The Role of Gender and Confidence in Pre-Service Teachers’ Computational Thinking Skills in an Undergraduate Introductory Educational Technology Course (Learning Sciences)
Bibliographic record
Abstract
Computational thinking (CT) is one of the most important competencies of the 21 st century. However, there are very few CT assessments in the literature and even fewer are validated empirically. Also, it is not known yet how pre-service teachers’ CT skills relate to their confidence and gender, as research indicates the importance of role-models in peaking students’ interest in coding skills and retaining them in Science, Technology, Engineering, and Mathematics careers. This research aims to assess whether there are any gender differences in 21 st -century CT skills and confidence of pre-service teachers in an introductory educational technology course at a large university in Western Canada. At the beginning of the course, n = 94 pre-service teachers answered a questionnaire that included a subset of 15 items from a validated assessment of CT skills, CTt. Results show that pre-service teachers’ CTt performance correlated with their confidence in their performance completing the overall test. Although there were no differences in pre-service teachers’ CTt performance across gender, males were significantly more confident about how they performed on the overall test than females, confirming prior research results. Implications include interventions for improving the confidence of female pre-service teachers to commensurate with their performance.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".