Effect of Online Learning on Mental Health and Academic Outcomes of Students with Intellectual Disabilities in Higher Education
Notice bibliographique
Résumé
The COVID-19 pandemic shift to online learning has raised concerns regarding students’ mental health and academic performance, particularly for students with intellectual disabilities. Objective: This paper examines the effects of online learning on stress, anxiety, and social isolation and those factors on academic performance, Grade Point Average (GPA), and participation in online learning and engagement, particularly for students with intellectual disabilities (IDs). Methods: The current study employed a quasi-experimental research design and targeted 500 participants, comprising both undergraduate and postgraduate students. Of these, 50 participants were identified as having intellectual disabilities (IDs) through self-reporting and institutional records. The remaining 450 participants were typically developing students selected through stratified random sampling to ensure proportional representation across academic levels and disciplines. The Perceived Stress Scale (PSS), Generalized Anxiety Disorder-7 (GAD-7), and UCLA Loneliness Scale were adopted from validated and widely used psychometric tools in mental health research. These instruments have been previously validated for reliability and applicability across diverse populations. Multiple linear regression and Pearson correlation coefficients (PPMC), which help identify associations and control for confounding factors, were used to examine the relationships and potential predictive effects between mental health variables and learning outcomes. Pearson correlation coefficients were utilized to analyze the linear relationships between mental health variables (stress, anxiety, and social isolation) and academic performance (GPA). Additionally, multiple linear regression analysis was conducted to predict the impact of these mental health variables on academic performance while controlling for confounding factors such as age, gender, and degree level. Results: Participants with IDs reported significantly higher levels of stress (PSS, M = 25.8), anxiety (GAD-7, M = 12.5), and social isolation (UCLA, M = 48.6) compared to the control group. Mental health variables had a significant negative relationship with GPA, with stress having a correlation coefficient of -0.51 and anxiety having a correlation coefficient of -0.48. In regression analysis, stress was found to have the largest effect on the outcome of GPA, seconded by anxiety and then social isolation. Conclusion: A direct impact of mental health on learning is observed, particularly for students with IDs, implying the necessity of developing an individual mental health promotion program and ways of creating more effective online learning for students with IDs that help alleviate stress, anxiety, and isolation.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».