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Automatic Classification for Cognitive Engagement in Online Discussion Forums: Text Mining and Machine Learning Approach

For effective learning, students must set learning objectives and adopt the ad hoc cognitive behavior to achieve them. Our research work aims to ensure good scaffolding by offering tutors the opportunity to observe learners’ cognitive behaviors, especially their cognitive engagement. In this respect...

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Detalles Bibliográficos
Autores principales: Hayati, Hind, Khalidi Idrissi, Mohammed, Bennani, Samir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334702/
http://dx.doi.org/10.1007/978-3-030-52240-7_21
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author Hayati, Hind
Khalidi Idrissi, Mohammed
Bennani, Samir
author_facet Hayati, Hind
Khalidi Idrissi, Mohammed
Bennani, Samir
author_sort Hayati, Hind
collection PubMed
description For effective learning, students must set learning objectives and adopt the ad hoc cognitive behavior to achieve them. Our research work aims to ensure good scaffolding by offering tutors the opportunity to observe learners’ cognitive behaviors, especially their cognitive engagement. In this respect, we propose in the present work an automatic system for classifying learners according to their levels of cognitive engagement. To this end, we focus on the analysis of social interactions within online discussion forums. Hence, the proposed system has two main steps: 1/Learners’ vector construction and 2/SVM-based classifier. The results show the efficiency of the proposed system with an accuracy = 0.9 and a cohen’s K = 0.89.
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spelling pubmed-73347022020-07-06 Automatic Classification for Cognitive Engagement in Online Discussion Forums: Text Mining and Machine Learning Approach Hayati, Hind Khalidi Idrissi, Mohammed Bennani, Samir Artificial Intelligence in Education Article For effective learning, students must set learning objectives and adopt the ad hoc cognitive behavior to achieve them. Our research work aims to ensure good scaffolding by offering tutors the opportunity to observe learners’ cognitive behaviors, especially their cognitive engagement. In this respect, we propose in the present work an automatic system for classifying learners according to their levels of cognitive engagement. To this end, we focus on the analysis of social interactions within online discussion forums. Hence, the proposed system has two main steps: 1/Learners’ vector construction and 2/SVM-based classifier. The results show the efficiency of the proposed system with an accuracy = 0.9 and a cohen’s K = 0.89. 2020-06-10 /pmc/articles/PMC7334702/ http://dx.doi.org/10.1007/978-3-030-52240-7_21 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Hayati, Hind
Khalidi Idrissi, Mohammed
Bennani, Samir
Automatic Classification for Cognitive Engagement in Online Discussion Forums: Text Mining and Machine Learning Approach
title Automatic Classification for Cognitive Engagement in Online Discussion Forums: Text Mining and Machine Learning Approach
title_full Automatic Classification for Cognitive Engagement in Online Discussion Forums: Text Mining and Machine Learning Approach
title_fullStr Automatic Classification for Cognitive Engagement in Online Discussion Forums: Text Mining and Machine Learning Approach
title_full_unstemmed Automatic Classification for Cognitive Engagement in Online Discussion Forums: Text Mining and Machine Learning Approach
title_short Automatic Classification for Cognitive Engagement in Online Discussion Forums: Text Mining and Machine Learning Approach
title_sort automatic classification for cognitive engagement in online discussion forums: text mining and machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334702/
http://dx.doi.org/10.1007/978-3-030-52240-7_21
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