Improving Mathematics Learning Through Computational Participation
Bibliographic record
Abstract
Computational Participation (CP) expands upon Computational Thinking (CT) by incorporating themes of problem-solving, creativity, and digital collaboration and communication. In the Fall of 2021, we partnered with two school boards to facilitate Professional Learning (PL) sessions with a broad community of educators and co-facilitated learning sessions with select classroom teachers. Both PL and co-facilitation learning sessions related to curriculum expectations for mathematics and coding. Instead of teaching coding for coding’s sake, our goal was to prepare teachers to use coding to help students understand mathematics under the pedagogical framework of CP. The questions guiding our overall research were to identify ways teachers can integrate CP while teaching mathematics in a meaningful way and identify the various learning opportunities that students gain when CP is integrated. Our research indicated that CP results in learning environments supportive of collaborative learning, communication, increased student engagement, and perseverance. In addition to this, teachers experienced a positive shift in their mindset toward cross-curricular planning. One persistent challenge in infusing digital coding with mathematics in this study was the lack of 1-to-1 technology in classrooms, which could interrupt momentum and disrupt student motivation.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".