CAN MOBILE LEARNING MATURITY BE MEASURED? A PRELIMINARY WORK
Bibliographic record
Abstract
New mobile platforms, connected seamlessly to the Internet via wireless access have become increasingly more powerful and have found usage in a diverse set of application areas. On such area that has seen exponential growth in the use of mobile devices is the educational sector. Learning facilitated by mobile phones mobile Learning (m-Learning) is increasingly being adopted as an alternative way of imparting education by several institutions. The educational institutions are becoming more open to embracing new learning platforms, which in turn has sparked the interest in developing new assessment frameworks. The increasing popularity of m-Learning in universities necessitates the development of a process maturity evaluation methodology that would help in successful adoption of the new learning platform within universities. Accordingly, this paper presents a maturity model for evaluating the maturity of universities while adopting the m-Learning platform. This is a preliminary framework based on the current understanding of the issue. Later versions are expected to be more detailed and robust with the availability of empirical data from users. Additionally, there is also is a lack of comprehensive assessment and evaluation methodology for m-Learning platform, which is considered to be one of the major roadblocks in implementing the technology. The present paper addresses an important question: Can m-Learning maturity be measured? One viable solution for the question is adapting the Capability Maturity Model (CMM) framework to design and propose a capability maturity model suitable for m-Learning within educational institutions.
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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 | 0.025 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".