A systematic literature review on Engineering Entrepreneurship Education Learning Outcomes and Assessment Tools
Bibliographic record
Abstract
Recent technological advancement is moving our society towards a more innovative and entrepreneurial one. As a result, engineering entrepreneurship education gained popularity and adoption across major education institutes worldwide. Based on a national survey done by Industry Canada, over 98% of Canadian post-secondary institutes offered at least one course in entrepreneurship. Despite this wide adoption, we believe the research on the short-term learning outcomes and assessment for engineering entrepreneur education on students is inadequate. This is often because of the lack of research in the engineering entrepreneurship and the lack of definition of engineering entrepreneurship education learning outcomes. We undertook a systematic review and meta-analysis of 123 studies on entrepreneurship education for undergraduate engineering students in North America in the past 15 years. We examined the learning outcomes defined and desired by major entrepreneurial educational institutes in North America and the assessment methods employed to measure the student learning outcomes. We particularly focused on the alignment between desired learning outcomes and assessment methods employed to study the validity and reliability of common assessment instruments. In this paper, we report on the results of the systematic literature review, identify the strength of common assessment instruments, and then describe the process we incorporate what we learned from this review into our engineering entrepreneurship education program.
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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 | 0.036 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.027 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".