Knowledge Base, Hot Topics, and Frontier Evolution of Adult Online Learning Research in the Last Decade: CiteSpace-Based Visual Analytics
Bibliographic record
Abstract
Adult online learning, as an important form of realizing information technology in education, is crucial to achieving the sustainable development goals. In the past decade, with the rapid development of internet technology and the global spread of the COVID-19 epidemic, the number of online open courses has surged, attracting the participation of a large number of adults. Exploring academic research on adult online learning contributes to an in-depth understanding of adult online learning and its impact on global education. Using the information visualization software CiteSpace to analyze 691 Social Sciences Citation Index (SSCI)-indexed research papers on adult online learning in the Web of Science database, the results showed that six scholars from the United States, the United Kingdom, Australasia, and Canada, and seven highly cited articles established the knowledge base in the field of adult online learning, focusing on innovations in technology adoption, health support, and educational policy practices. Research frontiers include “women,” “people,” and “stress.” Evolutionary paths range from the interpretation of adult online learning outcomes to a focus on global education policy implications to emerging technologies. In the future, research will continue to diversify and grow, contributing to the enrichment and renewal of the adult education body of knowledge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".