Learning to Hear Students’ Voices: Teachers’ Experiences on Student Mentoring
Bibliographic record
Abstract
In recent years, mentoring practices have become increasingly common in different disciplines. One of these disciplines is education. In this connection, mentoring at the macro level contributes to the education system, while mentoring at the micro level reduces school dropout rates, increases academic success, supports students in their career journeys and protects them against any problems and unhealthy habits. In this context, the objective of this study is to provide an in-depth examination of teachers’ student mentoring experiences in the school context. To this end, phenomenological design, one of the qualitative research methods, was used in this study and the study was carried out with face-to-face interviews. The maximum variation sampling method, one of the purposeful sampling methods, was used to select the participants. A total of 15 teachers selected from different branches formed the study group of the study. The data obtained from the study were transcribed and the thematic analysis method was used to determine the emerging themes and, in this way, a total of five themes were determined: identification, the role of the mentor, the types of mentoring used by the mentor, the mentoring strategies used by the mentor and the problems encountered during the student mentoring process.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| 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".