Research Data

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 modeling

Persistent Identifier

Date of first publication

2019

Publisher

PsychArchives

Is referenced by

Citation

  • Author(s) / Creator(s)
    Christ, Alexander
  • Author(s) / Creator(s)
    Penthin, Marcus
  • Author(s) / Creator(s)
    Kröner, Stephan
  • PsychArchives acquisition timestamp
    2019-10-08T12:43:03Z
  • Made available on
    2019-10-08T12:43:03Z
  • Date of first publication
    2019
  • 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.
    en
  • Abstract / Description
    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
    en
  • Sponsorship
    This research was supported by a grant from the Federal Ministry of Education and Research (01JKD1711) to Benjamin Jörissen and Stephan Kröner
    en
  • Persistent Identifier
    https://hdl.handle.net/20.500.12034/2234
  • Persistent Identifier
    https://doi.org/10.23668/psycharchives.2612
  • Language of content
    eng
  • Publisher
    PsychArchives
    en
  • Is referenced by
    https://doi.org/10.1177/0894439319888455
  • Is related to
    https://doi.org/10.23668/psycharchives.2611
  • Is related to
    https://doi.org/10.23668/psycharchives.2613
  • Is related to
    https://doi.org/10.23668/psycharchives.2614
  • Is related to
    https://doi.org/10.1177/0894439319888455
  • Keyword(s)
    big data
    en
  • Keyword(s)
    text mining
    en
  • Keyword(s)
    aesthetic
    en
  • Keyword(s)
    arts
    en
  • Keyword(s)
    and cultural education
    en
  • Keyword(s)
    digitalization
    en
  • Keyword(s)
    predictive modeling
    en
  • Dewey Decimal Classification number(s)
    150
  • Title
    List of Included Articles for: Big Data and Digital Aesthetic, Arts and Cultural Education: Hot Spots of Current Quantitative Research
    en
  • Alternative title
    Dataset for: Big Data and Digital Aesthetic, Arts and Cultural Education: Hot Spots of Current Quantitative Research
    en
  • DRO type
    researchData
    en