Evaluation of a Work-Integrated Learning Program for Undergraduate STEM Outreach Instructors
Bibliographic record
Abstract
Evaluation of a Work-Integrated Learning Program for Undergraduate STEM Outreach InstructorsThis paper describes and evaluates a comprehensive work-integrated learning program, developed and delivered by Actua, a Canadian National STEM organization.The program provides instructors with a variety of opportunities to improve their skills, career readiness, and their employer connections and networks.The program consisted of four sets of activities: (1) A set of skills-focused training modules to prepare participants for their more immediate STEM outreach work and longer-term work readiness;(2) Industry-Led Activities and Micro-experiences;(3) Short-term internships with industry partners; and (4) a Micro-Credentials pilot program in professional communications.The programs were evaluated using a comprehensive participant survey, alongside initiative-specific surveys and interviews to gather more precise feedback.Program evaluation demonstrated a strong positive impact on the professional skills, knowledge, confidence and workforce readiness of participating post-secondary students.This program provides a novel approach for work-integrated learning, in that it places more emphasis on the employment experience than is often the case in WIL programs -that is, it focuses as much on providing learning that enhances the work experience as it does on providing work experiences that enhance the learning experience.The paper draws from social cognitive career theory and identity trajectory theory to support the evaluation of work-integrated learning programming.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".