Is Log-Traced Engagement Enough? Extending Reading Analytics With Trait-Level Flow and Reading Strategy Metrics

2026-02-23

AI Summary

Traditional log-based metrics struggle to capture learners’ affective-cognitive states, limiting comprehensive explanations of learning outcomes. This study addresses this gap by integrating trait-like Deep Effortless Concentration (DEC)—a dispositional form of flow—with fine-grained reading strategy behaviors extracted from e-book interactions to construct a more holistic engagement metric. Through questionnaire-based DEC assessment, detailed analysis of reading logs, and regression modeling, the research reveals, for the first time, DEC’s moderating role in the relationship between behavioral indicators and academic performance. Findings show that incorporating DEC and reading strategies explains an additional 21.3% of the variance in academic achievement beyond baseline models, offering both theoretical and methodological innovations for personalized learning analytics.

Technology Category

• Machine Learning: Evaluation and Analysis
• Search and Optimization: Evaluation and Analysis
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web measurements

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 (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.

Problem

• student engagement
• reading analytics
• trait-level flow
• reading strategies
• learning outcomes

Innovation

• trait-level flow
• reading strategies
• learning analytics
• Deep Effortless Concentration
• log-traced engagement