[논문]학업성취도 예측 요인 분석 및 인공지능 예측 모델 개발 - 블렌디드 수학 수업을 중심으로

안도연; 이광호

doi:10.7468/mathedu.2022.61.2.257

학업성취도 예측 요인 분석 및 인공지능 예측 모델 개발 - 블렌디드 수학 수업을 중심으로
Analysis of achievement predictive factors and predictive AI model development - Focused on blended math classes 원문보기

Journal of the Korean Society of Mathematical Education. Series A. The Mathematical Education, v.61 no.2, 2022년, pp.257 - 271

안도연 (천안청당초등학교) , 이광호 (한국교원대학교)

초록
AI-Helper

본 연구는 학습분석학을 기반으로 블렌디드 수학 수업에서 발생하는 학습 데이터를 활용하여 수학 학업성취도를 예측하는 요인이 무엇인지 탐색하고, 그 결과를 활용하여 수학 학업성취도를 예측하는 인공지능 모델을 개발하고자 하였다. 초등학교 5~6학년 학생 205명의 수학 학습 성향, LMS 데이터, 평가 결과를 수집하여 랜덤포레스트 모델을 분석하였다. 수학 학습성향에는 수학학습 자신감, 수학불안, 수학교과 흥미, 수학학습 자기관리, 수학학습 전략이 포함되었다. LMS 데이터로 e학습터의 진도율, 학습 횟수, 학습 시간을 수집하였다. 평가는 진단평가와 각 단원의 단원평가 결과를 사용하였다. 분석 결과 수학 학습성향 중 수학 학습 전략이 저성취 학생을 예측에 가장 중요한 요인으로 나타났다. LMS 학습 데이터는 예측에 미미한 영향을 주었다. 본 연구는 인공지능 모델이 블렌디드 수학 수업에서 발생하는 학습 데이터로 저성취 학생을 예측할 수 있음을 시사한다. 또한 분석 결과를 통해 교사가 학생을 평가하고 피드백하는 데 구체적인 정보를 제공하여 교사의 평가 활동에 보조적인 역할을 할 수 있을 것으로 기대한다.

Abstract ▼ AI-Helper

As information and communication technologies are being developed so rapidly, education research is actively conducted to provide optimal learning for each student using big data and artificial intelligence technology. In this study, using the mathematics learning data of elementary school 5th to 6th graders conducting blended mathematics classes, we tried to find out what factors predict mathematics academic achievement and developed an artificial intelligence model that predicts mathematics academic performance using the results. Math learning propensity, LMS data, and evaluation results of 205 elementary school students had analyzed with a random forest model. Confidence, anxiety, interest, self-management, and confidence in math learning strategy were included as mathematics learning disposition. The progress rate, number of learning times, and learning time of the e-learning site were collected as LMS data. For evaluation data, results of diagnostic test and unit test were used. As a result of the analysis it was found that the mathematics learning strategy was the most important factor in predicting low-achieving students among mathematics learning propensities. The LMS training data had a negligible effect on the prediction. This study suggests that an AI model can predict low-achieving students with learning data generated in a blended math class. In addition, it is expected that the results of the analysis will provide specific information for teachers to evaluate and give feedback to students.

주제어

표/그림 (12)

그림 Figure 1. Confusion matrix.
그림 Figure 2. MDI feature importance.
표 Table 1. MDI of 10 factors.
그림 Figure 3. PDP of unit test and pretest.
그림 Figure 4. PDP of math learning propensity
그림 Figure 5. PDP of LMS data.
그림 Figure 6. Confusion matrix before after corracting cut-off value.
표 Table 2. Performance of models depending on number of factors .
그림 Figure 7. Accuracy distribution of 100 random seeds.
그림 Figure 8. Precision distribution of 100 random seeds.
그림 Figure 9. Recall distribution of 100 random seeds.
표 Table 3. Performance of the predictive model.

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