Tuesday, October 16
4:15 PM-5:30 PM
PDT
Jubilee Ballroom 3

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

Poster Demonstration ID: 53582
  1. aaa
    Hiroo Hirose
    Suwa University of Science
  2. Takeshi Ozaki
    Suwa University of Science
  3. Kurumi Kawate
    Suwa University of Science
  4. Yoshito Yamamoto
    Tokyo University of Science
  5. Hiroshi Ichikawa
    Otsuma Women's University

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.

No presider for this session.

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