Understanding the delivery of a Canadian-based after-school STEM program: a case study
Bibliographic record
Abstract
BACKGROUND: Due to the rising demands for a Canadian workforce with science, technology, engineering, and math (STEM)-related education, there is a need to increase youth engagement in STEM education and programming. Research, however, has shown that youth residing in low-income communities are disproportionately affected by psychosocial barriers, which often inhibit meaningful engagement in STEM programming. Visions of Science Network for Learning (VoSNL) was designed and implemented to address these existing barriers. VoSNL is a charitable organization in Southern Ontario, Canada, that provides weekly community-based STEM programming to low-income and marginalized youth during out-of-school time. VoSNL programming is delivered directly within the community and is free-of-charge for all youth in order to minimize barriers of physical and financial accessibility. The purpose of this report was to provide a detailed description of a core program within VoSNL-Community Science Clubs-and summarize the findings of a process evaluation, specifically the successes and challenges of implementing a community-based, out-of-school STEM program. RESULTS: Program successes are outlined along with the challenges that have been identified through program implementation. Successes include (a) delivering the program within a community context, (b) opportunities for consistent engagement, and (c) establishing positive youth-staff relationships. Challenges include (a) navigating community-based issues, (b) conducting outreach and promotion, and (c) accommodating a wide age range of youth. Further, lessons learned from an evaluation of program implementation are also discussed. CONCLUSIONS: This report provides one of the first program descriptions and process evaluations of a community-based, youth-focused STEM program within a Canadian context. The findings in this report have helped to improve the delivery and evaluation of the VoSNL program and may act as a catalyst for program expansion to reach more youth in marginalized communities. Further, the findings can also provide a strong framework for programmers interested in implementing STEM youth programming in a community context, assist in the replication of similar models in other locations, and enhance STEM learning amongst youth.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.030 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".