Features of Distance Learning in Higher Education Institutions in The Context of The Covid-19 Pandemic
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».