The impact of grades on student motivation
Bibliographic record
Abstract
Although research has explored how in-class pedagogical practices and narrative feedback affect student engagement and motivation, questions remain on the impact of grading systems (i.e. multi-interval grades vs pass/fail and narrative evaluation) on academic motivation. Here, we compared the motivation of students who received multi-interval grades to students who were evaluated with a pass/fail and end of course narrative evaluation. In addition, we compared academic motivation at institutions with different grading systems. Grades did not enhance academic motivation. Instead, grades enhanced anxiety and avoidance of challenging courses. In contrast, narrative evaluations supported basic psychological needs and enhanced motivation by providing actionable feedback, promoting trust between instructors and students and cooperation amongst students. Even when accounting for potential confounding factors, students in universities that used narrative evaluations experienced higher intrinsic and autonomous motivation compared to students who received multi-interval grades. Given the potential for grades to thwart basic psychological needs and academic motivation, institutions should re-evaluate when and in which programs grades may be appropriate or necessary.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".