A4 Artikkeli konferenssijulkaisussa
Automatic Content Analysis of Computer-Supported Collaborative Inquiry-Based Learning Using Deep Networks and Attention Mechanisms (2020)


Uribe, Pablo; Jiménez, Abelino; Araya, Roberto; Lämsä, Joni; Hämäläinen, Raija; Viiri, Jouni (2020). Automatic Content Analysis of Computer-Supported Collaborative Inquiry-Based Learning Using Deep Networks and Attention Mechanisms. In Vittorini P, Di Mascio T, Tarantino L, Temperini M, Gennari R, Prieta F (Eds.) Methodologies and Intelligent Systems for Technology Enhanced Learning, 10th International Conference, Advances in Intelligent Systems and Computing, 1241. Cham: Springer International Publishing, 95-105. DOI: 10.1007/978-3-030-52538-5_11


JYU-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Uribe, Pablo; Jiménez, Abelino; Araya, Roberto; Lämsä, Joni; Hämäläinen, Raija; Viiri, Jouni

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

Emojulkaisun toimittajat: Vittorini P, Di Mascio T, Tarantino L, Temperini M, Gennari R, Prieta F

Konferenssi:

International Conference in Methodologies and intelligent Systems for Techhnology Enhanced Learning

Konferenssin paikka ja aika: L'Aquila, Italy, 7.-9.10,2020

ISBN: 978-3-030-52537-8

eISBN: 978-3-030-52538-5

Lehti tai sarja: Advances in Intelligent Systems and Computing

ISSN: 2194-5357

eISSN: 2194-5365

Julkaisuvuosi: 2020

Sarjan numero: 1241

Artikkelin sivunumerot: 95-105

Kustantaja: Springer International Publishing

Kustannuspaikka: Cham

Julkaisumaa: Sveitsi

Julkaisun kieli: englanti

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

Avoin saatavuus: Julkaisukanava ei ole avoin

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/71837


Tiivistelmä

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.


YSO-asiasanat: tietokoneavusteinen oppiminen; luonnollinen kieli; sisällönanalyysi; tutkiva oppiminen; yhteisöllinen oppiminen; neuroverkot

Vapaat asiasanat: oppimisanalytiikka


Liittyvät organisaatiot


Hankkeet, joissa julkaisu on tehty

SA profilointi 2015-2018
Hämäläinen, Keijo
Suomen Akatemia
01.02.2015-31.08.2019


OKM-raportointi: Kyllä

Alustava JUFO-taso: 1


Viimeisin päivitys 2020-22-09 klo 10:34