The Development of Coaching and Mentoring Skills Through the GROW Technique for Student Teachers
Bibliographic record
Abstract
The purposes of this research were to develop coaching and mentoring skills through the GROW technique for the student teachers studying at the Faculty of Education, Ubon Ratchathani Rajabhat University, to study the students’ coaching and mentoring behaviors, to compare the students’ coaching and mentoring concepts before and after the study, and to compare the students’ learning achievement on the course of learning organization before and after the study. The sample consisted of 26 juniors studying in the first semester of academic year 2013, gained by cluster sampling. The instruments included a performance test, a behavior observation form of check-list type, a test of coaching and mentoring concepts, and an achievement test. The collected data were analyzed by using percentage, mean, standard deviation, and t-test. The findings revealed that the students’ coaching and mentoring skills were positive at the percentage of 65.00, the students’ coaching and mentoring behaviors were positive at the percentage of 53.00, the students’ coaching and mentoring concepts after the study were significantly higher than those before the study at the .01 level, and the students’ achievement after the study was significantly higher than that before the study at the .01 level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".