INTEGRATION OF TECHNOLOGY IN IMPROVING THE PROFESSIONALISM OF ISLAMIC RELIGIOUS EDUCATION TEACHERS
Bibliographic record
Abstract
Technological developments have significantly impacted various aspects of life, including education. This research aims to analyze the role of technology in increasing the professionalism of Islamic Religious Education (PAI) teachers. The research uses qualitative methods with a literature study approach, which involves collecting data from various sources such as scientific journals, books, articles, and related documents. Data analysis used a content analysis approach to identify the main themes and relationships between technology and PAI teacher professionalism. The research results show that technology has great potential in the professionalism of PAI teachers in utilizing technology through various innovations, such as interactive media, digital learning applications, and e-learning platforms. Technology helps teachers deliver material more interestingly and efficiently, and supports students to learn independently and collaboratively. In addition, the integration of technology in the professional development of PAI teachers has been proven to increase students' understanding of religious values, strengthen their character, and motivate them to be active in the learning process. This research concludes that optimal use of technology can be a solution to overcome challenges in increasing the professionalism of PAI teachers. It is hoped that these findings will become a reference for developing learning strategies that are more innovative and relevant to the educational needs of the 21st century.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".