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.

翻译：学生参与度是学习分析领域的核心构念，但现有研究常通过日志衍生的持续性指标对其进行操作化，忽略了情感认知状态。本研究聚焦阅读日志分析，探讨特质层面的心流——操作化为体验深度专注状态的倾向——以及从电子书交互数据中提取的阅读策略痕迹，如何能够拓展传统参与度指标对学习结果的解释力。我们在两门工程课程中对100名学生收集数据，将深度专注状态的问卷测量与细粒度阅读日志相结合。相关分析与回归分析表明：（1）深度专注状态与阅读策略痕迹能够解释成绩变异中超出日志追踪参与度的显著额外部分（在基线25.5%的基础上提升ΔR²=21.3%）；（2）深度专注状态调节阅读行为与结果之间的关系，表明日志衍生指标转化为学业表现的过程存在特质敏感性差异。这些发现提示，为支持更公平、个性化的教学干预，阅读日志分析应当超越“一刀切”的解读模式，整合个人特质与包含阅读行为及策略维度的测量指标。