Behavioral and Performance Indicators of Depression and Anxiety in Electronic Learning Systems

2026-06-02

AI Summary

This study investigates whether student behaviors and academic performance in e-learning environments can reflect symptoms of depression and anxiety. Leveraging Moodle log data, course grades, and self-reported psychological scales, the research employs event log mining, descriptive statistics, Benjamini–Hochberg–corrected t-tests, effect size estimation, and Spearman correlation analyses to systematically identify significant associations between multidimensional learning management system (LMS) behavioral indicators and mental health symptoms. The findings reveal that depression is significantly associated with temporal shifts in activity patterns, increased session duration, and last-minute assignment submissions, whereas anxiety correlates strongly with the concentration of study time and variability in session characteristics. These results highlight the potential of such non-intrusive behavioral markers to serve as early warning signals for mental health concerns in educational settings.

Abstract

This study investigates whether behavioral and performance indicators derived from a Moodle-based learning management system are associated with university students' depression and anxiety in two undergraduate Computer Engineering courses. Using a quantitative observational design, LMS event logs, academic records, and self-reported Beck Depression Inventory-II and Beck Anxiety Inventory scores from 97 students were integrated. A broad set of behavioral and performance indicators spanning temporal engagement, session structure, deadline-related behavior, page-refresh patterns, and LMS navigation was extracted from raw event logs and analyzed using descriptive statistics, independent-samples t-tests with Benjamini-Hochberg FDR correction, effect sizes, and Spearman correlations; inventory scores were confirmed invariant by sex and academic year. Several indicators were significantly associated with depression and anxiety. Higher depression was associated with shifted temporal activity patterns, longer session durations, and shorter homework submission lead times, while higher anxiety was associated with concentrated temporal engagement and session-based differences. These findings suggest that routine LMS data can provide meaningful behavioral signals related to student well-being and may support earlier educational awareness of students who experience mental-health-related strain. At the same time, such indicators should be interpreted as contextual and non-diagnostic markers rather than as substitutes for clinical assessment.

Problem

• depression
• anxiety
• learning management system
• behavioral indicators
• student well-being

Innovation

• learning analytics
• behavioral indicators
• mental health detection
• LMS event logs
• educational data mining