A4 Article in conference proceedings
Automatic Content Analysis of Computer-Supported Collaborative Inquiry-Based Learning Using Deep Networks and Attention Mechanisms (2020)

Uribe, P., Jiménez, A., Araya, R., Lämsä, J., Hämäläinen, R., & Viiri, J. (2020). Automatic Content Analysis of Computer-Supported Collaborative Inquiry-Based Learning Using Deep Networks and Attention Mechanisms. In P. Vittorini, T. Di Mascio, L. Tarantino, M. Temperini, R. Gennari, & F. De la Prieta (Eds.), Methodologies and Intelligent Systems for Technology Enhanced Learning, 10th International Conference (pp. 95-105). Springer International Publishing. Advances in Intelligent Systems and Computing, 1241. https://doi.org/10.1007/978-3-030-52538-5_11

JYU authors or editors

Publication details

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

Parent publication: Methodologies and Intelligent Systems for Technology Enhanced Learning, 10th International Conference

Parent publication editors: Vittorini, P.; Di Mascio, T.; Tarantino, L.; Temperini, M.; Gennari, R.; De la Prieta, F.

Conference:

Place and date of conference: L'Aquila, Italy, 7.-9.10,2020

ISBN: 978-3-030-52537-8

eISBN: 978-3-030-52538-5

Journal or series: Advances in Intelligent Systems and Computing

ISSN: 2194-5357

eISSN: 2194-5365

Publication year: 2020

Number in series: 1241

Pages range: 95-105

Publisher: Springer International Publishing

Place of Publication: Cham

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.1007/978-3-030-52538-5_11

Publication open access: Not open

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/71837

Abstract

Computer-supported collaborative inquiry-based learning (CSCIL) represents a form of active learning in which students jointly pose questions and investigate them in technology-enhanced settings. Scaffolds can enhance CSCIL processes so that students can complete more challenging problems than they could without scaffolds. Scaffolding CSCIL, however, would optimally adapt to the needs of a specific context, group, and stage of the group's learning process. In CSCIL, the stage of the learning process can be characterized by the inquiry-based learning (IBL) phase (orientation, conceptualization, investigation, conclusion, and discussion). In this presentation, we illustrate the potential of automatic content analysis to find the different IBL phases from authentic groups' face-to-face CSCIL processes to advance the adaptive scaffolding. We obtain vector representations from words using a well-known feature engineering technique called Word Embedding. Subsequently, the classification task is done by a neural network that incorporates an attention layer. The results presented in this work show that the proposed best performing model adds interpretability and achieves a 58.92{\%} accuracy, which represents a 6{\%} improvement compared to our previous work, which was based on topic-models.

Keywords: computer-assisted learning; natural language; investigative learning; collaborative learning; neural networks (information technology); content analysis

Free keywords: inquiry based learning; deep neural networks; natural language processing

Contributing organizations

Related projects

Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 1