Enhancing Undergraduate Materials Science Labs for Experiential Learning
Bibliographic record
Abstract
Recently, there has been increased pressure from industry, the local government, and the University of Calgary to include industry-relevant learning opportunities in undergraduate curricula to improve the transition of students from the university to the workforce.In engineering education, laboratories are often viewed as a bridge between course content and industry skills by grounding theoretical knowledge in practical experiments and developing familiarity with testing techniques and analyses used in industry.Yet nearly half of undergraduate mechanical and manufacturing engineering students enrolled in a mandatory third-year materials science course at the University of Calgary report no link between their laboratories and course content or future career development.Therefore the goal of this research endeavour is to identify actions that can be taken to improve the students' learning experience in undergraduate engineering laboratories.Critically reflective surveys were developed using Ash and Clayton's Describe, Examine, Articulate Learning (DEAL) model and the revised Bloom's taxonomy and released to current engineering students in a third-year materials science course at the University of Calgary's Mechanical and Manufacturing Engineering program.The purpose of these surveys was to evaluate where students feel their laboratories do not connect to their classes or careers, and what steps can be taken to improve learning outcomes.Following completion, these survey responses were evaluated using qualitative content analysis.Results indicate that students better perceive their laboratory learning experience when it includes a hands-on component designed using Kolb's experiential learning cycle.Students feel their time is better spent when learning outcomes are aligned with student abilities, laboratory material is clearly linked to industrial applications, and expectations and theory is clearly communicated.Group work and a design component, when relevant, can also improve this experience.The experience of the laboratory assessment can be improved by considering the purpose of the assessment when designing it.Finally, it was found that facilitators have a strong impact on the learning experience and should receive training to ensure consistency and that learning objectives are met.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".