The Framework for the Rational Analysis of Mobile Education (Frame) Model: An Evaluation of Mobile Devices for Distance Education
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
Mobile technology is a new and promising area of research in distance education.Currently, there are few if any descriptive models of mobile learning that can be used to develop appropriate pedagogical practices.This thesis has two main purposes: to develop a theoretical model of mobile learning and to use the model to evaluate a set of mobile devices.The Framework for the Rational Analysis of Mobile Education (FRAME) model describes mobile learning as a process resulting from the convergence of mobile technologies, human learning characteristics, and social interaction.The devices included in this study were equipped with wireless networking capacity, but varied in size, weight, processing power, interface design, portability, as well as input and output capabilities.This study is both theoretical and evaluative, relying on a small panel of experts to review the devices.During the first phase of data collection, the experts individually evaluated each device.In the second phase, they shared their observations in a face-to-face discussion.All questionnaires and discussion questions were based on the FRAME model.The study culminates in a discussion of some of the most significant factors likely to affect mobile device usability in distance education.It also outlines other areas of research suggested by the FRAME model.
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.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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