The Relationship Between Interpersonal Approaches of Thesis Supervisors and Graduate Student Satisfaction
Bibliographic record
Abstract
The purpose of this explanatory mixed method study is to describe students’ perceptions of the thesis supervision approaches used, their satisfaction with these approaches, and whether their satisfaction differed based on students’ gender, degree sought, and concentration. The study comprised two parts. First, a questionnaire was distributed to all graduate students who had written a thesis/dissertation during 2015-2017 (N=213) at one of the universities in the United Arab Emirates. Second, a group of students from among those mentioned above (N=16) were interviewed. The study revealed that the most commonly used approach by the supervisors was the collaborative interpersonal approach and the least used one was the directive informational approach. There was no significant difference according to the degree sought and concentration. However, when it came to gender, female students believed that the supervisors had used the collaborative approach more than the male students. Overall, graduate students were satisfied with their supervisors’ approaches, while some were highly satisfied. The findings indicated a pattern where the more collaborative the supervisor was, the more satisfied the student became and the more the supervisor used the non-directive interpersonal approach, the less satisfied the students became. The study recommends that faculty supervisors attend to the various needs and preferences of their students and be ready to shift away from their preferred approach to suit the diverse needs and abilities of their students.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 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".