A1 Journal article (refereed)
Deep Networks for Collaboration Analytics : Promoting Automatic Analysis of Face-to-Face Interaction in the Context of Inquiry-Based Learning (2021)

Lämsä, J., Uribe, P., Jiménez, A., Caballero, D., Hämäläinen, R., & Araya, R. (2021). Deep Networks for Collaboration Analytics : Promoting Automatic Analysis of Face-to-Face Interaction in the Context of Inquiry-Based Learning. Journal of Learning Analytics, 8(1), 113-125. https://doi.org/10.18608/jla.2021.7118

JYU authors or editors

Publication details

All authors or editors: Lämsä, Joni; Uribe, Pablo; Jiménez, Abelino; Caballero, Daniela; Hämäläinen, Raija; Araya, Roberto

Journal or series: Journal of Learning Analytics

eISSN: 1929-7750

Publication year: 2021

Volume: 8

Issue number: 1

Pages range: 113-125

Publisher: Society for Learning Analytics Research

Publication country: Canada

Publication language: English

DOI: https://doi.org/10.18608/jla.2021.7118

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/75023

Abstract

Scholars have applied automatic content analysis to study computer-mediated communication in computer-supported collaborative learning (CSCL). Since CSCL also takes place in face-to-face interactions, we studied the automatic coding accuracy of manually transcribed face-to-face communication. We conducted our study in an authentic higher-education physics context where computer-supported collaborative inquiry-based learning (CSCIL) is a popular pedagogical approach. Since learners’ needs for support in CSCIL vary in the different inquiry phases (orientation, conceptualization, investigation, conclusion, and discussion), we studied, first, how the coding accuracy of five computational models (based on word embeddings and deep neural networks with attention layers) differed in the various inquiry-based learning (IBL) phases when compared to human coding. Second, we investigated how the different features of the best performing computational model improved the coding accuracy. The study indicated that the accuracy of the best performing computational model (differentiated attention with pre-trained static embeddings) was slightly better than that of the human coder (58.9% vs. 54.3%). We also found that considering the previous and following utterances, as well as the relative position of the utterance, improved the model’s accuracy. Our method illustrates how computational models can be trained for specific purposes (e.g., to code IBL phases) with small data sets by using pre-trained models.

Keywords: collaborative learning; computer-assisted learning; interaction; content analysis

Free keywords: collaboration analytics; computational models; computer-supported collaborative learning; CSCL; CSCIL; deep networks; inquiry-based learning; word embedding

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Preliminary JUFO rating: 1