Educational data mining to predict students' academic performance: A survey study
Educational data mining is an emerging interdisciplinary research area involving both
education and informatics. It has become an imperative research area due to many …
education and informatics. It has become an imperative research area due to many …
Data-driven artificial intelligence in education: A comprehensive review
As education constitutes an essential development standard for individuals and societies,
researchers have been exploring the use of artificial intelligence (AI) in this domain and …
researchers have been exploring the use of artificial intelligence (AI) in this domain and …
University dropout prediction through educational data mining techniques: A systematic review
The dropout rates in the European countries is one of the major issues to be faced in a near
future as stated in the Europe 2020 strategy. In 2017, an average of 10.6% of young people …
future as stated in the Europe 2020 strategy. In 2017, an average of 10.6% of young people …
Early-predicting dropout of university students: an application of innovative multilevel machine learning and statistical techniques
This paper combines a theoretical-based model with a data-driven approach to develop an
Early Warning System that detects students who are more likely to dropout. The model uses …
Early Warning System that detects students who are more likely to dropout. The model uses …
Towards a students' dropout prediction model in higher education institutions using machine learning algorithms
Using machine learning to predict students' dropout in higher education institutions and
programs has proven to be effective in many use cases. In an approach based on machine …
programs has proven to be effective in many use cases. In an approach based on machine …
[HTML][HTML] Predicting student dropout rates using supervised machine learning: Insights from the 2022 National Education Accessibility Survey in Somaliland
MA Hassan, AH Muse, S Nadarajah - Applied Sciences, 2024 - mdpi.com
High student dropout rates are a critical issue in Somaliland, significantly impeding
educational progress and socioeconomic development. This study leveraged data from the …
educational progress and socioeconomic development. This study leveraged data from the …
Development of a web-based prediction system for students' academic performance
D Alboaneen, M Almelihi, R Alsubaie, R Alghamdi… - Data, 2022 - mdpi.com
Educational Data Mining (EDM) is used to extract and discover interesting patterns from
educational institution datasets using Machine Learning (ML) algorithms. There is much …
educational institution datasets using Machine Learning (ML) algorithms. There is much …
Designing, developing, and validating a measure of undergraduate students' conceptions of artificial intelligence in education
The purpose of this research study is to design, develop, and validate an instrument for
measuring undergraduate students' conceptions of artificial intelligence in education …
measuring undergraduate students' conceptions of artificial intelligence in education …
School dropout prediction and feature importance exploration in Malawi using household panel data: machine learning approach
Designing early warning systems through machine learning (ML) models to identify students
at risk of dropout can improve targeting mechanisms and lead to efficient social policy …
at risk of dropout can improve targeting mechanisms and lead to efficient social policy …
Comparison of predictive models with balanced classes using the SMOTE method for the forecast of student dropout in higher education
Based on the premise that university student dropout is a social problem in the university
ecosystem of any country, technological leverage is a way that allows us to build …
ecosystem of any country, technological leverage is a way that allows us to build …