The Algorithm for Assessing the Effects of Distance Education in General Education on Students’ Academic Results
Bibliographic Data
| ID | 22045133 |
|---|---|
| Authors | Vaidas Gaidelys (0000-0002-3790-0542, Kaunas University of Technology), Ruta Ciutiene (0000-0003-4821-0093, Kaunas University of Technology, corresponding author), Gintautas Cibulskas (Kaunas University of Technology), Asta Baliute (0000-0002-9373-5449, Kaunas University of Technology) |
| Year | 2023 |
| Volume | 13 |
| Issue | 9 |
| Pages | 957 |
| Publication date | 2023-09-19 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Education Sciences (JOURNAL) |
| Journal identifiers | ISSN: 2227-7102 • E-ISSN: 2227-7102 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/educsci13090957 |
| OpenAlex | W4386893053 |
| Language | EN |
| Citations received | 1 |
| References cited | 17 |
Distance education has gained great interest during the COVID-19 pandemic, when schools all over the world faced the challenge of transferring the traditional education processes to digital platforms. In this context, school communities not only discovered the new opportunities, but also encountered a number of problems when trying to provide high quality distance education and minimise learning losses for students. The rapid transition to distance education has had a negative impact on the academic results and daily routines of the students from all social groups, especially socially vulnerable families. Thus, it has become relevant to research various aspects of students’ learning losses. The purpose of this article is to theoretically substantiate the algorithm intended for assessing the effects of distance education on students’ academic results. The research was based on the methods of scientific literature analysis, secondary data analysis, theoretical modelling, inductive content analysis, and expert evaluation. The algorithm is intended for general education institutions. Results: After conducting scientific literature and secondary data analysis, the theoretical model for assessing the effects of distance education on students’ academic results was developed and substantiated. The model consists of four structural parts, represented by 11 criteria, which reveal the effects of distance education in general education following the appropriate algorithm
Distance education · Higher education · Mathematics education · Political science · Computer Science · COVID-19 and Mental Health · Educational Innovations and Challenges · Psychology · Technology-Enhanced Education Studies
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| Unique citing works | 1 |
|---|---|
| Citations per year | 0.5 |
| Citation span | 2024 - 2024 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |