Effect of Online Learning on Mental Health and Academic Outcomes of Students with Intellectual Disabilities in Higher Education
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".