The Framework for the Rational Analysis of Mobile Education (Frame) Model: An Evaluation of Mobile Devices for Distance Education
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.throughout my thesis research.Of my committee members, Dr.Richard Kenny's observations and suggestions were invaluable, and Dr. Rory McGreal's vision and enthusiasm inspired me to venture into mobile education
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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 | 0.029 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".