Using Robotics to Support the Acquisition of STEM and 21st-Century Competencies: Promising (and Practical) Directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To enhance how educators use robotics to support the development of STEM and 21st century competencies, we report findings from focus groups and interviews with 133 elementary teachers and 46 elementary students, 19 video-recorded classroom observations, and a teacher survey from Ontario, Canada. We find that teachers use robotics in a variety of ways to support the development of cognitive, interpersonal, and intrapersonal skills. Despite the potential benefits, our participants identified several factors that limit the adoption of robotics teaching and learning on a wider scale, including insufficient curriculum and assessment integration, resources, and professional development and support. We provide practical policy guidelines to support the broader integration of robotics and reflect on how these recommendations may inform teaching and learning in a (post-) COVID-19 classroom.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it