Construction of a Prediction Model of the Shortest Annual Graduation by Machine Learning Using Learning Environment Data

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Authors

Hiroo Hirose, Takeshi Ozaki, Kurumi Kawate, Suwa University of Science, Japan ; Yoshito Yamamoto, Tokyo University of Science, Japan ; Hiroshi Ichikawa, Otsuma Women's University, Japan

E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, Oct 15, 2018 in Las Vegas, NV, United States ISBN 978-1-939797-35-3

• Conference Info
• Proceedings Book

Abstract

This paper study to predict whether students can graduate in four years at an early stage using machine learning. Four data sets are made from students' data containing Academic results and data before enrollment. Using these data sets, classification analysis by three machine learning algorithm and discrimination analysis were compared in Recall rate. It will be showed that it is useful to use data set containing academic results in the end of sophomore year and data before enrollment.

Citation

Hirose, H., Ozaki, T., Kawate, K., Yamamoto, Y. & Ichikawa, H. (2018). Construction of a Prediction Model of the Shortest Annual Graduation by Machine Learning Using Learning Environment Data. In Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 510-515). Las Vegas, NV, United States: Association for the Advancement of Computing in Education (AACE). Retrieved August 9, 2024 from https://www.learntechlib.org/p/185001.

© 2018 AACE