Linked Data for Learning Analytics: Potentials and Challenges
Bibliographic record
Abstract
The emergence of massive open online courses (MOOCs) and the open data initiative have led to a change in the way educational opportunities are offered by shifting from a university-centric model to a multi-platform environments include not only diverse online learning platforms, but also social media applications (e.g., SlideShare, YouTube, Facebook, Twitter, or LinkedIn) data and resources. Henceforth, learning is now occurring in various forms and settings, both at the formal (university courses) and informal (social media, MOOC) levels. This has led to a dispersion of learner data across various platforms and tools, and brought across various environments for a comprehensive connectivist MOOC (cMOOC). In cMOOCs, learning, but relies on a range of dedicated online learning applications as well as social media and networking applications for sharing information and resources among learners (Siemens, 2005). These developments led to new requirements and imposed new challenges for both data collection and use.
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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| 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; both teacher heads agree on what is shown here.
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".