Analysis of Computational Bibliometric Mapping in Multimedia for Art Learning Media Publications using VOSviewer
DOI:
https://doi.org/10.37934/araset.52.1.163179Keywords:
Bibliometrics, Computational analysis, Multimedia, Art learning media, VOSviewerAbstract
This research explores the development of fine arts learning media using interactive multimedia research. The goal can be a reference for researchers in conducting and defining research themes, especially around multimedia learning. The method is used through a bibliographic approach with computational mapping analysis using VOSviewer. Article data is retrieved from the Google Scholar database using a publish-or-perish reference management application. Article titles and abstracts serve as guidelines when searching for keywords like "art," "learning media," and "multimedia." We found 447 seemingly related articles. The study period used as learning material is a paper from the last five years (2018 to 2023) indexed by Google Scholar. The results of research on fine arts learning media using interactive multimedia can be classified into four terms. The first term "Training" belongs to cluster 2 with a total of 34 links, an overall link strength of 138, and 59 events. The second term, "Media", belongs to cluster 2, with a total of 30 links, an overall connection strength of 142, and 74 events. The third term, "Multimedia", belongs to cluster 2, with a total of 27 links, an overall connection strength of 110, and 55 events. The fourth term "art" belongs to cluster 1 with a total of 34 links, an overall link strength of 138 and 59 events. From 2018 to 2020, the survey found research fluctuations in several media-based art learning media studies (65, 110, and 124 publications per year, respectively). The number of studies decreased from 44 (2021) to 16 (2023) from 2021 to 2023. Thus, there are still many opportunities for researchers to discuss it.