Shifting Conversations on Online Distance Education in South Korean Society During the COVID-19 Pandemic: A Topic Modeling Analysis of News Articles
Bibliographic record
Abstract
This study explored the dominant discourses on online distance education (ODE) that emerged in South Korean society before, during, and after the COVID-19 pandemic. The authors conducted a topic modeling analysis of 8,865 news articles published by 24 South Korean media outlets between 2019 and 2021. Using the Latent Dirichlet Allocation (LDA) algorithm and social network analysis software (NetMiner), the top five topics and the top ten words associated with each topic were identified from each period. The authors observed significant changes not only in the number of news articles but also in the depth of the conversations published each year. The results have revealed several key points. First, ODE, previously considered marginal and abnormal, gained in normality across all educational levels in Korean society. Second, ODE discourses have been shaped by the unique cultural, historical, and technological infrastructure in South Korea. Third, a clear division between social-justice-oriented and business-oriented ODE discourses reflect a persistent inequality in Korean society. Finally, ODE discourses matured in 2021, with more critical and realistic perspectives on both the positives and negatives of ODE. The useful implications of such insights for post-pandemic ODE research and practice are further discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".