A Comparison of the Uptake of Two Research Models in Mobile Learning: The FRAME Model and the 3-Level Evaluation Framework
Bibliographic record
Abstract
This paper discusses the diffusion of two models of mobile learning within the educational research literature: The Framework for the Rational Analysis of Mobile Learning (FRAME) model and the 3-Level Evaluation Framework (3-LEF). The main purpose is to analyse how the two models, now over 10 years old, have been referenced in the literature and applied in research. The authors conducted a systematic review of publications that referenced the seminal papers that originally introduced the models. The research team summarized the publications by recording the abstracts and documenting how the models were cited, described, interpreted, selected, rejected, and/or modified. The summaries were then coded according to criteria such as fields of study, reasons for use, criticisms and modifications. In total, 208 publications referencing the FRAME model and 97 publications referencing the 3-LEF were included. Of these, 55 publications applied the FRAME model and 10 applied the 3-LEF in research projects. The paper concludes that these two models/frameworks were likely chosen for reasons other than philosophical commensurability. Additional studies of the uptake of other mobile learning models is recommended in order to develop an understanding of how mobile learning, as a field, is progressing theoretically.
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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 | 0.290 | 0.385 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.029 | 0.017 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".