Constructivism as a framework for literacy teacher education courses: the cases of six literacy teacher educators
Bibliographic record
Abstract
This paper presents findings from the large-scale study Literacy Teacher Educators: Their Backgrounds, Visions, and Practices that includes 28 literacy/English teacher educators (LTEs) from four countries. The participants were interviewed three times and shared their course outlines. Six pre-service LTEs who use a constructivist approach are presented. The six LTEs speak English as their mother tongue. Three aspects of constructivism are discussed: knowledge is constructed by learners; knowledge is experience based; and a strong class community is essential. They have adopted a constructivist approach because they conceptualised the teaching/learning process as a partnership. Constructivism is a flexible and fluid framework so individual LTEs can shape their work for their context and draw on their strengths; however, it is demanding because courses have to be somewhat organic in order to create space for discussion of issues as they arise.
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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 | 0.026 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.042 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.010 |
| 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".