A4 Article in conference proceedings
Automatic content analysis in collaborative inquiry-based learning (2019)
Espinoza, C., Lämsä, J., Araya, R., Hämäläinen, R., Jimenez, A., Gormaz, R., & Viiri, J. (2019). Automatic content analysis in collaborative inquiry-based learning. In O. Levrini, & G. Tasquier (Eds.), Proceedings of ESERA 2019 : The Beauty and Pleasure of Understanding : Engaging with Contemporary Challenges Through Science Education (pp. 2041-2050). University of Bologna. https://www.esera.org/publications/esera-conference-proceedings/esera-2019
JYU authors or editors
Publication details
All authors or editors: Espinoza, Catalina; Lämsä, Joni; Araya, Roberto; Hämäläinen, Raija; Jimenez, Abelino; Gormaz, Raul; Viiri, Jouni
Parent publication: Proceedings of ESERA 2019 : The Beauty and Pleasure of Understanding : Engaging with Contemporary Challenges Through Science Education
Parent publication editors: Levrini, Olivia; Tasquier, Giulia
Place and date of conference: Bologna, Italia, 26.-30.8.2019
eISBN: 978-88-945874-0-1
Publication year: 2019
Pages range: 2041-2050
Number of pages in the book: 2056
Publisher: University of Bologna
Place of Publication: Bologna
Publication country: Italy
Publication language: English
Persistent website address: https://www.esera.org/publications/esera-conference-proceedings/esera-2019
Publication open access: Openly available
Publication channel open access: Open Access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/73911
Abstract
In the field of science education, content analysis is a popular way to analyse collaborative inquiry-based learning (CIBL) processes. However, content analysis is time-consuming when conducted by humans. In this paper, we introduce an automatic content analysis method to identify the different inquiry-based learning (IBL) phases from authentic student face-to-face discussions. We illustrate the potential of automatic content analysis by comparing the results of manual content analysis (conducted by humans) and automatic content analysis (conducted by computers). Both the manual and automatic content analyses were based on manual transcriptions of 11 groups’ CIBL processes. Two researchers performed the manual content analysis, in which each utterance of the groups’ discussions was coded to an IBL phase. First, an algorithm was trained with some of the manually coded utterances to prepare the automatic content analysis. Second, the researchers tested the ability of the algorithm to automatically code the utterances that were not used in the training. The algorithm was a linear support vector machine (SVM) classifier. Since the input of the SVM must be a numerical vector of constant size, we used a topic model to build a feature vector representation for each utterance. The correspondence of the manual and automatic content analyses was 52.9%. The precision of the classifier varied from 49% to 68%, depending on the IBL phase. We discuss issues to consider in the future when improving automatic content analysis methods. We also highlight the potential benefits of automatic content analysis from the viewpoint of science teachers and science education researchers
Keywords: investigative learning; collaborative learning; learning process; conversation analysis; text mining
Free keywords: Inquiry-oriented learning; Quantitative methods
Contributing organizations
Related projects
- Competitive funding to strengthen universities’ research profiles. Profiling actions at the JYU, round 1
- Hämäläinen, Keijo
- Research Council of Finland
Ministry reporting: Yes
Reporting Year: 2020
JUFO rating: 0