In the recent years, data mining has been utilized in education settings for extracting and manipulating data, and for establishing patterns in order to produce useful information for decision making. There is a growing need for higher education institutions to be more informed and knowledgeable about their students, and for them to understand some of the reasons behind students’ choice to enroll and pursue careers. One of the ways in which this can be done is for such institutions to obtain information and knowledge about their students by mining, processing and analyzing the data they accumulate about them. In this paper, we propose a general framework for mining student data enrolled in Science, Technology, Engineering and Mathematics (STEM) using performance weighted ensemble classifiers. We train an ensemble of classification models from enrollment data streams to improve the quality of student data by eliminating noisy instances, and hence improving predictive accuracy. We empirically compare our technique with single model based techniques and show that using ensemble models not only gives better predictive accuracies on student enrollment in STEM, but also provides better rules for understanding the factors that influence student enrollment in STEM disciplines.
Title = "Improving Student Enrollment Prediction Using Ensemble Classifiers",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "7",
Pages ="109 - 157",
Year = "2018",
Authors ="Stephen Kahara Wanjau , Geoffrey Muchiri Muketha"}
A framework for mining student data using performance weighted ensemble classifiers is proposed
There are significant attributes that highly affects the students choice to enroll in STEM courses
CRISP-DM process model is adapted and used as a guiding framework for modeling
Using ensemble models gives better predictive accuracies on student enrollment in STEM Courses.