Is There a Change? Distance Education Studies in COVID-19 Pandemic
Bibliographic record
Abstract
With the Coronavirus epidemic in China at the end of 2019 and in the rest of the World in the first quarter of 2020, all educational institutions started to give distance education partially or completely. Institutions were not ready for such a process, and they failed to the necessary preparations. That's why these applications are called emergency distance education. As a result, for the evaluation of distance education practices during the pandemic period, a number of studies have been published and continue to be conducted. Evaluation of these publications with content analysis will be very important in analyzing this period in the future. In this respect, the purpose of this study was to perform the content analysis of the articles related to distance education practices in the pandemic period. As a result of the search done using the Scopus database, a total of 180 articles were examined within the scope of the present study. The articles were examined in terms of country, number of times of citation, keywords, participants, data collection tools, variables / research interests, research designs and methods. Consequently, it was seen that the publications were mostly done in the USA; Opinion, Reflection and Review studies were conducted and cited most; that mostly the keywords of learning, online, education and covid-19 were used; that the participants were mostly undergraduate students; that the most frequent data collection tool was questionnaire; and that the most frequent dependent variables were engagement, readiness, perception and self-efficacy. The findings obtained were discussed in line with the related literature, and various suggestions were put forward.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".