Means to Cope with Difficulties Related to Online Learning Experience during the Pandemic: A Focus Group Interview Study with College Students
Bibliographic record
Abstract
Higher education organizations worldwide, including those in South Korea, are currently facing a major global health crisis caused by coronavirus disease-19 (COVID-19) since the spring 2020 semester. This study aimed at determining the difficulties that college students perceive in online courses, what strategies they use to cope with the problems they encounter, and what policies they demand the university to implement to enhance the situations. Six students (both undergraduate and graduate) from a university in a metropolitan area in South Korea participated in a series of focus group interview (FGI) sessions. The study conducted its sessions on October 11, 14, 21, and 22, 2021 in the university. Due to the distinct features of the online learning environment, students reported that they have major difficulties in staying academically motivated, suffer from fatigue and boredom, and have a hard time utilizing studying strategies. To cope with the current situation, the results revealed that students voluntarily form study group sessions with peers, and demand the university to provide them with a series of psychological therapy sessions as well as group study sessions. Educators and university leaders may apply the study results in order to better support their college students, so that they would be able to cope with these unprecedented pandemic-related problems, particularly regarding the online learning experience. Adequate supports provided to both instructors and students by reorganizing IT equipments and operating systems would result in better facilitation of online learning.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".