Impact of Online Learning in the Context of COVID-19 on Undergraduates with Disabilities and Mental Health Concerns
Bibliographic record
Abstract
The COVID-19 pandemic upended college education and the experiences of students due to the rapid and uneven shift to online learning. This study examined the experiences of students with disabilities with online learning, with a consideration of surrounding stressors such as financial pressures. In a mixed method approach, we compared 28 undergraduate students with disabilities (including mental health concerns) to their peers during 2020, to assess differences and similarities in their educational concerns, stress levels, and COVID-19-related adversities. We found that students with disabilities entered the Spring quarter of 2020 with significantly higher concerns about classes going online, and reported more recent negative life events than other students. These differences between the two groups diminished 3 months later with the exception of recent negative life events. For a fuller understanding of students’ experiences, we conducted qualitative analysis of open-ended interviews. We examined both positive and negative experiences with online learning among students with disabilities and mental health concerns. We describe how online learning enabled greater access—e.g., reducing the need for travel to campus—alongside ways in which online learning impeded academic engagement—e.g., reducing interpersonal interaction. We highlight a need for learning systems to meet the diverse and dynamic needs of students with disabilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| 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".