Constructing Sustainable Digital Learning Environments for Remote Rural Children of Sarawak
Bibliographic record
Abstract
Children today are labeled as Digital Natives, because they are born into an era where ICT has already permeated almost all layers of societies around the world. However, digital gaps among children still exist, particularly for those born into underprivileged remote rural communities. Making technology accessible for all learners, irrespective of their geographical locations, is often viewed as the means for narrowing, if not eliminating digital divide. Presence of technology would definitely generate interest and discussion about its potential use especially among learners from rural remote locations. However, the debate is still open about the feasibility and capability of technology to initiate meaningful learning. This paper describes part of an on-going research to investigate the impact of using technology to supplement classroom learning among children of remote rural locations in Sarawak, Malaysia. One of the key goals of the project is to develop a technology literacy programme in an informal learning setting using localized content which are selected and built to sustain and enhance local cultures, beliefs and traditions that already exist in these remote rural locations. This project also investigates the factors that need to be addressed when planning, designing and sustaining informal learning experiences using technology for children of various ethnic groups, languages, beliefs and cultures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".