Behavioral and Performance Indicators of Depression and Anxiety in Electronic Learning Systems
2026-06-02 • Human-Computer Interaction
Human-Computer InteractionComputers and Society
AI summaryⓘ
The authors studied if data from an online learning system (Moodle) could show signs of depression and anxiety in university students taking computer engineering courses. They looked at how students used the system, like when and how long they studied, and compared this to self-reported mental health scores. They found that certain behaviors, like studying at different times or closer to deadlines, were linked to higher depression or anxiety levels. The authors say these patterns could help spot students who might be struggling but should not replace professional mental health checks.
Learning Management System (LMS)MoodleBeck Depression Inventory-IIBeck Anxiety InventoryBehavioral IndicatorsTemporal EngagementSession DurationAcademic PerformanceMental Health ScreeningSpearman Correlation
Authors
Arya VarastehNezhad, Fattaneh Taghiyareh
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.