The 5R Adaptive Learning Content Generation Platform for Mobile Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ubiquitous mobile computing and the advances in wireless telecommunication networks have encouraged significant growth of mobile learning in recent years. Since mobile learning can take place at anytime and anywhere, there is an advantage to integrate the real world objects into learning contents. Mobile devices have characteristics that include location awareness and hardware diversity. Thus, there are needs and opportunities to provide mobile learners with adaptive learning experience. To implement adaptive mobile learning, it is essential that the learning contents are context sensitive to be retrieved through the adaptation mechanism built in the learning management system. In this paper, an adaptive learning content generation platform is presented. We adopted the 5R adaptation framework to provide the mobile learning system the capability of providing the right content to the right learner, through the right device, in the right location, and at the right time. We provide an example to demonstrate how to use the platform to create adaptive learning contents.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it