Enhancing prediction of student success: Automated machine learning approach. (January 2021)

• Record Type: Journal Article
• Authors: Zeineddine, Hassan; Braendle, Udo; Farah, Assaad
• Is Part Of: Computers & electrical engineering. Volume 89(2021)
• Publication Date: 2021-01
• Subjects: Automated machine learning -- Prediction accuracy -- Student performance -- Pre-admission data -- Ensemble model -- Higher education; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106903
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Academic institutions need to be more efficient in supporting students at risk. Automated machine learning helps in identifying optimal prediction models. Ensemble models with balanced data enhances prediction using pre-admission data. Abstract: Students' success has recently become a primary strategic objective for most institutions of higher education. With budget cuts and increasing operational costs, academic institutions are paying more attention to sustaining students' enrollment in their programs without compromising rigor and quality of education. With the scientific advancements in Big Data Analytics and Machine Learning, universities are increasingly relying on data to predict students' performance. Many initiatives and research projects addressed the use of students' behavioral and academic data to classify students and predict their future performance using advanced statistics and Machine Learning. To allow for early intervention, this paper proposes the use of Automated Machine Learning to enhance the accuracy of predicting student performance using data available prior to the start of the academic program. Graphical abstract: Image, graphical abstract