Bibliographic record
Abstract
There is a rapid increase in the use of mobile devices such as cell phones, tablet PCs, personal digital assistants, Web pads, and palmtop computers by the younger generation and individuals in business, education, industry, and society. As a result, there will be more access of information and learning materials from anywhere and at anytime using these mobile devices. The trend in society today is learning and working on the go and from anywhere rather than having to be at a specific location to learn and work. Also, there is a trend toward ubiquitous computing, where computing devices are invisible to the users because of wireless connectivity of mobile devices. The challenge for designers is how to develop multimedia materials for access and display on mobile devices and how to develop user interaction strategies on these devices. Also, designers of multimedia materials for mobile devices must use strategies to reduce the user mental workload when using the devices in order to leave enough mental capacity to maximize deep processing of the information. According to O’Malley et al. (2003), effective methods for presenting information on these mobile devices and the pedagogy of mobile learning have yet to be developed. Recent projects have started research on how to design and use mobile devices in the schools and in society. For example, the MOBILearn project is looking at pedagogical models and guidelines for mobile devices to improve access of information by individuals (MOBILearn, 2004). This paper will present psychological theories for designing multimedia materials for mobile devices and will discuss guidelines for designing information for mobile devices. The paper then will conclude with emerging trends in the use of mobile devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".