Exploring STEM in a University Outreach Setting
Bibliographic record
Abstract
STEM literature has proliferated in recent decades. However, few studies consider the setting of university-based STEM outreach programs - programs that negotiate the border of informal and formal learning. In this ethnographic case study, I explore a STEM summer camp at a large Canadian university via two cohorts of learners: (15 elementary school age) campers and (14 undergraduate and graduate student) instructors. The research questions ask: What are the motivations for participating in a university STEM outreach program? How do participants perceive or understand STEM, and its purposes, in a university STEM outreach program? How do participants experience teaching and learning in a university STEM outreach program? Data collection methods included semi-structured interviews, questionnaires, reflections, observations, artifacts, and images. Inductive and deductive analysis are used to identify thematic categories. The findings identify similarities in the interests and aspirations of cohorts that underpin common experiential, preparatory, and learning motivations. Both cohorts are motivated to learn about science and engineering; instructors anticipate teaching. Perceptions of STEM prioritize and conflate science and engineering and view STEM as providing an understanding of the world, affecting change, and serving personal goals. STEM is seen as interdisciplinary, with engineering design informing how it is organized, described, and conveyed. STEM is characterized as hands-on, fun, easy, and broadly transferrable. It is positioned as a reciprocal fund of knowledge; a fund of experience that is built, valued, and leveraged. The facilitation of teaching and learning in the outreach program includes tending to the management of the physical camp space, the relational camp space, and leading hands-on, design-oriented activities. Findings suggest that the outreach program is a site of learning shaped for and by its participants. STEM learning is mediated through engineering design and constructionist approaches that prioritize participant funds of knowledge, in a meaningful third space. This offers a reconceptualization of STEM where engineering is present as content, context, and pedagogy; a perspective that broadens and deepens the connections to be made across practices in outreach, engineering, and education so critical to this work. The thesis concludes with implications and recommendations for theory and practice.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".