Educating Professionals and Professionalising Education in Research-Intensive Universities: Opportunities, Challenges, Rewards and Values
Bibliographic record
Abstract
Abstract This study describes what higher education institutions (HEIs) that are known for their research excellence are doing to implement current student and teaching oriented higher education (HE) policies in England and Wales. Pressures to reach increasingly higher levels of excellence in both teaching and research challenge existing structures and mechanisms in these researchintensive universities (RIUs). Options for overcoming challenges are discussed by bringing together perspectives of different stakeholders. This thesis is based on analysis of documentary and empirical data to gain insight into perspectives and experiences of stakeholders of the implementation of current HE policies in England and Wales. Documentary data consisting of publicly available material about HE policies has been analysed by an interpretive analysis of policy, and papers about research have been systematically reviewed. The contents of interviews with academics in four RIUs have been analysed in case studies. This study contributes to existing research on ‘professionalism’ (see, for example, Kolsaker, 2008), ‘effective teaching’ (see, for example, Hunter & Back, 2011), and ‘evaluating teaching quality’ (see, for example, Dornan, Tan, Boshuizen, Gick, Isba, Mann, Scherpbier, Spencer, Timmins, 2014). This study also complements The UK Higher Education Academy’s (HEA) research in this area including Gibbs’ report on quality (2010) as well as earlier work on reward and recognition (2009). Key findings give insight into a troublesome relationship between teaching and research activities, which is at the core of many of the challenges RIUs are facing. Findings showing academics strong interest in their students, teaching, and research highlight their engagement in the development of these key activities. These support recommendations for development processes in RIUs involving organisation wide engagement to build parity of esteem between research and teaching to achieve aims to reach their full potential in terms of excellence in HE.
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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 | 0.039 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".