From “tactical discussion” in collaboration game to “behaviors”: A classification approach in stages
The analysis of group dynamics is extremely useful for understanding and predicting the performance of teamwork’s, since in this context, collaboration problems can naturally arise. Artificial intelligence, and specially machine learning techniques, enables automating the observation process and the...
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2018
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_11373601_v21_n61_p82_Berdun http://hdl.handle.net/20.500.12110/paper_11373601_v21_n61_p82_Berdun |
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paper:paper_11373601_v21_n61_p82_Berdun |
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paper:paper_11373601_v21_n61_p82_Berdun2023-06-08T16:09:12Z From “tactical discussion” in collaboration game to “behaviors”: A classification approach in stages Automatic classification Gamification Group dynamic User modeling Artificial intelligence Teaching Automatic classification Classification approach Collaboration problems Collaborative behavior Gamification Group dynamics Machine learning techniques User Modeling Learning systems The analysis of group dynamics is extremely useful for understanding and predicting the performance of teamwork’s, since in this context, collaboration problems can naturally arise. Artificial intelligence, and specially machine learning techniques, enables automating the observation process and the analysis of groups of users who use an online collaborative platform. Among the online collaborative platforms available, games are an attractive alternative for all audiences that enable capturing the players’ behavior by observing their social interactions, while engaging them in a pleasant activity. In this paper, we present experimental results of classifying observed conversations in an online game to collaborative behaviors, guided by the Interaction Process Analysis, a theory for categorizing social interactions. The proposed automation of the classification process can be used to assist teachers or team leaders to detect alterations in the balance of group reactions and to improve their performance by indicating actions to improve the balance. © 2018, Asociacion Espanola de Inteligencia Artificial. All rights reserved. 2018 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_11373601_v21_n61_p82_Berdun http://hdl.handle.net/20.500.12110/paper_11373601_v21_n61_p82_Berdun |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Automatic classification Gamification Group dynamic User modeling Artificial intelligence Teaching Automatic classification Classification approach Collaboration problems Collaborative behavior Gamification Group dynamics Machine learning techniques User Modeling Learning systems |
| spellingShingle |
Automatic classification Gamification Group dynamic User modeling Artificial intelligence Teaching Automatic classification Classification approach Collaboration problems Collaborative behavior Gamification Group dynamics Machine learning techniques User Modeling Learning systems From “tactical discussion” in collaboration game to “behaviors”: A classification approach in stages |
| topic_facet |
Automatic classification Gamification Group dynamic User modeling Artificial intelligence Teaching Automatic classification Classification approach Collaboration problems Collaborative behavior Gamification Group dynamics Machine learning techniques User Modeling Learning systems |
| description |
The analysis of group dynamics is extremely useful for understanding and predicting the performance of teamwork’s, since in this context, collaboration problems can naturally arise. Artificial intelligence, and specially machine learning techniques, enables automating the observation process and the analysis of groups of users who use an online collaborative platform. Among the online collaborative platforms available, games are an attractive alternative for all audiences that enable capturing the players’ behavior by observing their social interactions, while engaging them in a pleasant activity. In this paper, we present experimental results of classifying observed conversations in an online game to collaborative behaviors, guided by the Interaction Process Analysis, a theory for categorizing social interactions. The proposed automation of the classification process can be used to assist teachers or team leaders to detect alterations in the balance of group reactions and to improve their performance by indicating actions to improve the balance. © 2018, Asociacion Espanola de Inteligencia Artificial. All rights reserved. |
| title |
From “tactical discussion” in collaboration game to “behaviors”: A classification approach in stages |
| title_short |
From “tactical discussion” in collaboration game to “behaviors”: A classification approach in stages |
| title_full |
From “tactical discussion” in collaboration game to “behaviors”: A classification approach in stages |
| title_fullStr |
From “tactical discussion” in collaboration game to “behaviors”: A classification approach in stages |
| title_full_unstemmed |
From “tactical discussion” in collaboration game to “behaviors”: A classification approach in stages |
| title_sort |
from “tactical discussion” in collaboration game to “behaviors”: a classification approach in stages |
| publishDate |
2018 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_11373601_v21_n61_p82_Berdun http://hdl.handle.net/20.500.12110/paper_11373601_v21_n61_p82_Berdun |
| _version_ |
1768543097813204992 |