Navigating prophetic pedagogy: reflections and insights
Bibliographic record
Abstract
On September 30, 2024, the IIUM teaching community had the privilege of attending a Workshop on Prophetic Pedagogy, organized by the Center for Professional Development. Esteemed guest speakers from abroad shared their insights on Prophet Muhammad’s approach to educating the Ummah. This workshop served as a timely reminder for all attendees as we prepare to start the semester for the 2024-2025 academic year. Alhamdulillah! The workshop took place at the IIUM Senate Hall, moderated by Prof. Ts Dr. Mira Kartiwi, Director of CPD. The invited speakers included Dr. Sheikh Abdallah Idris Ali from the Islamic Society of North America (ISNA), as well as Dr. Rehenuma Asmi and Dr. Amaarah DeCuir from the Centre for Islam in the Contemporary World (CICW). Dr. Sheikh Abdallah, renowned for founding Toronto’s first full-time Islamic school, captivated the audience with his extensive teaching experience. In his presentation, he focused on the miracles of the Qur’an and their relevance to contemporary scientific developments. Dr. Rehenuma and Dr. Amaarah’s presentations explored the Prophet’s method of educating his companions. Throughout the event, all three speakers actively engaged the audience, encouraging questions and sharing their own teaching experiences.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.032 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.007 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".