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

• URL: http://arxiv.org/abs/2606.04254v1
• Date: Tue, 02 Jun 2026 22:08:07 GMT
• Title: Behavioral and Performance Indicators of Depression and Anxiety in Electronic Learning Systems
• Abstract summary: This study investigated whether behavioral and performance indicators derived from a Moodle-based learning management system are associated with depression and anxiety in computer engineering students.<n>Several indicators were significantly associated with depression and anxiety.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology [18.76197876841284]
  We propose the Circadian Rhythm Score (CRS), a composite index that compresses multi-domain daily behaviors into a unified representation of circadian rhythm.<n> CRS is constructed to maximize discriminative power for depression screening while preserving behavioral semantics.<n>We develop an interpretable depression screening framework based on gradient-boosted trees and SHAP analysis, revealing nonlinear and saturation-like associations between circadian rhythm and depression risk.
  arXiv  Detail & Related papers  (2026-07-06T04:04:32Z)
• Explainable Detection of Depression Status Shifts from User Digital Traces [1.3266402517619371]
  We propose an explainable framework for detecting and analyzing depression-related status shifts in user digital traces.<n>We evaluate the framework on two social media datasets.
  arXiv  Detail & Related papers  (2026-05-14T15:56:38Z)
• MA-DLE: Speech-based Automatic Depression Level Estimation via Memory Augmentation [59.709198999959206]
  Speech-based automatic estimation of depression levels is essential for enabling early detection and timely intervention.<n>Most existing approaches rely on RNN-based architectures to model temporal information for depression estimation.<n>We introduce a memory-based feature augmentation method that enhances the representational capacity of GRU-extracted features.
  arXiv  Detail & Related papers  (2026-04-20T14:42:13Z)
• Time-Resolved EEG Decoding of Semantic Processing Reveals Altered Neural Dynamics in Depression and Suicidality [28.636815691167474]
  We investigated semantic dynamics of affective processing using EEG.<n>Individuals with depression and suicidal ideation showed earlier onset, longer duration, and greater amplitude decoding responses.
  arXiv  Detail & Related papers  (2025-07-30T00:58:51Z)
• Real-Time Stress Monitoring, Detection, and Management in College Students: A Wearable Technology and Machine-Learning Approach [1.0389278083386493]
  College students are increasingly affected by stress, anxiety, and depression.<n>This study evaluated the efficacy of a mobile health (mHealth) intervention, Mental Health Evaluation and Lookout Program (mHELP)<n>mHELP integrates a smartwatch sensor and machine learning (ML) algorithms for real-time stress detection and self-management.
  arXiv  Detail & Related papers  (2025-05-21T19:45:42Z)
• Large-scale digital phenotyping: identifying depression and anxiety indicators in a general UK population with over 10,000 participants [2.2909783327197393]
  We conducted a cross-sectional analysis of data from 10,129 participants recruited from a UK-based general population. Participants shared wearable (Fitbit) data and self-reported questionnaires on depression (PHQ-8), anxiety (GAD-7), and mood via a study app. We observed significant associations between the severity of depression and anxiety with several factors, including mood, age, gender, BMI, sleep patterns, physical activity, and heart rate.
  arXiv  Detail & Related papers  (2024-09-24T16:05:17Z)
• The Relationship Between Speech Features Changes When You Get Depressed: Feature Correlations for Improving Speed and Performance of Depression Detection [69.88072583383085]
  This work shows that depression changes the correlation between features extracted from speech. Using such an insight can improve the training speed and performance of depression detectors based on SVMs and LSTMs.
  arXiv  Detail & Related papers  (2023-07-06T09:54:35Z)
• Handwriting and Drawing for Depression Detection: A Preliminary Study [53.11777541341063]
  Short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms. The aim of this study is to use a new tool, the online handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients.
  arXiv  Detail & Related papers  (2023-02-05T22:33:49Z)
• Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data [67.60224656603823]
  Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time. Sleep Activity Recognition constitutes a behavioural marker to portray patients' activity cycles. Mobile passively sensed data captured from smartphones constitute an excellent alternative to profile patients' biorhythm.
  arXiv  Detail & Related papers  (2022-11-08T17:29:40Z)
• Deep Multi-task Learning for Depression Detection and Prediction in Longitudinal Data [50.02223091927777]
  Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally. Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment. We introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks.
  arXiv  Detail & Related papers  (2020-12-05T05:14:14Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.