Towards a Descriptive View of Context Usage in Context-Aware U-Learning System
Bibliographic record
Abstract
Research in ubiquitous learning (U-learning) has gained attention of a large number of researchers and a number of ubiquitous learning systems are now available in the literature. Majority of these systems have been developed to resolve a specific problem in a given context; their development approaches do not dictate ubiquitous context usage requirements to fill in. U-learning systems developers need to have a clear and a general view of how their intended systems make use of the ubiquitous context. This paper introduces a comprehensive view of context usage through three different view-points inspired from Dowson’s work (Dowson, 1993) each one capturing a particular aspect of context handling. Then a set of facets is associated to each given aspect in order to study, understand and appropriately describe it. The findings of this research are aimed to provide context-aware u-learning system developers a clear understanding of the context usage in such systems and help in underlining the requirements of the u-learning environment. This research is also aimed to help in comparing and evaluating context-aware u-learning systems according to the descriptive system views.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".