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Features of Distance Learning in Higher Education Institutions in The Context of The Covid-19 Pandemic

2022· article· en· W4220874274 on OpenAlexaboutno aff
Oksana N. Goncharova, Milera Yu. Halilova

Bibliographic record

VenueOpen Education · 2022
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationContext (archaeology)Descriptive statisticsQuality (philosophy)Higher educationWorkloadMathematics educationPandemicIBMMedical educationPsychologyCoronavirus disease 2019 (COVID-19)Quarter (Canadian coin)StatisticsComputer scienceMathematicsMedicinePolitical scienceGeography

Abstract

fetched live from OpenAlex

The purpose of the research is to study the quality of distance learning. The paper identifies the main problems that arose during the transition to distance learning due to the epidemiological situation in 2020-2021 in the Russian Federation. Materials and methods. To obtain data, a survey was conducted among students of the Crimean Federal University named after V.I. Vernadsky. Because of testing the quality of distance learning, an array of 187 records was obtained. The data were processed in the IBM SPSS Statistics 23.0 program using descriptive statistics methods. The following methods were used in the work: analysis of methodological, pedagogical, psychological, scientific, technical and methodological literature on the research problem; private methods - classification, systematization, comparison, analysis and generalization of pedagogical experience, modeling of the content of education. 60.9% of women and 39.1% of men took part in the survey, among them the majority of full-time students (96.2%), less – extramural studies (2.7%) and composite study mode (1.1%). Among the levels of higher education: undergraduate students – 86.8%, graduate students – 10.4% and postgraduate students – 2.7%. Results. The study showed that half of the students of higher educational institutions have adapted to the new conditions of distance learning. Half of them have decreased learning motivation, only a quarter of the respondents are completely satisfied with the quality of the learning process, half note an increase in the workload. When evaluating the quality of software, students noted inconveniences in using the Moodle platform: problems of poor presentation of lectures, technical interruptions in the process of playing back the material, and the absence of valid hyperlinks. More than half of the students believe that they are provided with the necessary amount of material for independent study. When analyzing the degree of social activity, students noted a decrease in communication with classmates and the need for “live” communication with lecturers. More than half of the students note an increase in the level of general anxiety. In the choice of works that lecturers used more often in their classes, students note issuing of tasks for independent performance, placement of educational respondents, half faced difficulties in solving practical tasks without the help of a lecturer and did not always cope with a large amount of given information. Most of the problems in the initial stages of implementation were quickly resolved. Conclusion. Based on the study, it was found that the number of advantages of distance learning prevails over the number of disadvantages. The potential for using training in this format is at a high level, thus providing prospects for its use. However, the study also showed that further work is needed to improve feedback between lecturers and students in the face of declining non-verbal communication. The study proposes ways to eliminate the main difficulties faced by both students and lecturers during distance learning

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.141
GPT teacher head0.395
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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