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DOI: 10.14569/IJACSA.2020.0110728
PDF

Emotional Impact for Predicting Student Performance in Intelligent Tutoring Systems (ITS)

Author 1: Kouame Abel Assielou
Author 2: Cissé Théodore Haba
Author 3: Bi Tra Gooré
Author 4: Tanon Lambert Kadjo
Author 5: Kouakou Daniel Yao

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 7, 2020.

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Abstract: Current Intelligent Tutoring Systems (ITS) provide better recommendations for students to improve their learning. These recommendations mainly involve students’ performance prediction, which remains problematic for ITS, despite the significant improvements made by prediction methods such as Matrix Factorization (MF). The present contribution therefore aims to provide a solution to this prediction problem by proposing an approach that combines Multiple Linear Regression (Modelling Emotional Impact) and a Weighted Multi-Relational Matrix Factorization model to take advantage of both student cognitive and emotional faculties. This approach takes into account not only the relationships that exist between students, tasks and skills, but also students’ emotions. Experimental results on a set of pedagogical data collected from 250 students show that our approach significantly improves the results of Student Performance Prediction.

Keywords: Intelligent tutoring system; student performance prediction; matrix factorization; emotional impact; achievement emotions

Kouame Abel Assielou, Cissé Théodore Haba, Bi Tra Gooré, Tanon Lambert Kadjo and Kouakou Daniel Yao, “Emotional Impact for Predicting Student Performance in Intelligent Tutoring Systems (ITS)” International Journal of Advanced Computer Science and Applications(IJACSA), 11(7), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110728

@article{Assielou2020,
title = {Emotional Impact for Predicting Student Performance in Intelligent Tutoring Systems (ITS)},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110728},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110728},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {7},
author = {Kouame Abel Assielou and Cissé Théodore Haba and Bi Tra Gooré and Tanon Lambert Kadjo and Kouakou Daniel Yao}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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