“Should I stay or should I go?” Indicators of Dropping Out Thoughts of Doctoral Students in Computer Science.
Bibliographic record
Abstract
Evidence in the literature indicates that doctoral candidates may experience increased levels of stress and worry about successfully completing their doctorate degrees. As a result, a significant number of doctoral candidates drop out. In our study with 424 doctoral students in computer science (113 women, 311 men), we ask about the frequency of dropout thoughts as an indicator of possible premature termination. By means of machine learning algorithms, we extract variables associated with higher or lower likelihood of dropout thoughts. In particular, satisfaction with advisor’s support, experiencing a crisis, professional self-efficacy, choice of advisor, and perceived meaningfulness of additional work tasks proved to be of central importance. Based on these results, we suggest taking steps to improve professional and social support for doctoral students. Recommendations include implementing more intensive supervision in the early stages of the doctorate, improve the match between doctoral candidates’ expectations and the requirements of the respective institute, monitor progress during the doctorate (e.g., with the help of an advisor agreement), and increase the qualifications of advisors to include leadership and communication skills.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".