A Study of Preservice Teachers Developing Teaching Competencies with VEX GO Robotics
Bibliographic record
Abstract
There is global impetus to include the learning of STEM skills across k-12 school curricula to keep abreast of changing occupational, economic and societal needs. The province of Ontario, Canada implemented a revised Grades 1-8: Science and Technology curriculum in 2022 that emphasises STEM learning across the elementary grades. However, majority of elementary teachers in Canada are generalists – they are expected to teach all subjects such as language, science, mathematics, and the arts. In this type of teacher education context, where majority of the elementary preservice teachers (PTs) do not have undergraduate degrees in the STEM disciplines, there is need to provide them with STEM experiences to develop their STEM knowledge and skills and their teaching competencies for elementary school STEM teaching. The literature shows that educational robotics (ER) can develop STEM skills in k-12 students. This paper reports on a study that examined how middle-school preservice teachers develop confidence and knowledge to teach about coding with VEX GO robotics. Data sources for n = 50 preservice teachers included a pre-questionnaire on prior knowledge, a pre- and post-questionnaire on confidence to teach with robotics, and a worksheet to guide activities and record coding solutions. Preservice teacher (PT) participants volunteered to participate and signed a consent form, approved by the university research ethics board. The quantitative data were analysed with SPSS version 29. The results were statistically significant for the effect of the robotics intervention on PTs’ confidence about their competencies to use robotics in teaching and learning of middle school science and a large effect size was observed. The findings also revealed that PTs’ participation in the robotics activity resulted in a gain in their reported knowledge about robotics to integrate in teaching and learning. The results inform the design of instructional experiences in Teacher Education courses to improve elementary preservice teachers’ self-efficacy and competencies to teach with robotics in classrooms and also provide insights into the design of ER learning experiences for elementary school contexts.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".