Is Log-Traced Engagement Enough? Extending Reading Analytics With Trait-Level Flow and Reading Strategy Metrics
Erwin Lopez, Atsushi Shimada

TL;DR
This study enhances learning analytics by integrating trait-level flow and reading strategies with log data, revealing their significant role in explaining student performance and informing personalized educational interventions.
Contribution
It introduces a novel approach combining trait-level flow and reading strategies with log data to better predict learning outcomes beyond traditional engagement measures.
Findings
DEC and reading strategies explain 21.3% additional variance in grades
DEC moderates the relationship between reading behaviors and performance
Reading log analysis should incorporate personal traits for personalized interventions
Abstract
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 ({\Delta}R2 = 21.3% over the baseline 25.5%), and (2) DEC moderates the relationship…
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Taxonomy
TopicsFlow Experience in Various Fields · Online Learning and Analytics · Intelligent Tutoring Systems and Adaptive Learning
