Mentoring Matters in Workplace: The Impact of Formal Mentoring Program on EFL Instructors’ Performance at ELI, King Abdulaziz University, Saudi Arabia
Bibliographic record
Abstract
An unprecedented acceleration in globalization, cross-culture integration and intensified innovation are a few elements that have triggered the need for availability of mentoring as the professional identity of any institution of higher learning. It has got the status of a foundation stone of mutual accomplishments between universities in the provision of teacher development. Therefore, this research study was carried out to evaluate the experiences of faculty members who participated in a formal mentoring program organized by the English Language Institute (ELI) at King Abdulaziz University (KAU) from 2017 to 2019. In this mixed-method study, a questionnaire and semi-structured interviews were used to gather data in order to respond to questions connected to the effectiveness of the mentoring program for mentors and mentees. The study particularly sought to discover the character of the work issues discussed and the worth judged by participants as emerging from their contribution to a programmed mentoring correlation. Data analysis transpired that mentoring promoted all of those who participated in the program. The study concluded that mentoring could assist in constructing capability in two ways: featured and standardized mentoring of trainee teachers through overt mentoring practices, and demonstrating and deconstructing teaching methods and practices for mentors' pedagogical progression. This study emphasizes the worth and value of the formal mentoring program as a valued and fitting professional development approach.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".