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

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Automatic Classification for Cognitive Engagement in Online Discussion Forums: Text Mining and Machine Learning Approach
Creator	Bennani, Samir Idrissi, Mohammed
source	PMC
abstract	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.
has issue date	2020-06-10(xsd:dateTime)
bibo:doi	10.1007/978-3-030-52240-7_21
has license	no-cc
sha1sum (hex)	b4819f94f8faa031d096b980179d6f2956af8cf9
schema:url	https://doi.org/10.1007/978-3-030-52240-7_21
resource representing a document's title	Automatic Classification for Cognitive Engagement in Online Discussion Forums: Text Mining and Machine Learning Approach
has PubMed Central identifier	PMC7334702
schema:publication	Artificial Intelligence in Education
resource representing a document's body	covid:b4819f94f8faa031d096b980179d6f2956af8cf9#body_text
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