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A New Approach to Modelling Students' Socio-Emotional Attributes to Predict Their Performance in Intelligent Tutoring Systems

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Title: A New Approach to Modelling Students' Socio-Emotional Attributes to Predict Their Performance in Intelligent Tutoring Systems
Language: English
Authors: Assielou, Kouamé Abel (ORCID 0000-0002-1342-3083), Haba, Cissé Théodore (ORCID 0000-0002-4018-7194), Kadjo, Tanon Lambert (ORCID 0000-0002-4776-3019), Goore, Bi Tra (ORCID 0000-0002-7045-6041), Yao, Kouakou Daniel (ORCID 0000-0002-4144-9344)
Source: Journal of Education and e-Learning Research. 2021 8(3):340-348.
Availability: Asian Online Journal Publishing Group. 244 Fifth Avenue Suite D42, New York, NY 10001. Fax: 212-591-6094; e-mail: info@asianonlinejournals.com; Web site: http://www.asianonlinejournals.com
Peer Reviewed: Y
Page Count: 9
Publication Date: 2021
Document Type: Journal Articles
Reports - Research
Education Level: Secondary Education
Descriptors: Psychological Patterns, Predictor Variables, Intelligent Tutoring Systems, Secondary School Students, Social Influences, Grade Prediction, Item Sampling
ISSN: 2518-0169
Abstract: Intelligent Tutoring Systems (ITS) are computer-based learning environments that aim to imitate to the greatest possible extent the behavior of a human tutor in their capacity as a pedagogical and subject expert. One of the major challenges of these systems is to know how to adapt the training both to changing requirements of all kinds and to student knowledge and reactions. The activities recommended by these systems mainly involve active student performance prediction that, nowadays, becomes problematic in the face of the expectations of the present world. In the associated literature, several approaches, using various attributes, have been proposed to solve the problem of performance prediction. However, these approaches have failed to take advantage of the synergistic effect of students' social and emotional factors as better prediction attributes. This paper proposes an approach to predict student performance called "SoEmo"-WMRMF that exploits not only cognitive abilities, but also group work relationships between students and the impact of their emotions. More precisely, this approach models five types of domain relations through a Weighted Multi-Relational Matrix Factorization (WMRMF) model. An evaluation carried out on a data sample extracted from a survey carried out in a general secondary school showed that the proposed approach gives better performance in terms of reduction of the Root Mean Squared Error (RMSE) compared to other models simulated in this paper.
Abstractor: As Provided
Entry Date: 2021
Accession Number: EJ1314000
Database: ERIC
FullText Text:
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  Data: A New Approach to Modelling Students' Socio-Emotional Attributes to Predict Their Performance in Intelligent Tutoring Systems
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  Data: <searchLink fieldCode="AR" term="%22Assielou%2C+Kouamé+Abel%22">Assielou, Kouamé Abel</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1342-3083">0000-0002-1342-3083</externalLink>)<br /><searchLink fieldCode="AR" term="%22Haba%2C+Cissé+Théodore%22">Haba, Cissé Théodore</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4018-7194">0000-0002-4018-7194</externalLink>)<br /><searchLink fieldCode="AR" term="%22Kadjo%2C+Tanon+Lambert%22">Kadjo, Tanon Lambert</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4776-3019">0000-0002-4776-3019</externalLink>)<br /><searchLink fieldCode="AR" term="%22Goore%2C+Bi+Tra%22">Goore, Bi Tra</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7045-6041">0000-0002-7045-6041</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yao%2C+Kouakou+Daniel%22">Yao, Kouakou Daniel</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4144-9344">0000-0002-4144-9344</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Education+and+e-Learning+Research%22"><i>Journal of Education and e-Learning Research</i></searchLink>. 2021 8(3):340-348.
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  Data: Intelligent Tutoring Systems (ITS) are computer-based learning environments that aim to imitate to the greatest possible extent the behavior of a human tutor in their capacity as a pedagogical and subject expert. One of the major challenges of these systems is to know how to adapt the training both to changing requirements of all kinds and to student knowledge and reactions. The activities recommended by these systems mainly involve active student performance prediction that, nowadays, becomes problematic in the face of the expectations of the present world. In the associated literature, several approaches, using various attributes, have been proposed to solve the problem of performance prediction. However, these approaches have failed to take advantage of the synergistic effect of students' social and emotional factors as better prediction attributes. This paper proposes an approach to predict student performance called "SoEmo"-WMRMF that exploits not only cognitive abilities, but also group work relationships between students and the impact of their emotions. More precisely, this approach models five types of domain relations through a Weighted Multi-Relational Matrix Factorization (WMRMF) model. An evaluation carried out on a data sample extracted from a survey carried out in a general secondary school showed that the proposed approach gives better performance in terms of reduction of the Root Mean Squared Error (RMSE) compared to other models simulated in this paper.
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      – Text: English
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        StartPage: 340
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      – SubjectFull: Psychological Patterns
        Type: general
      – SubjectFull: Predictor Variables
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      – SubjectFull: Intelligent Tutoring Systems
        Type: general
      – SubjectFull: Secondary School Students
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      – SubjectFull: Social Influences
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      – SubjectFull: Grade Prediction
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      – SubjectFull: Item Sampling
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