Utilizing Reflective Teaching and Text Models for Increasing Students’ Participation and Achievement in Writing Class
Bibliographic record
Abstract
Reflective Teaching approach has been addressed to give beneficial contribution to learning achievement and learning attitude. Studies on Reflective Teaching Approach assure teachers to utilize the approach in their teaching and process. Dealing with the above consideration, the purpose of the study was to explore the impact of the implementation of reflective teaching combined with the use of text models for fostering students’ participation and achievement in writing. This study employed a mixed-method approach by incorporating qualitative and quantitative research. The study used observation and interviews with 20 English as a Foreign Language students to investigate the impact of practicing reflective teaching by the teacher on students’ participation. Additionally, Pre and Post-tests were applied to reveal whether the use of text models applied by the teacher could increase student achievement in writing. The results indicated the significant impact of the use of reflective teaching combined with text models on both students’ participation and achievement in writing. Therefore, the study recommends the utilization of reflective teaching combined with text models in writing courses.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".