A Bibliometric Analysis of Digital Literacy in Remote Learning
Bibliographic record
Abstract
The rapid digital transformation of education has significantly influenced remote learning, with digital literacy emerging as a crucial competency for students, educators, and institutions. This study employs a bibliometric analysis to explore research trends, influential authors, and thematic developments in digital literacy within remote learning contexts from 2020 to 2024. Using Scilit.net, the study analyzes 12,809 academic publications through quantitative methods, including co-authorship networks, keyword co-occurrence analysis, and citation mapping. The findings reveal a substantial increase in research interest following the COVID-19 pandemic, highlighting key themes such as digital competence, online pedagogy, and the digital divide. The study identifies Indonesia, Spain, and China as leading contributors to digital literacy research, with university-led initiatives and policy-driven frameworks playing a pivotal role in shaping digital education. The analysis also underscores persistent challenges, including disparities in technological access, the need for standardized digital literacy curricula, and the rapid evolution of educational technologies. These findings provide valuable insights for educators, policymakers, and researchers, emphasizing the need for interdisciplinary collaboration and continuous digital literacy development. This study contributes to the growing body of literature by mapping the trajectory of digital literacy research and offering a foundation for future investigations to foster equitable and effective remote learning environments.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.070 | 0.254 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".