Classroom practice and craft knowledge in teaching mathematics using Desmos: challenges and strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it