Deep Networks for Collaboration Analytics : Promoting Automatic Analysis of Face-to-Face Interaction in the Context of Inquiry-Based Learning
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
Published inJournal of Learning Analytics
© 2021 the Authors
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. ...
PublisherSociety for Learning Analytics Research
Publication in research information system
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Related funder(s)Academy of Finland
Funding program(s)Research profiles, AoF
Additional information about fundingThe publication of this article received financial support from the Academy of Finland [grant numbers 292466 and 318095, the Multidisciplinary Research on Learning and Teaching profiles I and II of University of Jyväskylä]and ANID/PIA/Basal Funds for Excellence (FB0003).
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