An analysis of three different approaches to student teacher mentoring and their impact on knowledge generation in practicum settings
Bibliographic record
Abstract
Mentoring in Teacher Education is a key component in the professional development of student teachers. However, little research focuses on the knowledge shared and generated in mentoring conversations. In this paper, we explore the knowledge student teachers articulate in mentoring conversations under three different post-lesson approaches to mentoring: dialogue journaling, regular conferences and stimulated-recall conferences. Propositional discourse analysis identified 4534 propositions that were subsequently classified into four types of knowledge: recalls, appraisals, rules and artefacts along with the precision of arguments therein. Additionally, log-linear analyses were conducted to search for differences among the three mentoring approaches. The results indicate that dialogue journaling demonstrated more appraisals of practice, regular conferences emphasised rules and artefacts, and stimulated-recall favoured more precision in the type of the arguments stated. The three mentoring styles favour different but complementary understandings of practice and point to the impact of various approaches to mentoring on the sort of knowledge shared and generated in post-lesson mentoring conferences.
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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 | 0.018 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".