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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it