Pre-Service Elementary School Teachers Becoming Mathematics Teachers: Their Participation in an Online Professional Community
Bibliographic record
Abstract
This pilot study sought to examine the mathematical knowledge for teaching that pre-service teachers used when participating in an online community, and to gain insight into their epistemological stance. The participants of this study were among the pre-service teachers in a large urban university, chosen as they were completing their mathematics methods course in their teacher-education program and before entering a field experience in the same academic year. A qualitative analysis of the online discussions of our participants was done using Ball and her colleagues’ (2008) framework for mathematical knowledge for teaching and that of communities of practice (Wenger, 1998). These theories provided insight into the development of pre-service teachers as they moved from “student” to “teacher”. Our findings show that pre-service teachers struggle to shed their student-perspective as they transition from theory to practice. This was readily evident in how they used their mathematical knowledge for teaching in their online exchange. Our work contributes to understanding the complexity of becoming a mathematics teacher in elementary school.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 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".