Digitalised higher education: key developments, questions, and concerns
Bibliographic record
Abstract
Higher education is already profoundly digitalised. Students, academics, and university administrators routinely use digital technologies, many of which rely on data, including artificial intelligence. Universities aim to operate as data-powered organisations to support institutional efficiency and the personalisation of learning and student experience. These developments are occurring against the backdrop of university digital infrastructure moving to the cloud and the increasing role of ‘Big Tech’ in the sector. However, there are many unknowns about the aggregate impact of digitalisation on the sector, and hence, questions about potential risks and harms remain unanswered. Our approach in this collective piece is to reflect on particularly relevant and impactful dynamics of higher education digitalisation. We first identify assetisation as an emergent mode of governance linked to the digitalisation of HE, which brings new temporal, relational, and lock-in challenges for universities and their constituents. Second, we examine the macro-level structural transformation of higher education with the increasing role of Big Tech and Big EdTech. We conclude by discussing the consequences of the identified macro power dynamics.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.025 | 0.041 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".