Identifying and supporting reflection in pre-service teacher education a rubric fit for purpose
Bibliographic record
Abstract
It is widely accepted that reflection and reflective practices are necessary components of teacher education programmes. However, these are not always explored or even fully understood by the very people who are expected to reflect – the student-teachers themselves. Support for these practices is therefore required so that students may understand what reflection is and helped to become reflective practitioners. This article describes the construction and use of a rubric to identify and support reflection in pre-service foreign language teacher education. It begins by reviewing key literature on reflection before moving on to a study of types of reflection and conceptual frameworks in the literature. Four types of reflection were identified and attributed labels: Type 0. Descriptive/behavioural; Type 1. Descriptive/analytical; Type 2. Dialogic/interpretative; and Type 3. Critical/transformatory. These were then used to construct the rubric which incorporated a matrix of five categories: discourse; rationale; level of inquiry; orientation to self; and views of teaching. Examples of how the rubric has been used to both capture and support reflection are provided. It is not a stand-alone tool, but one which is situated within a reflective practice model of teacher education which requires reflexive teacher educators, tools and practices which provide opportunities for reflection. It is flexible enough to allow teacher educators (and students) to identify types of reflection in spoken or written accounts, which may help when self-assessing, giving feedback, and supporting the momentum of reflection during a course or practicum.
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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 | 0.024 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".