Who am I and what keeps me going? Profiling the distance learning student in higher education
Bibliographic record
Abstract
<p>Student retention and progression has exercised the HE sector for some time now, and there has been much research into the reasons why students drop out of Higher Education courses. (Allen, 2006; Buglear, 2009;). More recently the Higher Education Academy Grants Programme Briefing (HEFCE, 2010) , outlined a number of areas that emergent project data revealed as being important to both the retention and progression of students, including areas outlined by a number of researchers as being essential to student success: expectations, support, feedback and involvement. But there has been less research, particularly within the distance learning sector, into factors that encourage students to stay (O'Brien, 2002). This small scale qualitative project using qualitative research methods and based in the Open University UK, builds upon an intensive institutional research project analyzing what type of interventions make a positive difference to student progression and success. The research revealed insights into factors linked to the expectations, identities and support of students which proved influential in terms of their resilience and motivation to remain on course.</p>
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".