Part-time students in transition: supporting a successful start to higher education
Bibliographic record
Abstract
The transition into higher education is a critical time for all students. A positive early experience provides a strong foundation for future academic success whilst a negative experience can be destabilising for a new learner. To date, research has primarily focused on full-time undergraduates in order to explain the reasons for high attrition rates at the end of the first year. Less is known about the experiences of part-time undergraduates despite the fact that they make up over one quarter of the total student population (HESA, 2015). This article reports on a study to investigate the initial experiences of a group of part-time undergraduates who have chosen to undertake a degree at a small study centre run by one university. Using a mixed methods research approach, the research captured the lived reality of the experience and identified the contributing and negating factors that can influence a successful transition. Perceptions of the level and type of support provided for students during transition were gained from both staff and students. The findings confirm a heterogeneous group. Despite being highly motivated, the early transition period was generally characterised by a sense of trepidation and self-doubt as students took their first steps in higher education. The research highlights the complexity of the initial decision-making process for part-time students and the barriers they face. It concludes that a flexible but unified approach, involving tutors and the wider support services, is needed, as unique students require unique responses to their transition needs.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".