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Virtual and remote labs in education: A bibliometric analysis
Open accessAbstract
Laboratory experimentation plays an essential role in engineering and scientific education. Virtual and remote labs reduce the costs associated with conventional hands-on labs due to their required equipment, space, and maintenance staff. Furthermore, they provide additional benefits such as supporting distance learning, improving lab accessibility to handicapped people, and increasing safety for dangerous experimentation. This paper analyzes the literature on virtual and remote labs from its beginnings to 2015, identifying the most influential publications, the most researched topics, and how the interest in those topics has evolved along the way. To do so, bibliographical data gathered from ISI Web of Science, Scopus and GRC2014 have been examined using two prominent bibliometric approaches: science mapping and performance analysis. Display Omitted Laboratory experimentation plays an essential role in engineering and sci-entific education.Virtual and remote labs are emerging as a valuable alternative to conven-tional hands-on labs.This paper analyzes the literature on virtual and remote labs from 1993 to 2015.4405 records retrieved from ISI Web of Science, Scopus and GRC2014 are processed.Two bibliometric approaches are applied: performance analysis and science mapping.
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Metadata
- Authors
- Heradio, R., de la Torre, L., Galan, D., Cabrerizo, F. J., Herrera-Viedma, E., & Dormido, S.
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2016-07
- Volume
- 98
- Pages
- 14-38
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W2510091332
- Cited by
- 574
- References
- 125
- FWCI
- 54.39
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
