[논문]인공지능을 이용한 학습부진 특성 추출 및 예측 모델 연구

양자영; 문경희; 박성호

doi:10.6109/jkiice.2022.26.4.510

인공지능을 이용한 학습부진 특성 추출 및 예측 모델 연구
Extracting characteristics of underachievers learning using artificial intelligence and researching a prediction model 원문보기

한국정보통신학회논문지 = Journal of the Korea Institute of Information and Communication Engineering, v.26 no.4, 2022년, pp.510 - 518

양자영 (Office of General Education, Pusan National University) , 문경희 (Office of General Education, Pusan National University) , 박성호 (Office of Information Technology&Services, Pusan National University)

초록
AI-Helper

국가수준에서 시행되는 진단평가는 학교에서 학습부진이 있는 학생을 조기 발견하는 것이 매우 중요하다. 본연구는 부산교육종단의 2019년 중학교 1학년의 데이터를 입력하여 2020년 성취여부를 판별하는 인공지능 모델을 구축하고 분석하였다. 머신러닝 알고리즘으로 중학교 국어, 영어, 수학 기초학력을 예측하는 예측모형을 개발하고, 다음 학년 예측에도 78%, 82%, 83% 의 정확도를 보이는 것을 확인하였다. 또한, 중학교 과목별 성취예측 의사결정트리를 그려서 과정을 분석해보면서, 성취 예측에 영향을 미치는 특성들은 어떠한 것들이 있는지 살펴보았다.

Abstract ▼ AI-Helper

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.

주제어

표/그림 (12)

표 Table. 1 The Department of Education ｢National level academic achievement evaluation results｣
표 Table. 2 ｢Achievement evaluation system｣ standard achievement rate
표 Table. 3 Confusion matrix
표 Table. 4 The first year of middle school's achievement
표 Table. 5 Comparison of algorithm's predictive performance
표 Table. 6 XGBoost Model's prediction accuracy in Korean, math, and English
그림 Fig. 1 Korean Achievement Prediction Model Decision Tree.
그림 Fig. 2 Korean Feature Importance
그림 Fig. 3 Math Achievement Prediction Model Decision Tree.
그림 Fig. 5 English Achievement Prediction Model Decision Tree.
그림 Fig. 4 Math Feature Importance
그림 Fig. 6 English Feature Importance

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상세보기
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