Application of Integer Programming in Maximizing the Number of Industrial Engineering Students Allowed to Attend Face-to-Face classes for Blended Learning in Mapúa University during the COVID-19 Pandemic
Bibliographic record
Abstract
With the increasing number of COVID-19 cases in the Philippines, universities are coming up with different ways on how to continue education online for the incoming school year. But this only poses a huge challenge, where many students have no access to education resources. Mapúa University recognized this problem and offered an option to its students to choose whether to have a fully online term or a blend of online and face-to-face classes (blended learning). The study aims to determine the maximum number of IE-EMG students allowed to attend face-to-face classes for the 1st quarter of A.Y. 2020-2021, where blended learning is opted to be implemented as the learning mode of delivery. An integer programming model is designed to help the beneficiaries of this study with assigning courses and class schedules for blended learning to IE students of the IE-EMG department while observing IATF protocols and the university's guidelines. An optimal solution was obtained using Excel's solver tool, where Max Z is equal to 135, this suggests that the faculty members should limit the total number of IE-EMG students that will attend face-to-face classes every week to 135. The obtained solution could be used by the faculty members of the department as a guide in arranging the class schedules of the students.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".