Contributing Stressors to Online Language Learning Difficulties at King Saud University: Basis for Adaptive Teaching Methodologies
Bibliographic record
Abstract
The application of technology in education has become a trend in teaching and the focus of interest in multiple studies because of its various means of implementation. The COVID-19 pandemic has even amplified the need to adapt to different digital-related modalities, one of which is online learning. Although online learning has many benefits for teachers and students, it still poses numerous challenges for education stakeholders. The current study aims to analyze the stressors contributing to difficulties induced by online language learning as experienced by both the students and teachers at King Saud University (KSU). The study also aims to serve as a basis for developing adaptive teaching modalities. This study uses a mixed-descriptive quantitative and qualitative research method, with an open-ended question and a 5-point Likert scale questionnaire. The findings suggest that both the teachers and students frequently felt stressed by the identified contributing stressors and felt that online language learning was difficult for both clusters of respondents. Furthermore, the study concludes with the suggestion that the administrators should consider the needs of the teachers and students and offer them the necessary support to help alleviate the stress and difficulties they are experiencing with online language learning.  
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".