Encouraging People to Learn Islam in a New Interactive Way Using Augmented Reality (AR)
Bibliographic record
Abstract
The augmented reality (AR) has been recognized to be fit for use in education. Augmented Reality is globally used for education standard curriculum. Text, graphics, video and audio can be superimposed into a student’s real time environment. Textbooks, flashcards and other educational reading material can contain embedded “markers”, but only when it were scanned using an application, it produce supplementary information to the student rendered in a virtual multimedia format. For instance, Construct3D, a Studiers tube system, allows students to learn mechanical engineering concepts, math or geometry. This is an active learning process in which students can learn and interact with technology directly. The aim of this study is to show how by using can encourage people to learn Islam in a new interactive media using .The objective is to investigate the type of AR feature that can be use and to identify potential of learning using AR. A survey was done to know about people opinion about implementing AR in learning Islam by giving questionnaire to be fill in by 50 people in University Teknologi Malaysia. 90% of the responder agreed that AR can help in learning Islam in more interactive way.Keywords: Augmented reality; medium; Islamic study; interactive; educational
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".