A Study On Learner Readiness For MobileLearning At Open University Malaysia
Bibliographic record
Abstract
Prior to embarking on mobile learning, a study was conducted to determine the readiness of learners at the Open University Malaysia (OUM), Malaysia’s first open and distance learning university. The study conducted in the last quarter of 2008 attempted to determine, among others, the extent of ownership of a mobile phone, readiness to be a mobile learner as indicated by questions such as willingness to buy a new mobile device and preparedness to subscribe to additional mobile services, types of materials they would like to receive and their perceptions about m-learning. Out of a total of 6,000 questionnaires distributed, 2,837 were returned. The respondents were from 31 learning centres from all parts of the country. Most of the respondents were between 31 and 35 years old and were largely undergraduates. The findings indicate that almost all (98.91 percent) learners at OUM have a mobile phone and that 82.84 percent of the respondents can imagine themselves learning through mobile devices. When further questioned, 47.98 percent of the learners stated they would be ready for m-learning within six months and another 15.73 percent believed they will be ready within 6 to 12 months. In other words, 63.71 percent of students are ready for m-learning within the next 12 months. The paper highlights the findings and implications to the m-learning project at the university
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".