Mentoring Experience at University Level: EFL Mentees' Perceptions and Suggestions
Bibliographic record
Abstract
The research paper aims to investigate EFL mentees' perceptions of mentoring experience in one of the Higher Education institutes in the Kingdom of Saudi Arabia in the Academic years 2019 and 2020. The mentoring experience includes four domains: the mentorship program, mentors' roles and responsibilities, benefits of mentoring, and barriers to mentoring. The researcher attempted to answer two questions. The first explores the mentees' perceptions of the mentoring experience, and the second question attempts to find out the mentees' suggestions to make the mentoring experience more effective. A questionnaire of 4- Likert scale was used to collect data from thirty-three newly recruited instructors to answer the first question. The mentees were also asked to add their suggestions for the betterment of mentoring experiences as an answer to the second question. Descriptive statistics in the form of means, standard deviation, and percentages were used to analyze the collected data. The findings revealed that the mentees were satisfied with the mentoring experience. They have a positive attitude towards the mentorship program and the mentor's roles and responsibilities. However, a few mentees have certain concerns regarding some factors related to their mentoring experience. These were highlighted and discussed under each domain. In addition, the mentees had a number of suggestions that would contribute to having a more effective mentoring experience. Based on the findings, the researcher presented a number of conclusions and recommendations.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".