Students’ Grit Level as a Predictor of Their Academic Achievement in Engineering Design Courses
Bibliographic record
Abstract
The primary purpose of this study is to explore the relationship between engineering students’ year of study, gender and grit level. This study also aims to assess whether there is any relationship between students’ peer assessment scores in a collaborative project-based learning course and their goal orientation — either towards performance goals or learning goals — and their grit level. The study design is a quasi-experimental design, and the methods used in this study are quantitative. Student grit level was measured using a 12-item scale. The questionnaire was administered in three engineering design courses at different levels of study. The first course is an introduction to engineering design course for firstyear engineering and computer sciences students; the second is an introduction to engineering design course for second-year engineering and computer science students; and the third is a computer-aided design/computer-aidedmanufacturing (CAD/CAM) engineering design capstone course for fourth-year mechanical engineering students. Data collection occurred during the fall semester of 2018- 2019 academic year. Students’ grit level was not found to be a predictor of students’ peer assessment scores, although their goal orientation predicted their level of contribution to their team project.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".