A1 Journal article (refereed)
Staying at the front line of literature : How can topic modelling help researchers follow recent studies? (2021)


Lämsä, J., Espinoza, C., Tuhkala, A., & Hämäläinen, R. (2021). Staying at the front line of literature : How can topic modelling help researchers follow recent studies?. Frontline Learning Research, 9(3), 1-12. https://doi.org/10.14786/flr.v9i3.645


JYU authors or editors


Publication details

All authors or editors: Lämsä, Joni; Espinoza, Catalina; Tuhkala, Ari; Hämäläinen, Raija

Journal or series: Frontline Learning Research

eISSN: 2295-3159

Publication year: 2021

Volume: 9

Issue number: 3

Pages range: 1-12

Publisher: EARLI

Publication country: Belgium

Publication language: English

DOI: https://doi.org/10.14786/flr.v9i3.645

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/75141


Abstract

Staying at the front line in learning research is challenging because many fields are rapidly developing. One such field is research on the temporal aspects of computer-supported collaborative learning (CSCL). To obtain an overview of these fields, systematic literature reviews can capture patterns of existing research. However, conducting systematic literature reviews is time-consuming and do not reveal future developments in the field. This study proposes a machine learning method based on topic modelling that takes articles from a systematic literature review on the temporal aspects of CSCL (49 original articles published before 2019) as a starting point to describe the most recent development in this field (52 new articles published between 2019 and 2020). We aimed to explore how to identify new relevant articles in this field and relate the original articles to the new articles. First, we trained the topic model with the Results, Discussion, and Conclusion sections of the original articles, enabling us to correctly identify 74% (n = 17) of new and relevant articles. Second, clusterisation of the original and new articles indicated that the field has advanced in its new and relevant articles because the topics concerning the regulation of learning and collaborative knowledge construction related 26 original articles to 10 new articles. New irrelevant studies typically emerged in clusters that did not include any specific topic with a high topic occurrence. Our method may provide researchers with resources to follow the patterns in their fields instead of conducting repetitive systematic literature reviews.


Keywords: computer-assisted learning; collaborative learning; systematic reviews; information retrieval; machine learning


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Preliminary JUFO rating: 1


Last updated on 2022-17-01 at 12:05