Student Profile Modeling Using Boosting Algorithms

The student profile has become an important component of education systems. Many systems objectives, as e-recommendation, e-orientation, e-recruitment and dropout prediction are essentially based on the profile for decision support. Machine learning plays an important role in this context and severa...

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Published in:	International journal of web-based learning and teaching technologies Vol. 17; no. 5; pp. 1 - 13
Main Authors:	Hamim, Touria, Benabbou, Faouzia, Sael, Nawal
Format:	Journal Article
Language:	English
Published:	Hershey IGI Global 01.09.2022
Subjects:	Academic Achievement Academic Failure Addition Algorithms Artificial Intelligence Classification Clustering College Faculty Comparative analysis Comparative Education Comparative studies Datasets Dropouts Dropping out Educational activities Educational systems Educational Technology Elimination Feature selection Influence of Technology Machine learning Man Machine Systems Mathematics Mathematics Achievement Periodicals Prediction Profiles Recruitment Recursion Regression (Statistics) Student Characteristics Students Supervision World Problems
ISSN:	1548-1093, 1548-1107
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Abstract	The student profile has become an important component of education systems. Many systems objectives, as e-recommendation, e-orientation, e-recruitment and dropout prediction are essentially based on the profile for decision support. Machine learning plays an important role in this context and several studies have been carried out either for classification, prediction or clustering purpose. In this paper, the authors present a comparative study between different boosting algorithms which have been used successfully in many fields and for many purposes. In addition, the authors applied feature selection methods Fisher Score, Information Gain combined with Recursive Feature Elimination to enhance the preprocessing task and models’ performances. Using multi-label dataset predict the class of the student performance in mathematics, this article results show that the Light Gradient Boosting Machine (LightGBM) algorithm achieved the best performance when using Information gain with Recursive Feature Elimination method compared to the other boosting algorithms.
AbstractList	The student profile has become an important component of education systems. Many systems objectives, as e-recommendation, e-orientation, e-recruitment and dropout prediction are essentially based on the profile for decision support. Machine learning plays an important role in this context and several studies have been carried out either for classification, prediction or clustering purpose. In this paper, the authors present a comparative study between different boosting algorithms which have been used successfully in many fields and for many purposes. In addition, the authors applied feature selection methods Fisher Score, Information Gain combined with Recursive Feature Elimination to enhance the preprocessing task and models’ performances. Using multi-label dataset predict the class of the student performance in mathematics, this article results show that the Light Gradient Boosting Machine (LightGBM) algorithm achieved the best performance when using Information gain with Recursive Feature Elimination method compared to the other boosting algorithms.
Author	Sael, Nawal Hamim, Touria Benabbou, Faouzia
AuthorAffiliation	Faculty of Sciences Ben M'sick, University of Hassan II, Casablanca, Morocco
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SubjectTerms	Academic Achievement Academic Failure Addition Algorithms Artificial Intelligence Classification Clustering College Faculty Comparative analysis Comparative Education Comparative studies Datasets Dropouts Dropping out Educational activities Educational systems Educational Technology Elimination Feature selection Influence of Technology Machine learning Man Machine Systems Mathematics Mathematics Achievement Periodicals Prediction Profiles Recruitment Recursion Regression (Statistics) Student Characteristics Students Supervision World Problems
Title	Student Profile Modeling Using Boosting Algorithms
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