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:
- International Conference in Methodologies and intelligent Systems for Techhnology Enhanced Learning
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
- 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: 1