Educational Robotics and Preservice Teachers: STEM Problem-Solving Skills and Self-Efficacy to Teach
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
Integrating STEM education within the elementary school science curriculum in Ontario, Canada, elevated the expectation for elementary preservice teachers to teach STEM skills such as problem-solving through coding. Research shows that educational robotics can promote STEM knowledge and skills. This mixed methods study investigates the effect of an educational robotics intervention on preservice teachers’ STEM problem-solving skills and their self-efficacy to teach with educational robotics during the COVID-19 pandemic. Data sources included a pre- and postquestionnaire on problem-solving, a pre- and post- self-efficacy teaching questionnaire, a problem-solving worksheet, and transcripts of group interactions. Quantitative findings were statistically significant for preservice teachers’ self-efficacy to teach with educational robotics (large effect size) and for problem-solving competencies (small effect size). Using a STEM problem-solving framework, two preservice teacher group interactions were analysed. Qualitative findings indicated that preservice teachers exhibited similar problem-solving processes as STEM experts, but preservice teachers’ prior STEM knowledge limited the types of decisions considered at the problem-solving stages. The study provides an example of how preservice teachers’ self-efficacy to teach with educational robotics was developed within a science education course and lends unique insights into the problem-solving processes these preservice teacher groups engaged in.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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