FUTURE SKILL DEVELOPMENT IN UNDERGRADUATE STUDENTS THROUGH WORK IN STEM OUTREACH
Bibliographic record
Abstract
This paper represents the experience and self-reported skill development of undergraduate Science and Engineering outreach instructors, who were working primarily online during the global pandemic in 2020. This work is part of a larger multi-year project designed to articulate the learning and employability skills gained by a pan-Canadian group of undergraduates, by way of theirtraining and work experience as youth program Instructors delivering STEM outreach activities for youth. The development of these skills was measured using a post-program survey, in which undergraduate instructors were asked a number of questions about their skill development. Instructors noted development most significantly in (1) teamwork and collaboration; (2) adaptability and flexibility: (3) communication, (4) leadership, (5) innovation and creativity, and (6)initiative. A significant theme noted was the learning that took place from the sudden shift to teaching remotely and working through a pandemic. Although the focus of STEM Outreach research & evaluation is often on the impact of the program on its participants, this work demonstrates the value of the instructor experience, and how this work can leverage other post-secondary initiatives designed to prepare undergraduates for their careers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".