JALT2014 Plenary Speaker article: An invitation to reflect on practice
Bibliographic record
Abstract
Reflective practice is now a common term in many teacher education and development programs worldwide. Although definitions of reflective practice may vary in different programs, it generally means teachers systematically collect information about their classroom happenings, and then analyze and evaluate this information and compare it to their underlying assumptions and beliefs so that they can make changes and improvements to their teaching. This paper outlines what reflective practice is, why it is important, and how teachers can reflect. 内省の実践（意識的に深く考え注意深く観察する）というのは世界中の多くの教員養成課程で使われている一般的な用語である。内省の実践の定義は、それぞれの教科課程で異なっているかもしれないが、一般的には、授業の向上と工夫のために、教師がクラス内の情報を体系的に集め、この情報を分析・評価し、自分達の教育の前提や信条と比較する、ということを意味している。本論では内省の実践とは何か、なぜそれが重要なのか、どのように教師は授業に反映できるかについて要点を述べる。
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.148 | 0.088 |
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".