Examining What Motivates Postsecondary Students with Dyslexia When It Comes to Assessment: It’s About More Than Getting Good Grades
Bibliographic record
Abstract
It is a common assumption that students are motivated by summative assessment. This is often considered in terms of grades, which is an extrinsic motivator and overlooks the wide range of other motivations that students experience with regard to classroom assessment. Indeed, motivation is not a singular construct but can have different qualities and factors impacting it. Of particular interest here are students with learning disabilities, such as dyslexia, who experience a range of challenges in the classroom that can impact their motivation related to summative assessment. To examine a more nuanced understanding of motivation as it relates to assessment, we surveyed 100 post-secondary students with dyslexia and asked them, “What motivates you when it comes to assessment of your learning?” Six themes emerged from their written responses: (a) self-development, (b) performance-based outcomes, (c) future-oriented focus, (d) nature of the task, (e) relationships, and (f) emotions. Recommendations aimed at instructors for supporting student motivation are provided, as well as limitations and directions for future research.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".