Supervisor-Student Research Meetings: A Case Study on Choice of Tools and Practices in Computer Science
Bibliographic record
Abstract
Supervisory meetings are a crucial aspect of graduate studies and have a strong impact on the success of research and supervisor-student relations, yet there is little research on supporting this relationship and even less on understanding the nature of this collaboration and user requirements. Thus, we conducted an exploratory study on the choice and success of tools and practices used by supervisors and students for meetings, for the purpose of making informed design recommendations. Results of a series of five focus groups and three individual interviews yielded three themes on: 1) supervisory style diversity, 2) distributed cognition demands, and 3) feedback channel dissonance. Student-supervisor collaboration has many unexplored areas for design and as a first step our work highlights potential areas for supportive designs and future research.
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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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".