E-Learning Systems Content Adaptation Frameworks and Techniques
Bibliographic record
Abstract
Content adaptation is defined as the process of selection, generation, or modification of content which include text, images, audio, video, navigation, interaction, any object within a Web page, and associate service agreement (Forte, Claudino, de Souza, do Prado, & Santana, 2007) to suit user’s context (TellaSonera, 2004). With the proliferation of mobile devices such as personal digital assistants (PDA) and smart mobile phones which have the capability of accessing the Internet anytime and anywhere, there is an increasing demand for content adaptation to provide these devices with appropriate content that is aesthetically pleasant, easy to navigate, and achieve satisfying user experiences. This article first provides an overview of frameworks and techniques in Web content adaptation that are being developed to extend Web applications to non-desktop platforms. After describing general adaptation techniques, this article focuses particularly on the adaptation requirements for e-learning systems, especially when they are accessed through mobile devices.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".