Classroom practice and craft knowledge in teaching mathematics using Desmos: challenges and strategies
Bibliographic record
Abstract
While traditional graphing calculators have become commonplace in high school mathematics classrooms, newer and more powerful connected graphing packages (CGP) are less pervasive. This study observes how four teachers integrate a CGP into their high school mathematics classrooms, with a focus on the challenges the teachers faced and the corresponding strategies they put into practice to deal with those challenges. Ruthven’s Structuring Features of Classroom Practice framework is used to frame reported data and to identify craft knowledge. Overall, it is concluded that the relevant craft knowledge was developed by these teachers over time based on practice, rather than formal training, and is unique to classroom context. It is argued that the types of challenges faced by each of the teachers were dependent on their particular expertize. Those with more experience with integration of the CGP in action displayed a more fluent and problem-free practice. The strategies reported in this study are also thought to be helpful to teachers and researchers, and potentially supportive of future integration of CGPs.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".