Challenges and Opportunities in an Alternative Approach for Academic Workload in the New Normal
Bibliographic record
Abstract
COVID-19 challenged the delivery of quality education as it abruptly altered in-person schooling in all educational institutions across the globe. College administrators were compelled to design and adopt a scheme that suits the environment of remote learning but safeguards quality teaching and learning. This quantitative-descriptive research evaluates the alternative approach to academic workload adopted by a local college in Batangas City, during the pandemic, when in-person classes were called-off. The adopted workload scheme aimed to ensure effective and efficient delivery of remote instruction so that quality learning will be sustained. The evaluation focused on the challenges and opportunities of the adopted system called the "two-term academic workload scheme." Data were gathered through a content-validated questionnaire distributed to two hundred and seventy-seven (277) respondents via Google form. The respondents were full-time teachers and students in the College of Education. Data gathering happened in the first quarter of 2022, a year and a half after the adopted scheme was implemented. It was found that teachers and students shared similar views, especially on the opportunities that resulted from the scheme but slight contrasting views on the challenges were observed. This, however, did not result to a significant difference in responses. The study revealed that the adopted scheme created more opportunities than challenges and hence has served the purpose of sustaining excellent delivery of instruction and the expected quality output was achieved.
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.
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".