlation and regression analyses show that (1) DEC and traces of reading strategies explain substantial additional variance in grades beyond log-traced engagement (ΔR2 = 21.3% over the baseline 25.5%), and (2) DEC moderates the relationship between reading behaviors and outcomes, indicating trait-sensitive differences in how log-derived indicators translate into performance. These findings suggest that, to support more equitable and personalized interventions, the analysis of reading logs should move beyond a one-size-fits-all interpretation and integrate personal traits with metrics that include behavioral and strategic measures of reading.
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