Bibliographic record
Abstract
Grit, defined as long-term focus and perseverance, has been proposed as a distinct personality trait that can help to predict success and achievement in challenging endeavours. In this work, we explore the concept of grit in computer science (CS) contexts and whether it has potential value to contribute to predictions of success. To validate the use of the scale in a CS context, we issued Duckworth's 12-item grit scale to 597 CS1 students. We confirm, using a factor analysis, that the items in the scale largely load as expected to two factors. We also evaluate the use of grit as a predictor and find little correlation between overall grit score and final course mark. However, we see a weak correlation between one component of grit -- perseverance of effort -- and final mark. These results suggest that grit, as a construct, may not have the same predictive power in CS1 as in other contexts but that perseverance in the face of difficulty contributes to success in CS1 and is a target of interest for further study.
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) | 0.001 | 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 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".