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.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".