Enhancing the prediction of student performance based on the machine learning XGBoost algorithm

Performance Factors Analysis (PFA) is considered one of the most important Knowledge Tracing (KT) approaches used for constructing adaptive educational hypermedia systems. It has shown a high prediction accuracy against many other KT approaches. While, the desire to estimate more accurately the stud...

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Bibliographic Details
Published in:	Interactive learning environments Vol. 31; no. 6; pp. 3360 - 3379
Main Authors:	Asselman, Amal, Khaldi, Mohamed, Aammou, Souhaib
Format:	Journal Article
Language:	English
Published:	Abingdon Routledge 18.08.2023 Taylor & Francis Ltd
Subjects:	Academic Achievement Accuracy Algorithms Artificial Intelligence Data Analysis ensemble learning Factor Analysis High School Students Hypermedia knowledge tracing Learning Processes Machine learning Mathematics Mathematics Instruction Middle School Students Performance Factors Performance factors analysis Performance prediction Physics Prediction prediction accuracy Student Behavior XGBoost
ISSN:	1049-4820, 1744-5191
Online Access:	Get full text
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