What We Talk About When We Talk About Virtual Learning Environments
Bibliographic record
Abstract
This is a literature review analysing articles published on virtual learning environments (VLEs), otherwise known as learning management systems, in higher education in the years 2014–2018. The Web of Science database was used to identity relevant articles over this five-year period. The sample comprises 99 peer-reviewed, academic journal articles. A coding sheet is used to analyse each article, identifying the research method, the classification of research (macro, meso, or micro), the focus of research (students, instructors, or both), and, where applicable, the specific brand of VLE. Most output on VLEs is found to be quantitative, to focus on students and on the micro level of learning and teaching, to not have a clear theoretical focus, to not specify which brand of VLE is used, and to be produced in affluent countries. This article adds to the understanding of VLE research by identifying the most frequent foci of research on VLEs, as well as identifying areas that have been under-researched.
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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".