Closing the Loop: Measuring Entrepreneurial Self-Efficacy to Assess Student Learning Outcomes
Bibliographic record
Abstract
Accredited degree programs primarily use graded assignments in the embedded-course method to measure individual-level assurances of learning (AoL). This method is expensive, subjective, retrospective, and difficult to implement for continuous program improvement. The purpose of our research is to explore the use of entrepreneurial self-efficacy (ESE) as an individual-level AoL outcome to augment the quality management of accredited entrepreneurship degree programs. Previous research on ESE, arising from intention models and theory of planned behavior, has used the construct primarily for predicting start-up intent or differentiating nonentrepreneurs from entrepreneurs. In contrast, we begin from an educational assessment and social cognitive theory perspective in constructing our ESE scale. The new ESE scale is operationalized by theoretically justifying 8 learning outcomes, testing 70 items based on scales in the extant literature, and extracting 11 factors or subdomains of ESE using principal components analysis to create a parsimonious new 44-item ESE scale. Expanding the ESE construct to 11 subdomains also expands the use of ESE into the fields of educational assessment, AoL, and program accreditation. This enables understanding the links between pedagogy, curriculum, assignments, grades, enactive mastery experiences, and peer feedback to achieve meaningful student transformation through self-efficacy beliefs.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".