The Effect of Coronavirus (Covid19) Outbreak on Education Systems: Evaluation of Distance Learning System in Turkey
Bibliographic record
Abstract
Due to Covid19, measures have been taken to minimize interaction, maintain social isolation, and ensure interpersonal social distance. As a result of these decisions taken by the authorities, educational activities in Turkey were suspended at first. After some time, lessons were given in the form of distance learning on digital platforms. This research was carried out to evaluate the efficiency, positive and negative aspects of the distance learning system, and its shortcomings from the viewpoints of the students. 594 students, 139 of whom are from private universities, 455 of whom are from state universities, faculties of physical sciences and sports schools, participated in the study in the 2019-2020 academic year. In the research, a survey program known as “Web-Based Instructional Attitude Scale” was used to collect data for the purpose of the research. The data obtained at the end of the research was analyzed with the SPSS 22 statistics program and the significance level was taken as 0.05 among the variables. In order to get an idea about the distribution of the data, firstly the normality of the distributions, and then the skewness and kurtosis tests were examined. According to test results, Independent Sample T test was performed in binary comparisons, One-Way Variance Analysis analysis in multiple comparisons, and correlation test was used to determine the relationship between variables. According to the answers given by the students who participated in the study, although they now ensured that their education activities continue without disruption; It has been determined that the courses taught in the form of distance learning are not as effective as face-to-face education, they are insufficient in terms of efficiency for students, and technical problems in the system negatively affect students’ motivation to learn. As a result, no matter how practical the distance learning system is during times of crisis, it may not be as efficient as face-to-face education, and it requires more technical development and always be ready for use.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".