Case study 4: Education doctoral student - 'An exploratory study of online social networking within a doctorate of education program'
Bibliographic record
Abstract
Introduction The professional doctorate is specifically designed for professionals investigating real-world problems and relevant issues for a profession, industry, and/or the community. The focus is scholarly research into professional practices. The research programme bridges academia and the professions, and offers doctoral candidates the opportunity to investigate issues relevant to their own practices and to apply these understandings to their professional contexts. The study on which this article is based sought to track the scholarly skill development of a cohort of professional doctoral students who commenced the course in January 2008 at an Australian university. Because they hold positions of responsibility and are time-poor, many doctoral students have difficulty transitioning from professional practitioner to researcher and scholar. The struggle many experience is in the development of a theoretical or conceptual standpoint for argumentation (Lesham, 2007; Weese et al., 1999). It was thought that the use of a scaffolded learning environment that drew upon a blended learning approach incorporating face to face intensive blocks and collaborative knowledge-building tools such as wikis would provide a data source for understanding the development of scholarly skills. Wikis, weblogs and similar social networking software have the potential to support communities to share, learn, create and collaborate. The development of a wiki page by each candidate in the 2008 cohort was encouraged to provide the participants and the teaching team members with textual indicators of progress. Learning tasks were scaffolded with the expectation that the candidates would complete these tasks via the wikis. The expectation was that cohort members would comment on each other’s work, together with the supervisor and/or teaching team member who was allocated to each candidate. The supervisor is responsible for supervising the candidate’s work through to submission of the thesis for examination and the teaching team member provides support to both the supervisor and the candidate through to confirmation. This paper reports on the learning journey of a cohort of doctoral students during the first seven months of their professional doctoral programme to determine if there had been any qualitative shifts in understandings, expectations and perceptions regarding their developing knowledge and skills. The paper is grounded in the literature pertaining to doctoral studies and examines the structure of the professional doctoral programme. Following this is a discussion of the qualitative study that helped to unearth key themes regarding the participants’ learning journey.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".