Exploring Extra-Curricular Bootcamps: A Qualitative Study on Accelerated Learning in Higher Education
Bibliographic record
Abstract
In response to the persistent challenge of study delay in higher education in the Netherlands, innovative approaches such as extra-curricular bootcamps have emerged. These dynamic and intensive programs offer an alternative to traditional education, providing accelerated learning experiences for students. Teachers are increasingly taking on coaching roles, guiding students through their study progress and choices. This article presents the findings of a qualitative research project of graduation bootcamps during three study years with 225 last year students with substantial study delay. We used open coding to get to the themes of key elements such as collaboration, peer interactions, teaching methods, motivation, and coaching within the context of bootcamps. We subsequently used selective coding based on the Self Determination Theory to further analyse the data. Although the implementation faced challenges, the results show that ongoing support and a community of like-minded individuals are essential for success. This study confirms the literature on the need for motivation and structured support in overcoming academic delays and provides practical insights for the development of effective educational interventions.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".