The return to university after fieldwork: toward disrupting practice-theory challenges identified by mathematics teacher educators
Bibliographic record
Abstract
In this paper, we present on a research study that was framed in disruptive pedagogy (DP) to examine the post-field context of mathematics teacher educators’ (MTEs’) practices. We open by referring to common challenges discussed in the literature related to theory-practice transitions of prospective teachers (PTs) as they move from university courses to their field placement. After reviewing these challenges, we then shift our focus toward understanding what MTEs see as challenges in the post-field context of teacher education programs; that is, what practice-theory challenges are identified by MTEs as PTs make the transition from field back to university. Briefly, our thematic analysis suggests that, in the post-field context of teacher education programs, MTEs are challenged by organizational issues and institutional structures; by PTs’ return to the university armed with superficial placement stories and unexamined indicators of “good mathematics teaching”; and by the significant emphasis PTs place on mentor voices and ways of teaching, often including unfavorable views on the value of reform teaching. Simply put, MTEs expressed being challenged by PTs’ skepticism, resistance, and lack of conviction toward the role of the university. Additionally, MTEs reported being challenged by their own feelings of resignation that our analysis suggests stems from a growing list of challenges which can result in some MTEs stepping down and settling on a pragmatic approach to their post-field mathematics teaching. To close, implications for MTEs are discussed by pointing specifically to the potential of DP for unpacking practice-theory transitions and considering the creation of a post-field third space.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".