Bibliographic record
Abstract
Course timetabling is a well known constraint satisfaction optimization (CSOP) problem, which needs to be solved in educational institutions regularly.Unfortunately, this course timetabling problem is known to be NP-complete [7,39].This M.Sc.thesis presents a multi-phase approach to solve the university level course timetabling problem.We decompose the problem into several sub-problems with reduced complexity, which are solved in separate phases.In phase-1a we assign lectures to professors, phase-1b assigns labs and tutorials to academic assistances and graduate assistants.Phase-2 assigns each lecture to one of the two day-sequences (Monday-Wednesday-Friday or Tuesday-Thursday).In Phase-3, lectures of each single day-sequence are then assigned to time-slots.Finally, in phase-4, labs and tutorials are assigned to days and time-slots.This decomposition allows the use of different techniques as appropriate to solve different phases.Currently different phases are solved using constraint programming and integer linear programming.The multi-phase architecture with the graphical user interface allows users to customize constraints as well as to generate new solutions that may incorporate partial solutions from previously generated feasible solutions."The problem is never how to get new, innovative thoughts into your mind, but how to get old ones out.Every mind is a building filled with archaic furniture.Clean out a corner of your mind and creativity will instantly fill it."-Dee Hock I take much pleasure to express my profound gratitude to my supervisor Dr. Shahadat Hossain for his persistent and inspiring supervision.I also thank my M.Sc.supervisory committee members Dr
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".