Exploring how learning by ‘talking and doing’ supports flourishing in S.T.E.M for elementary students
Bibliographic record
Abstract
Over the past three decades, researchers have increasingly advocated for pedagogical practices that privilege exploration, collaboration, problem-solving, and hands-on projects in K-12 Science, Technology, Engineering, and Mathematics (S.T.E.M.). Many researchers have studied the efficacy of these instructional practices, but there has been relatively little research exploring how learning by ‘talking and doing’ influences students’ affective relationship with S.T.E.M. With a growing need in society for a S.T.E.M. workforce, it is vital that students develop positive relationships with S.T.E.M. The purpose of this study is to explore how learning by ‘talking and doing’ might influence elementary students’ flourishing in S.T.E.M. In particular, we ask the following research question: How does a yearlong S.T.E.M. initiative that centralizes learning by ‘talking and doing’ influence elementary students’ flourishing in S.T.E.M? The participants were 50 elementary students (Grades 3, 4, 5, and 6) in a high-need elementary school in Eastern Canada. Students engaged in a yearlong intervention that emphasized learning by ‘talking and doing’. Using a mixed methods design, we measured students’ flourishing in S.T.E.M. via pre-/post-surveys and focus group interviews. Pre-/post-survey analyses indicated that the initiative had a statistically significant positive influence on students’ flourishing in science and STEM (general). Focus group interviews complemented and confirmed the survey analyses. The findings promote continued dialogue regarding students’ wellbeing in S.T.E.M. as an important outcome of interest when considering the efficacy of instructional practices.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".