Context-Aware Mobile Learning on the Semantic Web
Bibliographic record
Abstract
This chapter focuses on the theoretical and technological aspects of designing mobile learning (m-learning) services that deliver context-aware learning resources from various locations and devices. Context-aware learning is an important requirement for next generation intelligent m-learning systems. The use of context in mobile devices is receiving increasing attention in mobile and ubiquitous computing research. In this research work, context reflects timeliness and mobility to nurture pervasive instruction throughout the learning ecosystem. In this context of ubiquity that is supported by a new generation of mobile wireless networks and smart mobile devices, it is clear that the notion of context plays a fundamental role since it influences the computational capabilities of the used technology. In particular, three types of context awareness are being considered in this work —platform-awareness, learner-awareness, and task-awareness. In this research work, these contextual elements are defined at the semantic level in order to facilitate discoverability of context-compliant learning resources, adaptability of content and services to devices of various capabilities, and adaptability of services to task at hand and interaction history. The work presented in this chapter contributes towards this direction, making use of the progress in Semantic Web theory and mobile computing to enable context-aware learning that satisfies learning timeliness and mobility requirements.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".