Educational Robotics and Preservice Teachers: STEM Problem-Solving Skills and Self-Efficacy to Teach
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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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".