[논문]머신러닝 추천모듈이 적용된 맞춤형 학습 플랫폼 효과성 탐색: 학습시간, 자기주도적 학습능력, 수학에 대한 태도, 수학학업성취도를 중심으로

박만구; 임현정; 김지영; 이규하; 김미경

doi:10.7468/mathedu.2020.59.4.373

머신러닝 추천모듈이 적용된 맞춤형 학습 플랫폼 효과성 탐색: 학습시간, 자기주도적 학습능력, 수학에 대한 태도, 수학학업성취도를 중심으로
The effects on the personalized learning platform with machine learning recommendation modules: Focused on learning time, self-directed learning ability, attitudes toward mathematics, and mathematics achievement 원문보기

Journal of the Korean Society of Mathematical Education. Series A. The Mathematical Education, v.59 no.4, 2020년, pp.373 - 387

박만구 (서울교육대학교) , 임현정 (안산청석초등학교) , 김지영 (서울북가좌초등학교) , 이규하 (위두커뮤니케이션즈) , 김미경 (위두커뮤니케이션즈)

초록
AI-Helper

본 연구의 목적은 학습 빅데이터 분석을 통해 추천 알고리즘을 스스로 고도화하는 머신러닝 추천모듈이 적용된 개인 맞춤형 학습 플랫폼이 학생들의 학습시간, 자기주도적 학습능력, 수학에 대한 태도, 수학학업성취도에 미치는 영향과 이들 사이의 구조적 관계를 검증하는 것이다. 연구 결과 개인 맞춤형 학습은 학생들의 학습시간, 자기주도적 학습능력, 수학에 대한 태도, 수학학업성취도에 대해 긍정적인 영향을 미치고 있었다. 또한, 맞춤형 학습과 수학에 대한 태도와 수학학업성취도의 관계에서 학습시간과 자기주도적 학습능력의 매개효과가 유의하였다.

Abstract ▼ AI-Helper

The purpose of this study is to verify the effects of personalized learning platforms applied with machine learning recommendation modules that upgrade recommended algorithms by themselves through learning big data analysis on students' learning time, self-directed learning ability, mathematics achievement, and attitudes toward mathematics, and the correlation between them. According to the study, customized learning affected learning time, self-directed learning ability and mathematics attitude, while learning time affected self-directed learning ability. Self-directed learning ability has had a significant impact on the attitude of mathematics and mathematical achievements. As a result of the mediated effectiveness test, the indirect impact of customized learning on mathematics attitude and mathematics performance was significant through the medium of learning time and self-directed learning ability.

주제어

표/그림 (10)

표 [Table 1] Analytical variable description
표 [Table 2] The results of descriptive statistics
그림 [Fig. 1] Research model
그림 [Fig. 2] The concept of learning recommendation algorithm
그림 [[Fig. 3] Data storage interoperability standards Resource: ISO/IEC TS 20748-3 LAI - Part 3: Guideline for data interoperability
표 [Table 3] The construct reliability and validity
표 [Table 4] The goodness of fit for measurement models
표 [Table 5] The results of path coefficients
그림 [Fig. 4] The path coefficient analysis
표 [Table 6] Bootstrapping results of mediation effect

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