Use of Tablet Computers to Improve Access to Education in a Remote Location
Bibliographic record
Abstract
A research project was carried out in using mobile learning to increase access to education. This project is contributing to the achievement of Goal 4 of the Sustainable Development Goals (SDGs), which is to “Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all”. The mobile learning project involved the use of mobile technology to deliver learning materials to students to provide flexibility of access. Students used tablet computers to access electronic learning materials from the Aptus local server without having to connect to the Internet. The Aptus system is portable and was designed by the Commonwealth of Learning to allow learners to connect to digital learning platforms and access course materials without the need for Internet access. The project was implemented in a school in Pakistan. A total of 74 Grade 8, 9, and 10 students were involved in this project. The research revealed a positive impact on students and on learning as a result of their participation in the mobile learning project: students were better able to use the mobile technology for learning. Both students and parents also indicated that the project increased the students’ knowledge on the use of tablets for learning. Parents indicated that the mobile learning project increased their childrens’ interest in studying. Teachers also acknowledged that the students were taking more interest in classroom learning and concentrated on their tablets during study. Students were tested before and after they were supplied with content on their tablets. The post-test scores were significantly higher than the pre-test scores, indicating the use of the tablets for learning improved students’ performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".