Online Learning in Civic Education Research Trend: A Bibliometric Analysis
Bibliographic record
Abstract
Online learning in civic education (OLCE) has been going on since the 2000s. It has become an increasingly interesting topic in light of recent technological advances and emergencies, and it contributes to improving the quality of learning processes and outcomes. This study aimed to track the publication trends of OLCE in the Scopus database (2005–2024). The method used was bibliometric, with VOSviewer software analysis. The investigation found 123 documents, half of which were articles, and the rest distributed among conference papers, book chapters, conference reviews, books, and notes. These publications were written by 320 authors from 39 different countries and used nearly 800 keywords. The number of OLCE publications increased significantly in 2021 and reached its highest peak in 2024. VOSviewer analysis showed that civic education was connected to the keywords “online learning” and “e-learning” in the case of large nodes and close distances. However, other strategic keywords, such as “MOOC,” “digital citizenship,” “artificial intelligence,” and “social media” were detected in small nodes and far distances. The keyword “global citizenship education” was not directly connected; even “ChatGPT,” the most influential OpenAI today, was not seen at all. This could mean that the development of several strategic keywords would make for a potential research study in future. This research provides new insights for researchers and institutions involved in OLCE publication mapping for future development.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.127 | 0.176 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".