The Brilliant Scheduler: Automated Scheduling System for Video Conferencing Courses
Bibliographic record
Abstract
The study aims to investigate the problems and drawbacks associated with the present scheduling process for courses of video-conferencing, conducted at Administration of Educational Media at KAU (King Abdul-Aziz University). In KAU, male and female campuses are almost entirely segregated. The number of male staff and the diversity of their academic specialties is higher than those of their female counter parts. Hence, some subjects are taught to female students by male faculty, while utilizing videoconferencing technology. This research investigates the main challenges, faced by the Administration of Educational Media at King Abdul-Aziz University in scheduling video conferencing courses for classrooms reservation requests. It provides a complete list of tools and features to enhance, support, and automate the scheduling process for classrooms and supervisors, following Rapid Application development (RAD) methodology. The system has been implemented as a web-based automated scheduling system to undertake the capability of technology and to create an influential scheduling system. This system might also help in the migration of traditional paper-based work to a better technological environment, satisfied employees, and faster feedbacks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".