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http://dx.doi.org/10.6109/jkiice.2019.23.8.917

Deep learning based teacher candidate acceptance prediction using college credits and activities  

Kim, Geun-Ho (Department of Computer Education, Kongju National University)
Kim, Eui-Jeong (Department of Computer Education, Kongju National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.23, no.8, 2019 , pp. 917-922 More about this Journal
Abstract
The recent increase in preference for teacher jobs has led to a rise in preference for education colleges. Not all students can enter teachers, but they must pass the test called the competitive examination for teacher appointment candidates after graduation. However, due to the declining population, the and employment T.O.s are decreasing every year and the competition rate is rising steeply. Therefore, in order to concentrate on the recruitment exam upon entering the university, the university is becoming a huge academy for the exam, not a place to study and learn. We found a connection between students' overall school life and their use of study groups as well as their grades and whether they passed the competition test for teachers using deep running. The academic activities did not significantly affect the acceptance process, and the accuracy of the prediction of the acceptance rate was generally 70% accurate.
Keywords
Appointment test; Deep Learning; Prediction System; Artificial intelligence;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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