Competency Development System for the Pre-service Teachers Using a Machine Learning Model

Abstract

Teaching professional experience training is a one-semester training period in a school where students are assigned to teach their major subject for at least four lessons per week and perform other teaching duties as assigned by the school. The current system requires supervisors to supervise teaching and monitor student behavior three times per semester, both online and onsite. However, this inconsistent monitoring process can lead to gaps in student support. Since university supervisors and associate teachers often do not have enough information to help or advise students immediately, students are at risk of having lower academic performance and not being able to receive a teaching license. Therefore, this study aims to investigate the need for a competency development system for pre-service teachers using a machine learning model. To obtain the necessary information to develop the system, A survey was conducted to analyze the opinions of university supervisors, associate teachers, and school administrators in the network schools.