CanCore: Best Practices for Learning Object Metadata in Ubiquitous Computing Environments
Bibliographic record
Abstract
Understanding the potential of ubiquitous computing technologies for learning and education (i.e. "ubiquitous e-learning") is a relatively new undertaking. Ubiquitous computing refers to making computers imperceptibly and pervasively available to the user in her environment. The notion of ubiquitous computing as a research issue itself dates back only to 1988 (Weiser, 1991), and the popular realization of this technology in the form of wireless mobile devices such as mobile telephones, personal digital assistants, Webpads, laptops, onboard automobile navigation systems, and other portable, networked computing technologies has only recently taken place. This paper begins from the premise that the emergent requirements of ubiquitous e-learning are very well suited to the flexibility and adaptability of a learning objects approach. The paper starts with a brief consideration of the characteristics and requirements of ubiquitous e-learning, and also explains the learning object approach and the standards and infrastructures used to support it. It then identifies challenges presented by the fact that these standards and infrastructures have not been developed with the requirements of ubiquitous e-learning in mind. The paper then suggests a number of adaptations or extensions to one specific and important e-learning standard - the standard for "learning object metadata." In discussing the application of this standard to ubiquitous e-learning, this paper makes significant reference to the CanCore guidelines for the implementation of learning object metadata in order to ensure maximum reusability and interoperability of data in this new context.
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 | 0.052 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.024 | 0.041 |
| Open science | 0.015 | 0.018 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".