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http://dx.doi.org/10.6109/jkiice.2022.26.4.510

Extracting characteristics of underachievers learning using artificial intelligence and researching a prediction model  

Yang, Ja-Young (Office of General Education, Pusan National University)
Moon, Kyong-Hi (Office of General Education, Pusan National University)
Park, Seong-Ho (Office of Information Technology&Services, Pusan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.4, 2022 , pp. 510-518 More about this Journal
Abstract
The diagnostic evaluation conducted at the national level is very important to detect underachievers in school early. This study used an artificial intelligence method to find the characteristics of underachievers that affect learning development for middle school students. In this study an artificial intelligence model was constructed and analyzed to determine whether the Busan Education Longitudinal Data in 2020 by entering data from the first year of middle school in 2019. A predictive model was developed to predict basic middle school Korean, English, and mathematics education with machine learning algorithms, and it was confirmed that the accuracy was 78%, 82%, and 83%, respectively, in the prediction for the next school year. In addition, by drawing an achievement prediction decision tree for each middle school subject we are analyzing the process of prediction. Finally, we examined what characteristics affect achievement prediction.
Keywords
Basic education; Predictive models; artificial intelligence; XGBoost;
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