[논문]예비교사를 위한 머신러닝 활용 물질의 상태 분류에 대한 융합교육 프로그램의 효과 분석

이소율; 이영준; 백성혜

doi:10.22156/cs4smb.2022.12.05.139

예비교사를 위한 머신러닝 활용 물질의 상태 분류에 대한 융합교육 프로그램의 효과 분석
Analysis of Effects of Convergence Education Program about State Classification of the Matters using Machine Learning for Pre-service Teachers 원문보기

융합정보논문지 = Journal of Convergence for Information Technology, v.12 no.5, 2022년, pp.139 - 149

이소율 (한국교원대학교 컴퓨터교육과) , 이영준 (한국교원대학교 컴퓨터교육과) , 백성혜 (한국교원대학교 화학교육과)

초록
AI-Helper

본 연구는 예비교사의 미래 교육을 위한 인공지능 융합교육 역량을 함양하고, 동시에 학생의 학습 과정에 대한 이해를 증진할 수 있는 교육 프로그램을 개발하고 효과를 분석하는 것을 목적으로 하였다. 이를 위해 물질의 상태 분류를 주제로 머신러닝포키즈와 스크래치3를 활용한 인공지능 융합교육 프로그램을 15주차 분량으로 개발하였다. 개발된 내용은 자발적으로 참여한 K대학교 예비교사들에게 처치되었다. 그 결과, 예비교사들은 머신러닝의 학습을 이해하는 과정을 통해 학생의 학습 과정을 비유적으로 이해할 수 있었다. 또한, 인공지능 교수효능감의 사전-사후 t검정 결과는 t=-7.137(p< .000)으로 통계적으로 유의한 향상을 보였다. 따라서 본 연구에서 개발한 인공지능 융합교육 프로그램은 교생실습 외에 비간접적인 방식으로 예비교사의 학생에 대한 이해를 높일 수 있는데 도움이 되고, 인공지능 교육 역량 함양에 기여할 수 있음이 시사된다.

Abstract ▼ AI-Helper

The purpose of this study is to develop and analyze the effects of an educational program that can cultivate artificial intelligence(AI) convergence education competency for future education and enhance students' understanding of pre-service teachers. For this end, an AI convergence education program using Machine Learning for Kids and Scratch 3 was developed for 15 weeks under the theme of classifying the state of matter. The developed program were treated by K University pre-service teachers who participated voluntarily. As a result, pre-service teachers were able to metaphorically understand the learning process of students through understanding of machine learning training process. In addition, the pre-post t-test result of AI teaching efficacy showed a statistically significant improvement with t=-7.137 (p<.000). Therefore, it is suggested that the AI convergence education program developed in this study can help to increase the understanding of the pre-service teacher's students in an indirect way other than practice teaching, and can contribute to foster AI education competency.

주제어

표/그림 (15)

그림 Fig. 1. Design of Experiment
표 Table 1. General Background of Participants (N=20)
표 Table 2. Consist of AI-TEBI[19]
표 Table 3. Curriculum of AI Convergence Education using ML4K for Classifying of the Matters
표 Table 4. Classification Criteria for Solids, Liquids, and Gases Presented in Korean ElementarySchool Science Textbooks
표 Table 5. Labels, Features, and Training Data for Model of Classifying the State of Matter
그림 Fig. 2. Code Script
그림 Fig. 3. Example of Program Execution
그림 Fig. 5. Decision Tree for Classification Program
표 Table 6. Team Project Case of Revised Labels, Features, and Training Data Sets
표 Table 7. Example of Input Values and Classification Results for Various Objects
표 Table 8. Another Example of Input Values and Classification Results for Various Objects
표 Table 9. Result of Normality Test (N=20)
표 Table 10. Paired t-test result of Pre-Post AI-TEBI (N=20)
표 Table 11. Paired t-test result of Pre-Post AI-TEBI according to Area (N=20)

참고문헌 (31)

S. Russell & P. Norvig. (2021). Artificial Intelligence: A Modern Approach. 4th ed. NJ: Pearson.
K. Schwab. (2017). The fourth industrial revolution, Geneva: World Economic Forum, pp. 1-73.
H. J. Jeon. (2021.02.02.). Including the artificial intelligence convergence project (AI+X), the public offering for 18 new projects KRW 40.7 billion started in '21. (Online). http://www.aitimes.kr/news/articleView.html?idxno20161
Ministry of Education. (2021.11.24.). Announcement the main importance of the 2022 revised curriculum overview (Online). https://www.moe.go.kr/boardCnts/viewRenew.do?boardID294&boardSeq89671&lev0&searchTypenull&statusYNW&page1&smoe&m020402&opTypeN
Y. H. Kim. (2021). Research Subject Trend Analysis on AI Education with Network Text Analysis on Korean Journals. Journal of Educational Innovation Research, 31(4), 197-217. DOI : 10.21024/pnuedi.31.4.202112.197
R. Mitchell, J. Michalski & T. Carbonell. (2013). Machine Learning: An Artificial Intelligence Approach. Berlin: Springer.
J. P. Smith III, A. A. DiSessa & J. Roschelle. (2009). Misconceptions Reconceived: A Constructivist Analysis of Knowledge in Transition. Journal of the Learning Sciences, 3(2), 115-163. DOI : 10.1207/s15327809jls0302_1

상세보기
H. J. Kang & J. S. Song. (2008). The Analysis of Concept Patterns and The Relevance of the States of Matter presented in Science Textbook. SECONDARY EDUCATION RESEARCH, 56(3), 271-292. DOI : 10.25152/ser.2008.56.3.271
M. B. Nakhleh. (1992). Why Some Students Don't Learn Chemistry: Chemical Misconceptions. Journal of Chemical Education, 69(3), 191-196. DOI : 10.1021/ed069p191.

상세보기
S. H. Paik, S. K. Kim & Y. M. Kim. (2008). Type Analysis of Secondary School Students' and Science Teachers' Criteria for Classifying States of Various Matter. Journal of the Korean Chemical Society, 52(5), 569-579. UCI : G704-000364.2008.52.5.004

원문보기 상세보기
E. S. Lee & J. H. Park. (2008). The Investigation of the Concept of Matter State of the Elementary Students and Teachers. Journal of Research in Curriculum Instruction, 12(1), 183-200. DOI : 10.24231/rici.2008.12.1.183

상세보기
P. Ashton. (1984). Teacher efficacy: A motivational paradigm for effective teacher education. Journal of teacher education, 35(5), 28-32. DOI : 10.1177/002248718403500507

상세보기
A. Bandura. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191-215. DOI : 10.1037/0033-295X.84.2.191

상세보기
H. N. Kim. (2010). An Analysis of Elementary Science Teaching Efficacy. Journal of educational studies, 41(1), 97-118. UCI : G704-001618.2010.41.1.002
M. B. Kang & J. H. Kim. (2014). The Development of Mathematics Teaching Efficacy Instrument. Journal of Elementary Mathematics Education in Korea, 18(3), 519-537. UCI : G704-SER000010591.2014.18.3.009
S. Y. Yi & Y. J. Lee. (2017). The Development of Teachers' Training Course about Educational Programming Language to Enhance Informatics Teaching Efficacy for Elementary School Teachers. The Journal of Korean Association of Computer Education, 20(5), 35-47. DOI : 10.32431/kace.2017.20.5.003
S. Y. Yi & Y. J. Lee. (2018). Development of Software Education Teaching Efficacy Belief Instrument for Elementary School Teachers. The Journal of Korean Association of Computer Education, 21(6), 93-103. DOI : 10.32431/kace.2018.21.6.008

원문보기 상세보기
H. J. Park, H. S. Kim., J. I. Choi & Y. J. Jeon. (2021). Development of Teaching Efficacy Instrument in Informatics(Software and AI) Subject. The Journal of Korean association of computer education, 24(4), 39-52. DOI : 10.32431/kace.2021.24.4.004
S. Y. Yi, S. W. Kim & Y. J. Lee. (2021). Development of Teaching Efficacy Belief Instrument about Artificial Intelligence for Pre-service Teachers. The Journal of Korean association of computer education, 24(1), 47-61. DOI : 10.32431/kace.2021.24.1.006
S. Gibson & M. H. Dembo. (1984). Teacher efficacy: A construct validation. Journal of educational psychology, 76(4), 569-582. DOI : 10.1037/0022-0663.76.4.569

상세보기
B. Y. Park & H. N. Lee. (2014). Development and Application of Systems Thinking-based STEAM Education Program to Improve Secondary Science Gifted and Talented Students' Systems Thinking Skill. Journal of Gifted/Talented Education, 24(3), 421-444..

원문보기 상세보기
S. Y. Yi & Y. J. Lee. (2021). Development of Artificial intelligence Education based Convergence Education Program for Classifying of Reptiles and Amphibians. Journal of Convergence for Information Technology, 11(12), 168-175. DOI : 10.22156/CS4SMB.2021.11.12.168

원문보기 상세보기
S. A. Kim, J. H. Kim. S. J. Jeon & J. W. Choi. (2022). Development of a contents system to enhance the professionalism of Artificial Intelligence education for pre-service teachers. Journal of the Korean Institute of industrial educators, 47(1), 22-44. DOI : 10.35140/kiiedu.2022.47.1.22

상세보기
H. K. Jeon & Y. S. Kim. (2021). Effect of Anchored AI.SW Convergence Program Using Drones for Pre-service Teachers on Creativity and Convergence Capabilities. The Journal of Korean association of computer education, 25(1), 83-92. DOI : 10.32431/kace.2022.25.1.008
J. R. Kim. (2021). A Pre-Service Teachers' Syllabus Development for AI-based Elementary English Teaching Proficiency Enhancement. Journal of Education Science, 23(3), 79-104.
Scratch. (2022). Scratch - Image, Program, Share. Scratch (Online). https://scratch.mit.edu
Google. (2022). Experiments with Google. Experiments with Google. (Online). https://experiments.withgoogle.com/
Naver Connect Foundation. (2022). Entry - We can be everything. Entry (Online). http://playentry.org
Dale Lane. (2022). Introduction of Machine Learning for Kids. Machine Learning for Kids (Online). https://machinelearningforkids.co.uk/
S. A. Min, I. S. Jeon & K. S. Song. (2021). The Effects of Artificial Intelligence Convergence Education using Machine Learning Platform on STEAM Literacy and Learning Flow. Journal of The Korea Society of Computer and Information, 26(10), 199-208. DOI: 10.9708/jksci.2021.26.10.199

원문보기 상세보기
S. Y. Yi & Y. J. Lee. (2021). Development of Convergence Education Program for 'Understanding of Molecular Structure' using Machine Learning Educational Platform. Journal of The Korean Association of Information Education, 25(6), 961-972. DOI : 10.14352/jkaie.2021.25.6.961

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