Supporting transnational students in the transition to doctoral study through online technologies
Bibliographic record
Abstract
This paper will report findings of an 18 month research project, funded by the Higher Education Academy in the United Kingdom (UK), to identify the differences in experience, expectation and engagement of using technologies, designed for use in Western Universities with post-graduate students in the East. The focus of the research is a Professional Doctorate course delivered by a UK based university and taught in Hong Kong (HK) by UK academic staff over 4 weekends each year, with supervisory support throughout the academic year by tutors based in the UK. The research investigated the use of technologies, including the UK university's Virtual Learning Platform (VLE), to identify whether there is a Western culture bias in the use of the VLE in the delivery of post-graduate courses in the East. While literature is extensive in using technologies in learning and teaching in the West, and in teaching international students, there appears to be a lack of research focused on using new technologies designed in the West used in course delivery in the East. A multi-layered approach to data collection through observation, software analytics, questionnaire and interview has resulted in a higher quality experience for the students, deeper levels of engagement and the introduction of new technologies to support the development of a community of practice encompassing students in HK and the UK. This paper explores challenges faced by staff and students and provides research informed evidence of how Eastern students can be engaged with Western designed technologies.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.022 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".