Evaluation of the teaching practice course carried out with the Lesson Study Model
Bibliographic record
Abstract
The purpose of this research was to evaluate the teaching practice process carried out with the lesson study model. In this research “action research” approach was adopted. The study group of the research consisted of four Turkish Language and Literature pre-service teachers. Lesson study was carried out in nine weeks of the teaching practice course. Qualitative data collection techniques such as observation, unstructured focus group interview, and document review were used as data collection techniques. Pre-service teacher course observation forms obtained before the lesson study process, course plans, reflective diary forms, peer observation forms and student opinion forms obtained during the lesson study application process were analyzed with descriptive analysis method. At the end of the lesson study process, focus group interview data and letters written by pre-service teachers were analyzed by content analysis method. At the end of the research, it was seen that pre-service teachers’ perception of teaching profession changed in line with student-centered understanding. It has been determined that pre-service teachers personally develop in terms of multi-faceted thinking, problem solving, self-confidence and patience and also improve professionally on issues such as coping with students, preparing plans, and producing activities. Received: 14 April 2020Accepted: 22 October 2020
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".