Turning universities into data-driven organisations: seven dimensions of change
Bibliographic record
Abstract
Abstract Universities are striving to become data-driven organisations, benefitting from data collection, analysis, and various data products, such as business intelligence, learning analytics, personalised recommendations, behavioural nudging, and automation. However, datafication of universities is not an easy process. We empirically explore the struggles and challenges of UK universities in making digital and personal data useful and valuable. We structure our analysis along seven dimensions: the aspirational dimension explores university datafication aims and the challenges of achieving them; the technological dimension explores struggles with digital infrastructure supporting datafication and data quality; the legal dimension includes data privacy, security, vendor management, and new legal complexities that datafication brings; the commercial dimension tackles proprietary data products developed using university data and relations between universities and EdTech companies; the organisational dimension discusses data governance and institutional management relevant to datafication; the ideological dimension explores ideas about data value and the paradoxes that emerge between these ideas and university practices; and the existential dimension considers how datafication changes the core functioning of universities as social institutions.
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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 | 0.036 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.019 | 0.104 |
| Scholarly communication | 0.049 | 0.040 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".