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

TL;DR

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.

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.

Figures (1)

• Figure 1: (Top) Clusters’ z-normalized metric values. (Bottom) Predicted grades from DECI for each cluster. Lines show model estimates; dark bands = 95% CI, light bands = 95% prediction intervals. Points are actual grades, colored by engagement (global scale).