How to Do STEM Outreach Evaluation -- Recommendations Based on a Review of Self Evaluation Tools in Canadian STEM Outreach Programs
Bibliographic record
Abstract
STEM (Science, Technology, Engineering, and Mathematics) outreach programs in Canada, especially those oriented towards youth, play a critical role in supporting the nation's future workforce, innovation capacity, and equity across social groups in STEM fields. They constitute a large, multi-layered ecosystem connecting universities, national laboratories, non profit organizations, and grassroots community groups. Despite the growing importance of these programs, and the frequency that they undergo self-evaluation, little systematic information exists on best practices or common approaches to evaluating the effectiveness of STEM outreach initiatives. To address this gap, we integrated literature review with email inquiries about self evaluation tools sent to Canadian STEM outreach programs funded by NSERC (Natural Sciences and Engineering Research Council of Canada) PromoScience grants. We contacted 200 programs and heard back from about 100 of them, for a response rate of 50%. Of those respondents, 68 shared information about a formal self-evaluation tool appropriate for general STEM outreach. The results led us to develop a toolbox of self-evaluation methods, master question banks and starting-point templates for student/participant and teacher/chaperone surveys, and a synthesized list of recommendations for evaluation process, design, and implementation. Our approach provides a broad treatment of how to do STEM outreach evaluation, supplementing the relevant literature, where large-N studies and Canadian studies are relatively rare. We acknowledge that some of the most effective practices in STEM outreach evaluation require resources or capacity (e.g. longitudinal approaches), which may be limited for many outreach practitioners, but others seem to have a high ratio of benefit to cost (e.g. adding qualitative questions to an otherwise quantitative survey).
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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 | 0.230 | 0.284 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.031 | 0.032 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".