Are Students with LD Impacted by Online Learning Similarly to their Peers? An Investigation from an Expectancy Value Theory Lens
Bibliographic record
Abstract
The increased use of online learning in postsecondary education has documented negative impacts for students that may be particularly pronounced for students with learning disabilities (LD). We collected data from 224 postsecondary students with (n = 44) and without LD during the Fall 2020 semester when nearly all post-secondary courses in Canada were being offered exclusively online. Using an Expectancy-Value Theory lens, we examined how students’ expectancy for success, value ascribed to an academic task, and potential costs were related to their satisfaction, academic achievement, and burnout. Moreover, we wanted to determine how students rated courses they completed before the switch to online learning because of the COVID-19 pandemic to their current courses in terms of expectancy, value, and cost. When considering courses completed after the shift to online learning to ones before, students with LD identified that they had lower expectancies to do well, and perceived their courses to have higher costs than their peers without LD. Moreover, for students with LD, academic achievement was associated with higher expectancy and cost, while burnout was also associated with higher cost, but lower expectancy. Ways to support students with LD during online learning are highlighted.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".