A Synthesis of Best Practices for Engineering Skill Self-Efficacy Measures: Towards Improved Evaluation of Computer-Aided Design Education
Bibliographic record
Abstract
Self-efficacy is a concept that refers to one’s belief in one’s ability to complete tasks and achieve goals, and literature has shown it is correlated to student retention and success in engineering education settings. Task-specific self-efficacy measures can be used in engineering contexts to evaluate student confidence in specific skills, which educators can use to evaluate learning impacts in their classrooms. This work seeks to support the creation of these tools by presenting a structured literature review consolidating existing work on the creation of skill-specific self-efficacy measures, predominantly within engineering. An example of how instructors might use these learnings is then provided by explaining application of these findings in the context of the creation of a Computer-Aided Design self-efficacy measure. By summarizing key learnings around the development of engineering skill-specific self-efficacy measures, we hope to enable engineering education researchers and educators to conduct more comprehensive evaluation of educational interventions.
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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 | 0.160 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.024 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".