A Survey of the Collaboration Rate of Authors in the E-Learning Subject Area over a 10-Year Period (2005-2014) Using Web of Science
Bibliographic record
Abstract
Partnership is one of the mechanisms of scientific development, and scientific collaboration or co-authorship is considered a key element in the progress of science. This study is a survey with a scientometric approach focusing on the field of e-learning products over 10 years. In an Advanced Search of the Web of Science, the following search formula was used: TS=("m-learning" OR "mlearning" OR "mobile learning" OR "online learning" OR "virtual learning" OR "distance learning" OR "electronic learning"). The study was limited to 2005-2014, and the document type was limited to paper. A total of 4292 documents were found, to which 12362 authors contributed. The articles were evaluated individually and their information was entered into Microsoft Office Excel 2007 for analysis using the collaborative coefficient formula. In the Computers and Education journal, articles with two authors are the most frequent. The United States, with the highest production of articles in the field of e-learning, tends to produce articles with two authors. In 2014, the most productive year, articles with three authors were more frequent. The highest collaborative coefficient is in 2005 and 2014. Our findings show that despite the need for research activities as a team, the authors in the field of e-learning tend to publish their papers alone or in a team of two.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".