Three essays on the motivational effects of grading in higher education
Bibliographic record
Abstract
List of Tables 3.11 Balance on observables between initially failing participants who are more likely to have a fixed vs. growth mindset . . . . . . . . . . . . . . . . . . .107 3.12 Balance on observables between initially excessively successful participants who are more likely to have a fixed vs. growth mindset . . . . . . . . . . .108 3.13 Differences in average second-period effort and performance between initially excessively successful participants who are more likely to have a fixed vs. growth mindset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .109 1 E.g.Hossain & Tsigaris (2015) having surveyed 169 Canadian undergraduates enrolled in a second-year course, reveal that the midterm grades do help the students reduce their overconfidence and form more accurate expectations.However, Stinebrickner & Stinebrickner (2012) attribute as much as 40% of early college dropouts to the students' learning about their grade performance or academic ability.2 E.g.Kohn (1999) suggested several reasons for that: "[the grades'] hidden punitive side, their effect on relationships, their failure to uncover and deal with the source of the problem, their tendency to discourage risk-taking, and their long-term negative effect on intrinsic motivation".
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".