Setting Learning Analytics in Context: Overcoming the Barriers to Large-Scale Adoption
Bibliographic record
Abstract
A core goal for most learning analytic projects is to move from small scale research towards broader institutional implementation, but this introduces a new set of challenges because institutions are stable systems, resistant to change. To avoid failure and maximize success, implementation of learning analytics at scale requires explicit and careful consideration of the entire TEL technology complex: the different groups of people involved, the educational beliefs and practices of those groups, the technologies they use and the specific environments within which they operate. It is crucial to provide not only the analytics and their associated tools, but also to begin with a clear strategic vision, to critically assess institutional culture, to identify potential barriers to adoption, to develop approaches to overcome these and to put in place appropriate forms of support, training and community building. In this paper, we provide tools and case studies that will support educational institutions in deploying learning analytics at scale with the goal of achieving specified learning and teaching objectives. The ROMA Framework offers a step-by-step approach to the institutional implementation of learning analytics and this approach is grounded by case studies of practice from the UK and Australia.
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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 | 0.129 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".