Integrating Modalities into Context Aware eLearning System Using Cloud Computing
Bibliographic record
Abstract
ICT and digital Technologies have influenced the everyday life and the way how people learn and work. Nowadays, there is a variety of learning systems supporting innovative digital approaches and many schools, universities and organizations worldwide are investing in building their own e-Learning platforms. However, learners” performance still a concern that requests focus for improvement. The learning processes can be adapted to suit the needs of particular users when the proposed e-learning systems include context-awareness computing. We believe that instead of designing an e-Learning environment with hidden contextual information, e.g.: user's activity, location or nearby devices, we must consider a new e-Learning application approach including context-awareness capabilities. Such a system will be more efficient and adaptive to users' needs. Existing e-Learning systems suffer a lack of dynamic scalability. It's also complexes to extend, and expensive to integrate with other e-Learning platforms. In particular students who have visual processing disorder or hearing impairments can manipulate digital text or use adaptive modules like Subtitles which help them in processing the information effectively. This paper focus on the development of a hybrid e-Learning cloud system that combines the National Institute of Standards and Technology (NIST) architecture and various contexts aware computing such as User context, system context, and environment context that can quickly adjust to any environment change is expected. Were viewed traditional e-Learning architectures and their limitations and then proposed a hybrid cloud computing architecture to offer resources efficiently to all learning stakeholders and improve the education system quality at an affordable cost. To demonstrate the usefulness of the proposed system, we implemented and evaluated a simulation scenario. Based on our results, we highlight facts for increasing the cooperation and communication levels among learners in the proposed e-Learning environments and provide possible directions for future work.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".