The Educational Impact of Distance Learning during the COVID-19 Pandemic on Students' Interaction in the Educational Process
Bibliographic record
Abstract
Due to the widespread impact of the Covid 19 pandemic, teachers and students were driven to relocate their teaching learning practices to the safety of their own homes. The goal of this research was to learn how Saudi students felt about distance learning online during the lockout of the College of Science and Humanities at Sulail, Prince Sattam Bin Abdullah University. Multiple methods were used to complete the study. Researchers collected data from 152 degree students of Management,Computer Science, English, Islamic Studies, Arabic department to rate their satisfaction with online learning settings using a Likert scale ranging from one to five points. A mixed research strategy was chosen for the study's purposes, with descriptive analysis used for the quantitative data analysis and content analysis used for the qualitative data analysis. Although some respondents expressed enthusiasm for online distance learning, the vast majority reported difficulties with the format and stated a preference for traditional in-person classes. Some students have expressed enthusiasm for this form of distance education. The study concluded that the findings can help policymakers and professors construct effective or efficient teaching ways to overcome difficult situations or pandemics, which is a summary of the study's main points.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".