Video-based Reflective Practice on Online Teaching: Korean EFL Pre-service Teachers
Bibliographic record
Abstract
The central focus of this study is to explore the impact of video-based reflection on Korean EFL pre-service teachers’ online class practices and their personal growth. This study employed a data-driven qualitative method, involving survey questionnaires, in-depth interview, journal writing based on video reflection, classroom video from 11 pre-service teachers. The main findings in this study indicated that video-based reflection helped preservice teachers in three main ways: (i) to heighten awareness of student responses which enabled teachers to tailor their teaching strategies to better address individual learning needs and interest; (ii) to promote the overall quality of teacher-student interactions in the virtual learning environment; (iii) to rediscover their professional identity as a teacher and increase their confidence in online English teaching. Some important implications from the study include incorporating video-based reflection into teacher education programs to help pre-service teachers develop critical reflection skills and enhance instructional strategies in online teaching environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".