Impact of course timetabling on learning quality: sustaining an optimized stress level to stimulate enhanced comprehension
Bibliographic record
Abstract
This paper aims to improve students’ learning performance by optimizing their mental stresses in learning through proposing a new course timetabling method. This new method is based on two hypotheses that formulate the link between course timetabling and learning experience: i) a student’s learning performance is superior when the student is subject to moderate stress; ii) an individual’s mental capacity varies during a day according to Circadian Rhythm. The student’s mental stress in taking a course is defined as a function of their mental capacity and the workload required by the course. The workload is determined by utilizing a multi-criteria prioritization technique—Analytic Hierarchy Process. As a result, the timetabling problem is formulated as a mixed-integer linear programming model, which is tested on an engineering program to produce a student-centered timetable for its scheduled courses. This new method differs from traditional course scheduling and timetabling approaches, which are usually tackled as a constrained optimization problem with an objective to optimize a given set of criteria, such as student and faculty preferences, walking distances between consecutive classes, classroom utilization and operating expenses.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".