The Agile Approach with Doctoral Dissertation Supervision
Bibliographic record
Abstract
Several research findings conclude that many doctoral students fail to complete their studies within the allowable time frame, in part because of problems related to the research and supervision process. Surveys show that most doctoral students are generally satisfied with their dissertation supervision. However, these surveys also reveal some students think their supervisors meet with them too infrequently, lack interest in their dissertation topics, and provide insufficient practical assistance. Furthermore, many countries will soon witness a large turnover in the labour market as people near retirement. Because this is also the case at many universities and colleges, the expectation is that there will be many teaching and research vacancies. Therefore, many new doctoral students who plan to enter academia after earning their doctoral degrees are needed. In responding to these complaints, this conceptual paper examines the use of the agile approach–which has achieved recognition and approval in software development–in the doctoral dissertation process. In the teaching/learning sphere, the agile approach can be used in iterative meetings between doctoral student and supervisor for dissertation planning, direction, and evaluation. The focus of the iterations, the so-called Sprints, is on communication and feedback throughout the entire process. The paper is based in theories on teaching/learning and on the author’s personal experience with the agile approach. Use of the agile approach, which can decrease the time required for doctoral studies, may thus increase the number of graduates with doctoral degrees. The paper makes suggestions for practical implementation of the agile approach.
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 | 0.033 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".