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NTIS 바로가기Journal of the Korean Society of Mathematical Education. Series E: Communications of Mathematical Education, v.34 no.3, 2020년, pp.215 - 234
신동조 (고려대학교)
With the advent of the AI, the need to use AI in the field of education is widely recognized. The purpose of this study is to shed light on how prospective mathematics teachers perceive the need for AI and the role of teachers in future mathematics education. As a result, with regard to teaching, pr...
핵심어 | 질문 | 논문에서 추출한 답변 |
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AI의 정의는 무엇인가? | AI라는 용어는 1956년 Dartmouth 대학에서 주관한 워크숍에서 John McCarthy에 의해 처음 사용된 것으로 알려져 있다. Dartmouth 워크숍을 위해 제출된 제안서에 따르면, AI는 “인간이 하는 것과 같이 지능적이라고 불릴 수 있는 방식으로 행동하는 기계를 만드는 것”으로 정의되었다(McCarthy, Minsky, Rochester, & Shannon, 1955, p. 11). | |
수학이 타교과에 비해 AI 기술을 활발히 적용되는 이유는 무엇인가? | 최근 교육 분야에서도 AI라는 용어가 종종 입에 오르내리고 있다. 특히, 수학은 타 교과에 비해 비교적 잘 구조화되어 있고 명확한 답을 가진다는 점에서 AI 기술이 가장 활발히 적용되고 있는 교과 중 하나이다(Holmes et al., 2019). | |
수학교육에서 AI 활용을 크게 두 가지 측면으로 분류하면 어떻게 되는가? | 수학교육에서 AI 활용은 크게 두 가지 측면으로 분류할 수 있다. 첫째, 선행연구에서는 학생의 수학학습 과정을 분석하기 위해 기계학습(machine learning)과 데이터 마이닝(data mining)과 같은 AI 알고리즘을 사용하였다. AI는 학습 분석학(learning analytics) 측면에서 주로 교육용 빅데이터를 분석하여 학생의 인지적·정의적 영역에 미치는 변인 식별과 예측 그리고 학생의 학습 행동과 패턴을 모델링하기 위해 사용되는 경향이 있다(Shin & Shim, 2020). 예를 들어, Aksoy, Narli, & Idil(2016)은 데이터 마이닝 기술을 사용하여 중학교 학생의 성별, 학년, 유치원 경험유무, 부모의 교육수준, 수학학습 선호도에 관한 변인으로부터 수학적 태도에 영향을 미치는 변인을 식별하고 이를 예측하기 위한 다양한 규칙(예를 들어, 성별=여자, 학년=8학년, 모 교육수준=초졸, 부 교육수준=고졸 → 학생의 수학적 태도=부정적)을 찾아냈다. Gabriel et al.(2018)은 기계학습을 사용하여 PISA1) 자료(오스트리아)로 부터 수학적 소양2)에 미치는 인구통계학적 변인과 심리학적 변인을 분석한 결과 수학 자아효능감이 학생의 수학적 소양에 가장 유의미한 변인이라는 것을 보고하였다. Masci et al.(2018) 역시 PISA 자료를 기계학습을 통해 분석하여 9개국3) 학생의 수학 성취도에 영향을 미치는 국가별 유의미한 학생 변인과 학교 변인을 탐색하였다. Martin et al.(2015)은 반복적 등분할(equipartitioning) 조작으로 특정 분수를 만드는 온라인 게임을 설계한 뒤 온라인 게임 과정에서 발생한 초등학생들의 로그 데이터(log data)를 분석하여 분수 분할 활동 패턴을 군집화하고 군집별 학생의 분수학습 방식을 조사하였다. 나아가 Araya et al.(2014)은 수학적 모델링 학습을 위해 설계된 게임 기반 온라인 학습에서 초등학교와 중학교 학생의 학습패턴을 검토하였고, Kim, Yoon, Jo, & Branch(2018)는 온라인 통계 수업에서 대학생들의 자기주도적 학습패턴을 3개의 군집으로 분류하고 군집별 효과적인 교수학적 전략을 제시하였다. 둘째, 선행연구에서는 맞춤형 수학 교수·학습을 제공하기 위해 지능형 교수 시스템(intelligent tutoring system, 이하 ITS)과 같은 AI 기반 시스템을 개발하여 사용하였다. 앞서 기술된 학습 분석학적 측면은 주로 연구자의 관점에서 AI 기술이 사용되었던 반면 맞춤형 교수·학습 시스템은 실제 학생과 교사가 현장에서 활용할 수 있다는 점에서 보다 실제적이다. |
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