Listening to The Student Voice in Online Masters Community and Resource Development
Bibliographic record
Abstract
Expectations of online masters students commencing their studies has been under-researched, as have the challenges of transition from undergraduates learning on-campus to postgraduate online students. The study described here investigates student expectations of this transition, development of resources for academic skills teaching, and student evaluation of interventions supporting them to join the academic community as masters. The methods were a series of action research cycles with a total of 38 students participating from 5 annual cohorts of Master of Research students, with the taught component entirely online. A student cohort (12 students) surveyed for initial course evaluation led to resources being developed for the course induction. Group interviews with the following cohorts evaluated new resource development after each course iteration, leading to further online seminars and skills resources development. In addition, further synchronous and non-synchronous activities with teacher presence were employed to improve student enculturation in the academic community. Recorded online interviews in virtual classrooms preceded transcription and thematic analysis, showing that student expectations of masters study and the skills required to join the academic community in all cohorts needed management. Students expected a continuation of undergraduate studies, ‘but harder’. Development of an optional online academic skills course, allied to student activities embedded in specialist content with increased teacher and social presence, was praised by the last student cohort interviewed. The online skills course is available to other online courses within this Graduate School. This model may be transferable to other institutions, particularly in light of increased online Covid-19 teaching.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".