Introduction to Learning Analytics and Networked Learning Minitrack
Bibliographic record
Abstract
This minitrack addresses the leading edge of technology use and system design to analyze, support, and/or create learning and learning environments. Papers that fit this minitrack fall under new and ongoing areas of learning research that may be referred to as learning analytics, networked learning, technology enhanced learning, computer-supported collaborative learning, and mobile learning. The minitrack title reflects two research areas relating to technology in learning environments. Networked learning is the earlier term, picked up here from conferences of that name that have been ongoing in the UK and Europe since 1998. The focus at the research presented there has been on understanding the actual and potential transformations in learning and pedagogy emerging from the networked connectivity of the Internet. Learning analytics is the newer term, representing a rapidly emerging area of research and practice that aims to describe and evaluate the definition, collection, analysis, and use of data resulting from technology use. Such analytics encompasses system design to create better models and evaluations of learning on and through information technologies and new media, as well as evaluations of the learning process itself that can be accomplished based on data traces resulting from the use of technology, and of the social and ethical inputs and ramifications from such analytics. For this year's minitrack, the scope was wide to include papers that explore technology use to examine how social learning happens, use data from learning environments to support learning processes, and examine new practices of formal and informal learning on and through the Internet. The minitrack comprises these three papers selected following peer review of seven submissions.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.139 | 0.075 |
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".