Interesting Attributes of Student Performance Using Machine Learning Models Based on Family and Educational Backgrounds in the Faculty of Agriculture and Technology at Rajamangala University of Technology Isan

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Sakchan Luangmaneerote
Anyawee Chaiwachiragompol


This research studied used six machine learning models to predict student performance and studied interesting attributes that cause influence students to achieve their degree based on their family background and background studies.  The student’s data was obtained from Education service system (ESS) of University using data that was collected the last 5 years, and used five machine learning models to predict the student performance. Results had been clearly showed that the data related to student’s family collected in ESS is was not sufficient for accurate prediction, while student grades, course learning and their study location during high school is was a distinguishing feature caused influencing students to achieve their degrees.  Additionally, the best accuracy result was Random forest which can predicted an accuracy of 0.62, while the other models did not show satisfactory results. The results of this research may enable the faculty to develop an improved database to predict student performance and promote faculty resource management.

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