Trends and Maps of Research Collaboration on Teacher Digital Literacy and Student Digital Citizenship: Bibliometric Analysis
Bibliographic record
Abstract
This study aims to identify trends, main topics, and patterns of scientific collaboration in the study of teachers' digital literacy and students' digital citizenship. Using bibliometric methods, this study analyzed scientific publications obtained from the Scopus database during the period 2016–2025. The data collected includes information about titles, authors, year of publication, keywords, affiliations, and journal sources. The analysis was conducted using VOSviewer software to map collaboration networks between researchers and between countries, as well as identify keywords that frequently appear in related publications. The results of the study show that teachers' digital literacy is a topic that has experienced a significant increase, especially since 2020, in line with the increasing need for digital learning during the pandemic. Meanwhile, studies on student digital citizenship are still relatively few but show a steady upward trend. The most frequently appearing keywords include "digital literacy", "digital citizenship", "teachers", "students", and "digital competence". Collaboration between countries is dominated by the United States, followed by countries such as the United Kingdom, Canada, China, and Australia. Indonesia began to engage in collaborative networks, albeit on a limited scale. This study provides a comprehensive overview of the direction and development of research, as well as identifying opportunities for further study in the field of digital literacy and digital citizenship in the context of education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.127 | 0.222 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".