Experiential learning in engineering education: A systematic literature review
Bibliographic record
Abstract
Abstract Background The evolving transformations of our society at large, academic institutions, and engineering discipline in the 21st century have profound implications for the nature of experiential learning being offered in engineering education. However, what is experiential learning in the context of engineering education? Purpose The introduction and evaluation of experiential learning in undergraduate engineering education between 1995 and 2020, as well as the essential elements for consideration in its future implementation, have been analyzed and synthesized. Design/Method A population–intervention–comparison–outcome framework and PRISMA flow diagram were used to outline a systematic literature review on how experiential learning was introduced into undergraduate engineering curricula, how it was evaluated, and the essential elements for consideration in its future implementation. Findings A total of 220 studies were synthesized. These studies offered a new lens of seeing experiential learning, which were interpreted as “paradigm shifts.” More than one‐half of the total studies were conducted between 1995 and 2005. These studies were strongly directed at measuring student performance and occurred in a decade when many North American engineering curricula were being restructured. The review indicated that experiential learning has been successfully carried out via diverse methodologies. However, there is a strong need to enrich it with a theoretical basis. Conclusions Experiential learning introduced into engineering education appeared to be an interdependent self – school – community entity. In the changing work environment of the 21st century, heightened by the impacts of the COVID‐19 pandemic, invoking the inseparability of self, school, and community would provide unique perspectives to our evolving understanding of experiential learning and its relevance in engineering discipline.
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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 | 0.019 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".