Learning Analytics Research: Current Approaches and Future Directions
Bibliographic record
Abstract
With the increased use of technology in educational research, we now have access to complex data sets collected during student learning, for example log data from learning management systems or eye tracking data. Learning analytics has emerged as a field that attempts to (a) analyze these data using analytic approaches that continue to evolve, (b) address privacy and ethical issues, and (c) disseminate results in ways to inform and support effective learning and teaching. This symposium brings together scholars in the field who use diverse methods and approaches in their learning analytics research. A range of research will be presented, including: the importance of learner access to learning analytics data to facilitate self-regulated learning, the value of taking a grassroots approach to learning analytics research to inform learning and teaching, and the use of multichannel data to foster emotion regulation in virtual learning environments. The session will end with time for audience questions and a lively discussion of all things learning analytics! [MOU1] I don’t think LA has emerged as a field to address privacy and ethical issues. It’s just having to address them as part of the process.
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 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 | 0.092 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.027 | 0.048 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".