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.From the perspective of data collection, the emergence of cloud services and the rapid development of scalable web architectures allow for pulling and mashing data from various online applications.This is supported by major Web stakeholders such as Facebook, LinkedIn, or Twitter, and by MOOC providers such as Coursera and Udacity.From the perspective of data use, the plethora of resources and interactions occurring in educational platforms requires analytic capabilities, including the ability to handle different types of data.take the form of unstructured content, ranging from posts.This multitude of kinds and sources of data pro-Chapter 30: Linked Data for Learning Analytics: Potentials and Challenges 1 2 3 2,4and diversity of learning environments, the emergence of scalable learning models such as massive open online courses (MOOCs), and the integration of social media platforms in the learning process.This diversity poses multiple challenges related to the interoperability of learning platforms, the integration of heterogeneous data from multiple knowledge sources, and the content analysis of learning resources and learning traces.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.021 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.024 | 0.055 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".