Classification and prediction of student performance data using various machine learning algorithms. (2023)

• Record Type: Journal Article
• Authors: Pallathadka, Harikumar; Wenda, Alex; Ramirez-Asís, Edwin; Asís-López, Maximiliano; Flores-Albornoz, Judith; Phasinam, Khongdet
• Is Part Of: Materials today. Volume 80:Part 3(2023)
• Page Start: 3782
• Page End: 3785
• Publication Date: 2023
• Subjects: Educational Data Mining -- Machine Learning -- Student Performance -- Classification -- Prediction; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.07.382
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: In today's competitive world, it is critical for an institute to forecast student performance, classify individuals based on their talents, and attempt to enhance their performance in future tests. Students should be advised well in advance to concentrate their efforts in a specific area in order to improve their academic achievement. This type of analysis assists an institute in lowering its failure rates. Based on their prior performance in comparable courses, this study predicts students' performance in a course. Data mining is a collection of techniques used to uncover hidden patterns in massive amounts of existing data. These patterns may be valuable for analysis and prediction. Education data mining refers to the collection of data mining applications in the field of education. These applications are concerned with the analysis of data from students and teachers. The analysis might be used for categorization or prediction. Machine learning such as Nave Bayes, ID3, C4.5, and SVM are investigated. UCI machinery student performance data set is used in experimental study. Algorithms are analysed on certain parameters like- accuracy, error rate.