Bibliographic record
Abstract
In the 2010s, growth of information and communication technologies and the emergence of big data led to the possibility of meaningful analysis of data at scale. University student interactions were channeled through learning management systems (LMS) or virtual learning environments (VLEs), so researchers were able to collect clickstream data and observe patterns of use which were previously invisible or non-existent. Leading thinkers saw the potential for learning analytics and led the development of this distinct field, separating away from other data-informed approaches, such as electronic data mining and academic analytics. The field was strengthened through the development of the Society of Learning Analytics Research (SoLAR). SoLAR publish the Journal of Learning Analytics and established leading conferences to build a worldwide network of disciplinary expertise. Thought leaders from Australia, Canada, Europe, the United Kingdom, and the United States led the global movement to implement learning analytics. Initially, learning analytics focused on the potential for using data-driven decision-making to inform actionable insights or interventions, which could improve student learning outcomes. As data reveals information not previously accessible, ensuring ethical approaches and respecting student privacy have been consistent themes. Data collection has grown to be multimodal in nature and analytic approaches have continued to develop, expanding to include social and networked analyses, cluster analyses, and others. Attention was directed to the way students and instructors visualize and communicate findings from the wealth of data. There is a continued focus on sense-making via visual displays to ensure information is effectively interpreted and understood. Tools and applications for implementing interventions were often initially tested in siloed or individual courses. The field is now expanding to bring insights and positive findings from initial learner support to inform a broader understanding of how and in what way these tools specifically support learners through this complex, situational, and social process across institutions worldwide. Researchers argue for greater pedagogical and theoretical links to ensure scalability and support for learners and educators alike. The most effective use of these technologies combines established learning theories and learning design with analytics to generate useful and actionable insights. The ideal is to support student success through personalized learning. However, the significant potential for improving student learning outcomes can only be achieved through broad stakeholder engagement. To support widespread adoption by educators and implementation at an institutional-level, policy frameworks such as the SHEILA framework and DELICATE checklist have been developed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".