Foreign Language Students’ Voices on Blended Learning and Fully Online Classes during the COVID-19 Pandemic
Bibliographic record
Abstract
Due to the restrictions of direct interactions during the pandemic, educational practices have massively and simultaneously shifted to remote teaching. Remote teaching is to some extent often viewed as an ineffective means of instructional delivery. It lacks the kind of interactions between teachers and students that are primarily found in traditional classrooms. In addition to ubiquitous technical hindrances, many educators find students' learning progress hard to monitor in remote teaching. The obstacles in remote teaching have prompted the government and educators to explore the possibilities of holding face-to-face meetings in a blended learning format amid the pandemic. This paper is aimed to present a sketch of students' perceptions of the possibilities of combining face-to-face classes with online learning during a pandemic. Using online surveys for data gathering and descriptive statistics for data analysis, this study found that students’ perception of current emergencies influences their preferred mode of instructional delivery. The students appear to be much more tolerant of numerous hindrances in remote teaching than the potential risks of COVID-19 transmission. Most students in this study preferred fully online learning to blended learning. For them, health is the top priority.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".