Sticking together in a world full of sharks: pre-service teachers’ perspectives of mathematics content courses
Bibliographic record
Abstract
Secondary mathematics pre-service teachers (PSTs) are required to take university-level mathematics content courses to develop their mathematical content knowledge. Although PSTs’ experiences as students play a major role in the types of teachers they become, there is limited research investigating the experiences of PSTs engaging in these courses. Thus, our study used a series of semi-structured interviews to provide first-hand accounts of PSTs’ experiences. Findings suggest that PSTs experienced a range of challenges, including difficulties connecting with and understanding course content, and being ignored and dismissed by mathematics instructors. To cope with these challenges, PSTs became reflective practitioners and considered how their experiences in these courses applied to their learning as future teachers. PSTs also developed a community with each other that grew out of needing support with mathematical content, but evolved into collegial friendships. While PSTs were able to find positive features within negative experiences, this study highlights the need to understand PSTs’ experiences in these courses so that effective improvements can be made.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| 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".