List of Included Articles for: Big Data and Digital Aesthetic, Arts and Cultural Education: Hot Spots of Current Quantitative Research
Dataset for: Big Data and Digital Aesthetic, Arts and Cultural Education: Hot Spots of Current Quantitative Research
Author(s) / Creator(s)
Christ, Alexander
Penthin, Marcus
Kröner, Stephan
Abstract / Description
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, we 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, we 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, we 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, we were able to identify current hot spots of QRD-ACE by screening less than 15% of the corpus. We discuss implications for harnessing text mining, predictive modeling, and priority screening in future research syntheses and avenues for future original research on QRD-ACE.
Dataset for: Christ, A., Penthin, M., & Kröner, S. (2019). Big Data and Digital Aesthetic, Arts, and Cultural Education: Hot Spots of Current Quantitative Research. Social Science Computer Review, 089443931988845. https://doi.org/10.1177/0894439319888455
Keyword(s)
big data text mining aesthetic arts and cultural education digitalization predictive modelingPersistent Identifier
Date of first publication
2019
Publisher
PsychArchives
Is referenced by
Citation
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List of Included Articles.csvCSV - 322.95KBMD5: cdf339334d28c6a3a01486608e042a5d
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There are no other versions of this object.
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Author(s) / Creator(s)Christ, Alexander
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Author(s) / Creator(s)Penthin, Marcus
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Author(s) / Creator(s)Kröner, Stephan
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PsychArchives acquisition timestamp2019-10-08T12:43:03Z
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Made available on2019-10-08T12:43:03Z
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Date of first publication2019
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Abstract / DescriptionSystematic 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, we 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, we 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, we 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, we were able to identify current hot spots of QRD-ACE by screening less than 15% of the corpus. We discuss implications for harnessing text mining, predictive modeling, and priority screening in future research syntheses and avenues for future original research on QRD-ACE.en
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Abstract / DescriptionDataset for: Christ, A., Penthin, M., & Kröner, S. (2019). Big Data and Digital Aesthetic, Arts, and Cultural Education: Hot Spots of Current Quantitative Research. Social Science Computer Review, 089443931988845. https://doi.org/10.1177/0894439319888455en
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SponsorshipThis research was supported by a grant from the Federal Ministry of Education and Research (01JKD1711) to Benjamin Jörissen and Stephan Kröneren
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Persistent Identifierhttps://hdl.handle.net/20.500.12034/2234
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Persistent Identifierhttps://doi.org/10.23668/psycharchives.2612
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Language of contenteng
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PublisherPsychArchivesen
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Is referenced byhttps://doi.org/10.1177/0894439319888455
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Is related tohttps://doi.org/10.23668/psycharchives.2611
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Is related tohttps://doi.org/10.23668/psycharchives.2613
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Is related tohttps://doi.org/10.23668/psycharchives.2614
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Is related tohttps://doi.org/10.1177/0894439319888455
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Keyword(s)big dataen
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Keyword(s)text miningen
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Keyword(s)aestheticen
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Keyword(s)artsen
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Keyword(s)and cultural educationen
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Keyword(s)digitalizationen
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Keyword(s)predictive modelingen
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Dewey Decimal Classification number(s)150
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TitleList of Included Articles for: Big Data and Digital Aesthetic, Arts and Cultural Education: Hot Spots of Current Quantitative Researchen
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Alternative titleDataset for: Big Data and Digital Aesthetic, Arts and Cultural Education: Hot Spots of Current Quantitative Researchen
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DRO typeresearchDataen