Teacher Candidates’ Key Understandings about Computational Thinking in Mathematics and Science Education
Bibliographic record
Abstract
With the increasing advocacy for CT integration in K-12 education, it is important to consider how teacher education programs could better prepare teacher candidates (TCs). At the Faculty of Education at Western University, CT has been included in the curriculum as part of the teacher education program through the course Computational Thinking in Mathematics and Science Education, oriented to Intermediate/Senior (Grades 7 to 12) preservice teachers. In this paper, we describe the case study of the 2017 cohort of the CT course. We aimed to answer the question: What key understandings about CT did teacher candidates develop through their participation in the course? We found that TCs in our course developed a better understanding of: (1) CT connections to the real world, as well as lesson ideas and pedagogical examples to integrate CT in the context of mathematics and science; (2) the different affordances of CT integration; (3) the use of several technologies to implement CT integration; and (4) what CT is, as well as the set of skills that contribute to its development.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".