Big Data and Digital Aesthetic, Arts, and Cultural Education. Hot Spots of Current Quantitative Research.

Autor/inn/en	Christ, Alexander; Penthin, Marcus; Kröner, Stephan
Titel	Big Data and Digital Aesthetic, Arts, and Cultural Education. Hot Spots of Current Quantitative Research.
Quelle	In: Social science computer review, 39 (2019) 5, S. 821-843 PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Beigaben	Literaturangaben
Sprache	englisch
Dokumenttyp	online; Zeitschriftenaufsatz
ISSN	0894-4393; 1552-8286
DOI	10.1177/0894439319888455
Schlagwörter	Forschungsergebnis; Forschungsmethode; Methode; Quantitative Forschung; Systematic Review; Videospiel; Literatur; Zitat; Datenbank; Datengewinnung; Digitalisierung; Kunst; Ästhetik; Kulturelle Bildung; Screening; Abstract; Modellierung; Original; Publikation; Synthese; Datenaufbereitung
Abstract	Systematic reviews are the method of choice to synthesize research evidence. To identify main topics (so-called hot spots) relevant to large corpora of original publications in need of a synthesis, one must address the "three Vs" of big data (volume, velocity, and variety), especially in loosely defined or fragmented disciplines. For this purpose, text mining and predictive modeling are very helpful. Thus, [the authors] applied these methods to a compilation of documents related to digitalization in aesthetic, arts, and cultural education, as a prototypical, loosely defined, fragmented discipline, and particularly to quantitative research within it (QRD-ACE). By broadly querying the abstract and citation database Scopus with terms indicative of QRD-ACE, [the authors] identified a corpus of N = 55,553 publications for the years 2013-2017. As the result of an iterative approach of text mining, priority screening, and predictive modeling, [the authors] identified n = 8,304 potentially relevant publications of which n = 1,666 were included after priority screening. Analysis of the subject distribution of the included publications revealed video games as a first hot spot of QRD-ACE. Topic modeling resulted in aesthetics and cultural activities on social media as a second hot spot, related to 4 of k = 8 identified topics. This way, [the authors] were able to identify current hot spots of QRD-ACE by screening less than 15% of the corpus. [The authors] discuss implications for harnessing text mining, predictive modeling, and priority screening in future research syntheses and avenues for future original research on QRD-ACE. (Orig.).
Erfasst von	Externer Selbsteintrag
Update	2021/2