Embracing Big Data in Complex Educational Systems: The Learning Analytics Imperative and the Policy Challenge.
Bibliographic record
Abstract
In the new era of big educational data, learning analytics (LA) offer the possibility of implementing real–time assessment and feedback systems and processes at scale that are focused on improvement of learning, development of self–regulated learning skills, and student success. However, to realize this promise, the necessary shifts in the culture, technological infrastructure, and teaching practices of higher education, from assessment–for–accountability to assessment–for–learning, cannot be achieved through piecemeal implementation of new tools. We propose here that the challenge of successful institutional change for learning analytics implementation is a wicked problem that calls for new adaptive forms of leadership, collaboration, policy development and strategic planning. Higher education institutions are best viewed as complex systems underpinned by policy, and we introduce two policy and planning frameworks developed for complex systems that may offer institutional teams practical guidance in their project of optimizing their educational systems with learning analytics.
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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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.021 | 0.034 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".