Learning Management System (LMS) Research During 1991-2021: How Technology Affects Education
Bibliographic record
Abstract
Learning Management System (LMS) becomes one of the learning media tools that are quite widely used, so a study is needed to know the trend of LMS development. The objectives of this study are to analyze the types of documents, languages, contributing countries, top affiliates, sponsorship funding, top productive authors, research citations, subject areas, top source titles, trend mapping visualization and top-cited 100 publications, also review some publications on LMS research over 1991-2021 using bibliometric analysis. The metadata gathered is by Scopus database and analyzed by VOSViewer within 2.689 documents. The bibliometric analysis results show LMS research has conference paper being the most widely published document type and English is the most widely used language, the country with the most publications is the United States of America. National Natural Science Foundation of China became the top funding sponsor, the top affiliate is Bina Nusantara University and the most productive authors are Graf, S. Top cited author achieved by Davis, F.D., top subject areas are Computer Science. Then, Lecture Notes In Computer Science Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics became the title of the top source. Trends of LMS research in 1991-2021 are: 1) related to E-learning; 2) implementation of learning activities and students and teachers; 3) integration of technology in learning; 4) distance learning; 5) Technology education; 6) Online learning environment; 7) Interactive learning environment.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.020 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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".