Online Learning is a Rollercoaster: Postsecondary Students With Learning Disabilities Navigate the COVID-19 Pandemic
Bibliographic record
Abstract
Most of what researchers know about the challenges students with learning disabilities (LDs) experience during postsecondary education is based on experiences during face-to-face learning on campus. Less is known about challenges students with LD face during learning online-the mode of instruction students had to navigate during the COVID-19 pandemic. Therefore, the purpose of our research was to examine the lived experience of undergraduate students with LD during their first full semester of online instruction as a result of the pandemic. We interviewed six students in Western Canada and used a phenomenological approach to analyze their experiences. Overall, we extracted six main themes from their interviews. Two of these themes, (a) the broad impact of having LD and (b) accommodations during COVID-19, were specific to being a student with LD. The remaining four themes were more generally related to their overall student experience: (c) online learning is different, (d) the role of others, (e) emotional impact, and (f) resilience and perseverance. We discuss these results in terms of recommendations for future research and teaching in online learning environments.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".