Implications and Challenges in Studying as a Full Distance Learner on a Masters Programme: Students’ Perspectives
Bibliographic record
Abstract
There has been a growing interest in the application of information and communication technology (ICT) as a means of improving and extending participation in Higher Education and in its impact on pedagogy. Six years ago, two students were recruited to a Masters Degree programme at St Mary’s University, London, as Full Distance Learners. Full Distance Learning implies that through asynchronous participation, students are not required to be together at the same time but can access course materials and communicate with tutors and other students flexibly at their own time and convenience through a virtual learning environment (VLE). Numbers have grown exponentially and, currently, there are more than fifty Full Distance Learners engaged at some point in the programme. This paper sets out to explore the personal reflections of the experiences of Full Distance Learners who have successfully completed the course. Adopting a phenomenological approach, it was possible for the researcher to explore individual perceptions of students in order to evaluate their particular experiences, which are not often studied. Consequently, it was possible to interpret the benefits and limitations of studying as Full Distance Learners from their own experiences. It was hoped that an examination of the experiences and perceptions of individuals from their own personal points of view would indicate to what extent they would support, inform and challenge conventional practice.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".