Enhancing Quranic and fardhu ain programmes through hybrid \nlearning among orphans at Rumah Amal Baitul Kasih, Rawang
Why this work is in the frame
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Bibliographic record
Abstract
This project involves the transfer of knowledge \nbetween a university community and an orphanage. \nThe project applies the hybrid learning method which \nis a combination of the traditional and computerassisted \napproaches to enhance the Quranic Recital, \nFardhu Ain and Arabic language programs. This KTP \nproject is combining the theories of multimedia \nlearning by Mayer (2001) and hybrid learning by \nNicole & Retta (2006) in the activities. Mayer (2001) \nstates that " people learn more deeply from words and \npictures than from words alone", while Nicole & Retta \n(2006) mentioned that the hybrid learning approch is \neffective in carrying out 'in-depth learning'. An \norphanage was chosen as the partner for this project \nbecause the orphans are in dire need of attention and \nsupport. The project helps to ensure that they are not \nleft behind. The researchers chose to teach Quranic \nrecital and Fardhu Ain since the skills could help to \nmake them a better person. The religious and moral \nvalues which are embedded in these two courses may \nhelp towards producing ethical manpower in the \nfuture. This paper presents some of activities that were \nheld in the first quarter of 2014. It was successfully run \nwith a few modifications to the strategies and timing of \nthe activities. Some of the children were found to be \nextremely weak in their Jawi script writing skill and \nArabic language proficiency. Hence more time was \nneeded to assist them during the the learning activities. \nThis project implies the need for the government to set \nup a special care center for orphans under its \nsupervision which integrates education and welfare in \nensuring a brighter future for this needy group.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it