Improved student dropout prediction in Thai University using ensemble of mixed-type data clusterings

• Published in: International journal of machine learning and cybernetics Vol. 8; no. 2; pp. 497 - 510
• Main Authors: Iam-On, Natthakan, Boongoen, Tossapon
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2017; Springer Nature B.V
• Subjects: Academic achievement; Algorithms; Artificial Intelligence; Classification; Clustering; Colleges & universities; Complex Systems; Computational Intelligence; Control; Data collection; Data mining; Decision trees; Engineering; Feature selection; Learning; Mechatronics; Original Article; Pattern Recognition; Principal components analysis; Robotics; Student retention; Students; Systems Biology; Variables; Student dropout; Educational data mining; Feature transformation; Classification; Ensemble clustering

Increasing student retention has been a common goal of many academic institutions, especially in the university level. The negative effects of student attrition are evident to students, parents, university and the society as a whole. The first-year students are at the greatest risk of dropping out o...