Coronavirus (COVID-19) and Online Studying Cas’s Study Alasala University KAS Law School
Bibliographic record
Abstract
The first quarter of 2020 is one of the most difficult times for the region and whole world. Wherethe epidemic (COVID-19) invaded the world and thus affected many aspects of human aspects: interms of low industrial production, the global economy stopping work, and educational aspects closedto students. All educational institutions modified their academic assessments, and then educationalprograms were modified during this period. As stakeholders and management of higher educationinstitutions do not have another option is to take advantage of Internet and new technologies, andtherefore go to online learning to continue academic activities at all levels of education around theregion and the whole world.The study aims at assess whether students in KSA, especially Alasala University in general, and inparticular the College of Law, if they are satisfied with the “group” online learning experience in highereducation institutions, therefore, the study used an online survey to investigate the level of satisfactionwith online learning and how students adapt to these “new initiatives”.The study undertakes a case study of a law school within a leading university such as Alasala, which hasimplemented a quality system. The study reviews the development of the quality system and examinesthe concept of service quality in law education. The aim of this study is to address the paucity of servicequality research in law education in this region. Empirical research is used to determine the factorsthat influence student evaluation of service quality. With data collected from 664 students, the studyidentifies six factors that influence students’ evaluations of service quality. Research implications of thestudy are then discussed. The study results indicate that the implementation of online learning programswas a very impressive idea as the majority of students included in the sample supported the initiative.
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How this classification was reachedexpand
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".