First-Year Students in Experiential Learning in Engineering Education: A Systematic Literature Review
Bibliographic record
Abstract
This complete theory paper is a literature review that outlines the introduction of experiential learning in undergraduate engineering education.Using a population-intervention-comparison framework and PRISMA flow diagram, we investigated how experiential learning was implemented in undergraduate engineering education between 1995-2020.This paper is part of a larger review highlighting engineering education research findings that apply to the first-year experience.From a total of 220 studies that were synthesized, 45 studies purely involved first-year students and 39 studies pertained to a combination of first-year students and second-year to fourth-year students.These 84 studies examined what students learned in their first-year and addressed the nature of preparation and composition of students entering engineering.Experiential learning was mostly measured through the lens of student performance (89%) through different forms of evaluations including performance checks, surveys, and individual interviews.A second lens was faculty evaluations (7%) including instructors' observations, feedback, and reflections of students' performance and experience.Finally, a third lens was industry feedback (4%), obtained to inform capstone design courses where students work at industrial sites on company based projects with industry mentors.From our literature survey, we identified four key elements with corresponding insights that described successful implementation of experiential learning that might serve as consideration for future implementation for engineering educators and researchers.These four key insights include: 1.) Relevance and collaboration with stakeholders, students, academe, industry, and society, 2.) Students engagement and ownership, 3.) Scaffolding and integration across levels, and 4.) Importance of assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".