Factors Influencing Class Satisfaction in Online and Offline Blended Classes - Focusing on Digital Competence, Interactions between Teachers and Learners, and Interactions between Team-members
Bibliographic record
Abstract
The class environment in 2022 is based on full face-to-face classes in accordance with the easing of social distancing, but many instructors and learners are conducting a mixture of non-face-to-face and face-to-face classes. In the post-COVID-19 class environment, a plan to effectively apply online and offline blended classes is needed. This study analyzes how college students' digital competence, teacher-learner interaction, and team-member interaction affect class satisfaction in online and offline blended classes. The research subjects of the study were students of four-year university E located in Gyeonggi-do. Data were collected through an online survey method between June and July 2022. The analysis method was frequency analysis and descriptive statistical analysis. In addition, correlation analysis was conducted to confirm the validity of variables and to confirm multicollinearity. Moreover, factor factor analysis was conducted to confirm the validity of the measurement tool developed in this study. Finally, multiple regression analysis was conducted to verify the influence of college students' digital competence, teacher-learner interaction, and team-member interaction on class satisfaction of online and offline blended classes. As a result of the study, it was found that interaction factors between teacher-learners and team-members, excluding digital competency, affect class satisfaction in online and offline blended classes. These results suggest that to improve class satisfaction in operating online and offline blended classes in the post-COVID-19 era, it is necessary to come up with teaching strategies that can enhance interaction between teacher-learners and team-members.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".