Development Of Artistic Digital Quranic Interpretation Toward Innovative Foreign Language Learning
Bibliographic record
Abstract
This research attempts to develop a model of artistic quranic digital interpretation toward innovative foreign language learning to increase student’s insight on quranic understanding. The purpose of research is to introduce alTashwir alFanniy (TF) model and the student’s opinions about the TF. This quranic interpretation will use several digital exegesis books and non-digital to highlight deeply selected quranic verses. Descriptive analysis approach was used in this qualitative research to take a generalization of the quranic digital interpretation that the selection of controlled samples will produce a reliable answer. The data of population were collected by using the Simple Random Sampling (SRS) and the questionnaire used Lickert scale by twenty of selected respondents. The most important results is there is 70% respondents agreed that alTashwir alFanniy (TF) in the quranic interpretation is very interesting to be learned, 85% respondents agreed that alTahswir alFanniy based on 4 C’s of 21st century skills is easy to be understood and applied in obtaining new insights and deep contemplation in this digital era, and 75% respondents agreed that students can improve their Arabic and English vocabularies by using alTashwir alFanniy model based on technology application.
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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 | 0.003 | 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 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".