Grit, motivation and university grades
Bibliographic record
Abstract
Dataset underpins an article published on 'South African Journal of Economics', 2022, vol. 90, no.1, pp. 1-120. Many South African university students either do not complete their degrees or take a prolonged time to meet the minimum degree requirements, with significant cost implications. Identifying malleable drivers of academic success is an important starting point in designing policies and programmes to improve student outcomes. To this end, we assess grit and intrinsic motivation as possible predictors of academic success, where motivation type is coded using text analysis of open-ended responses. We also investigate interactions between these traits. In line with existing literature, mostly in the United States and Canada, our results show that higher levels of grit are related to higher grades among Economic and Management Science students in a South African University. We further note intrinsic motivation as a significant predictor of grit levels. Our findings suggest that grade outcomes might be improved by interventions focusing on building grittinessin students.<br>Data and stata dofile are attached.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.137 | 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".