Who am I and what keeps me going? Profiling the distance learning student in higher education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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>
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.
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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it