Bibliographic record
Abstract
Since its emergence, the field of learning analytics has proposed that educational institutions can and should make better use of learner data to optimize learning and learning environments. A range of social, political and economic forces have also encouraged educational institutions to consider system-wide implementations of learning analytics. In spite of a decade of optimism and interest, however, very few examples of effective institutional LA implementation exist, and evidence of positive impact on learning is sparse. This chapter provides an updated summary of the growing body of literature exploring the challenges of making systemic change with LA in complex educational contexts. Proposed frameworks for guiding institutional LA implementations are reviewed, and work describing use of the most promising – the SHEILA framework – is outlined in more detail. The need for attention to complexity leadership and institutional logics is noted as a focus of recent work, and emerging issues are highlighted: a critical need to expand the literature documenting evidence of real impact on learning, a need for institutions to make use of reliable LA evaluation strategies, and the need for critical consideration of how and if LA can also benefit learners beyond the traditional higher education contexts of the wealthy North.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".