ERIC Number: EJ1344178
Record Type: Journal
Publication Date: 2022
Pages: 13
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1548-1093
EISSN: EISSN-1548-1107
Student Profile Modeling Using Boosting Algorithms
Hamim, Touria; Benabbou, Faouzia; Sael, Nawal
International Journal of Web-Based Learning and Teaching Technologies, v17 n5 Article 4 2022
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.
Descriptors: Mathematics, Artificial Intelligence, Man Machine Systems, Student Characteristics, Profiles, Mathematics Achievement, Prediction
IGI Global. 701 East Chocolate Avenue, Hershey, PA 17033. Tel: 866-342-6657; Tel: 717-533-8845; Fax: 717-533-8661; Fax: 717-533-7115; e-mail: journals@igi-global.com; Web site: https://www.igi-global.com/journals/
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A