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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of web-based learning and teaching technologies Jg. 17; H. 5; S. 1 - 13
Hauptverfasser: Hamim, Touria, Benabbou, Faouzia, Sael, Nawal
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hershey IGI Global 01.09.2022
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1548-1093
1548-1107
DOI:10.4018/IJWLTT.20220901.oa4