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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Interactive learning environments Ročník 31; číslo 6; s. 3360 - 3379
Hlavní autoři: Asselman, Amal, Khaldi, Mohamed, Aammou, Souhaib
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Routledge 18.08.2023
Taylor & Francis Ltd
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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 student level leads researchers to enhance PFA by inventing several advanced extensions. However, most of the proposed extensions have exclusively been improved in a pedagogical sense, as the improvements have mostly been limited to the analysis of students' behaviour during their learning process. In contrast, Machine Learning provides many powerful methods that could be efficient to enhance, in the technical sense, the prediction of student performance. Our goal is to focus on the exploitation of Ensemble Learning methods as an extremely effective Machine Learning paradigm used to create many advanced solutions in several fields. In this sense, we propose a new PFA approach based on different models (Random Forest, AdaBoost, and XGBoost) in order to increase the predictive accuracy of student performance. Our models have been evaluated on three different datasets. The experimental results show that the scalable XGBoost has outperformed the other evaluated models and substantially improved the performance prediction compared to the original PFA algorithm.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1049-4820
1744-5191
DOI:10.1080/10494820.2021.1928235