The Perspective of Pre-Service Teachers through Synchronous Learning According to Coaching and Mentoring: SAIFON Guidelines
Bibliographic record
Abstract
This research aims to enhance pre-service teachers' English language teaching ability in the 21st Century toward SAIFON guidelines. The population consists of 40 Rajabhat University pre-service teachers, and the sample group consists of 25 students of fifth-year pre-service teachers. They studied in the second semester of the 2020 academic year. The tools used in the research are 1) Need analysis, 2) In-depth interview, and 3) Field notes. Mixed Method research includes Qualitative Research, In-depth interviews, Field notes, using content analysis and coding techniques for grounded theory, and quantitative research consists of a Need analysis. The results revealed that: Pre-service teachers enhance teaching English ability in the 21st Century toward SAIFON guidelines that consisted of eight aspects: 1) S = Survey the teachers' needs, 2) A= Associating with a plan, 3) I = Instructing teaching strategies, 4) F = Feedback on teaching demonstration, 5) O = Observing teaching in the actual context, and 6) N = Notifying problems and solutions. Furthermore, it included pre-service teachers' perspectives on Synchronous Learning in three aspects: impressions, problems, and solutions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".