Distance Learning in Higher Education: The Experience of the Covid-19 Pandemic and War in Ukraine
Bibliographic record
Abstract
Distance learning has become one of the most popular educational trends of the 21st century, and the COVID-19 pandemic and war in Ukraine has only accelerated the process of its integration into the education sector. The purpose of our work is to study the influence of the online learning format on the adaptation and academic success of students, as well as to search for promising analogues. The methodology. In addition to a comprehensive theoretical analysis, which included a comparison of different approaches and research, we used the method of interviewing respondents, which involved 200 first-year students from 6 Ukrainian higher education institutions (H.S. Skovoroda Kharkiv National Pedagogical University, Taras Shevchenko National University of Kyiv, V. N. Karazin Kharkiv National University, National Technical University of Ukraine Kyiv Polytechnic Institute, State Biotechnology University, Kharkiv National University of Radio Electronics). The survey was conducted online using the Google Forms platform in the period from December 19 to 26, 2021, the calculation and visualization of the received data were performed using Microsoft Office tools. Fisher's statistical test (online-tool) was used to establish differences between the indicators of academic success of the respondents of the two groups. Results. We decided to compare the academic success of students who study online with students included in the blended learning system. Thus, only 8% of the respondents who took the course in an online format received a mark of 5 at the end of the academic semester, while almost a quarter (25%) of the students of the second group who took the course in blended learning received the highest score. We also asked respondents to evaluate the process of their own adaptation to new conditions (distance and blended learning). The results of the survey showed that the adaptation process proceeds much easier in the conditions of the blended learning or Flipped Classroom blended learning model, while the adaptation of respondents to the online format had a number of problems. Conclusion. Online learning has a high potential, which is difficult to realize due to the high demands on technical support, communication problems in an unfamiliar space, and the lack of social presence of participants in the educational process. Blended learning, as a combination of full-time and distance learning, can offset the shortcomings of online learning and realize its potential. The next step in our research will be to compare the performance of another learning models.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".