Challenges and Solutions of Online Language Teaching and Assessment During Covid-19
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
The Covid-19 pandemic which began in March 2020 can be construed as the biggest change of the century, in terms of the adjustments in quality of life, economic, social, and education factors. The biggest change was seen in terms of interaction due to the lockdown, where physical interaction is no longer allowed during the early phase of the pandemic. Subsequently, all educational sectors were forced to close, and remote or online learning was the new norm. The sudden change affected all teachers and instructors from around the world, but this study is particularly interesting to know the effect towards language instructors in their online teaching and assessments. In particular, this study aims to investigate the challenges faced by language instructors during online learning and assessment amid the Covid-19 pandemic, and to provide recommendations for remote or online learning if faced by future crises. This study employs a qualitative method approach through focus group interviews. The focus group interviews were analysed using thematic analysis. The findings demonstrate that the main challenges in online teaching include technological issues, creativity, interactions, class duration, inaccessibility issues and student emotional support, whereas the challenges in conducting online assessments are in terms of the different evaluation platforms, the issue of plagiarism and authenticity, marking or grading online assessments and assessing non-verbal cues.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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