The Impact of Implementing Full E-learning During Covid-19 on the Students’ Academic Performance in the Courses of Accounting and English Language (A Case Study: Students of the Department of Administrative Sciences - Community College in Khamis Mushait- King Khalid University- KSA)
Bibliographic record
Abstract
This study aimed at figuring out the effect of full e-learning on the students’ academic performance in accounting courses, which include multiple mathematical calculations compared with the English Language courses, which are free of mathematical calculations. A questionnaire was designed and distributed among a random sample of students during the second semester of the academic year 2019-2020, during which full e-learning was implemented due to the Coronavirus Pandemic. The sample included (302) out of (1411) male and female students at the Department of Administrative Sciences, whose major is accounting and business administration. Besides, they study English language as part of the general courses at the Community College in Khamis Mushait. The study found that, there is a statistically significant effect regarding the features of full e-learning on the students’ academic performance with respect to the accounting and English Language courses. The problems related to e-learning did not affect the students' academic performance in accounting courses, while it impacted negatively on the students' academic performance in the English language courses. The study recommended that, the educational institutions should continuously develop the e-learning atmosphere in order to become conducive, attractive and creative.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".