- Volume 40 Issue 1
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A Case Study on the Application of Plant Classification Learning for 4th Grade Elementary School Using Machine Learning in Online Learning
온라인 학습에서 머신러닝을 활용한 초등 4학년 식물 분류 학습의 적용 사례 연구
- Shin, Won-Sub (Seoul National University of Education) ;
- Shin, Dong-Hoon (Seoul National University of Education)
- Received : 2021.01.21
- Accepted : 2021.01.26
- Published : 2021.02.28
This study is a case study that applies plant classification learning using machine learning to fourth graders in elementary school in online learning situations. In this study, a plant classification learning education program associated with 2015 revision science curriculum was developed by applying the Artificial Intelligence biological classification teaching Learning model. The study participants were 31 fourth graders who agreed to participate voluntarily. Plant classification learning using machine learning was applied six hours for three weeks. The results of this study are as follows. First, as a result of image analysis on artificial intelligence, participants were mainly aware of artificial intelligence as mechanical (27%), human (23%) and household goods (23%). Second, an artificial intelligence recognition survey by semantic discrimination found that artificial intelligence was recognized as smart, good, accurate, new, interesting, necessary, and diverse. Third, there was a difference between men and women in perception and emotion of artificial intelligence, and there was no difference in perception of the ability of artificial intelligence. Fourth, plant classification learning using machine learning in this study influenced changes in artificial intelligence perception. Fifth, plant classification learning using machine learning in this study had a positive effect on reasoning ability.
- Chung, W., Hur, M. & Cha, H. (1991). A study on the concept of plant classification among Korean elementary, middle and high school students. Journal of the Korean Association for Science Education, 11(1), 25-36.
- Jho, H. (2018). Exploration of predictive model for learning outcomes of students in the E-learning environment by using machine learning. Journal of Learner-Centered Curriculum and Instruction, 18(21), 553-572. https://doi.org/10.22251/jlcci.2018.18.21.553
- Jung, J. Y. (2020). [News 1 Korea] We will strengthen artificial intelligence education in the 'AI era'..."Introduction of Artificial Intelligence Subjects." Seoul= News1, Retried November 20, 2020, from, https://www.news1.kr/articles/?4124828.
- Jung, M., Kim, M. & Song, J. (2020). Color analysis of music textbook illustration for 3rd and 4th graders in elementary school using machine learning. The Korean Journal of Arts Education, 18(2), 93-118
- Kim, J. H. (2016). Fourth industrial revolution, education in the age of artificial intelligence. STSS Conference on Sustainable Science, 21-29.
- Kim, K. Y. (2018). A study on model of skin type judgment tool using machine learning technique. The Treatise on The Plastic Media, 21(4), 115-121.
- Kim, Y. (2020). Analysis of the root of beans using machine learning. Korea Soybean Society, 350, 5-7.
- KOFAC. (2019). 2019 AI convergence education conference, 2019 AI Convergence Education Conference Policy Kit, Seoul: Korea Foundation for the Advancement of Science & Creativity.
- Kwon, J. S. & Kim, B. K. (1994). The development of an instrument for the measurement of science process skills of the Korean elementary and middle school students. Journal of the Korean Association for Science Education, 14(3), 251-264.
- Lee, G., Ha, H., Hong, H. & Kim, H. (2018). Exploratory research on automating the analysis of scientific argumentation using machine learning. Journal of the Korean Association for Science Education, 38(2), 219-234. https://doi.org/10.14697/JKASE.2018.38.2.219
- Lee, Y. & Cho, J. (2020). Artificial intelligence education plan using teachable machine. The Korean Institute of Information Scientists and Engineers, 2020, 913-915.
- Lee, Y. H., Choi, Y. H., Han, J. Y., Lee, H. K. & Bang, J. H (2005). A case study on attitude of teachers and students toward practical arts(technology education․ home economics) subject matter through semantic differential method. The Journal of Vocatonal Education Research, 24(3), 1-22.
- Ministry of Education. (2015). Science curriculum. Ministry of Education.
- Ministry of Education. (2019). Teacher's guide science 6-2. Seoul: Visang Education.
- Nam, Y. G., Hong S. K., Jang, S. H., Cho, S. H. & Oh, S. J. (2019). A study on prediction of weather data by using machine learning method. The KSMT Autumn Conference 2019, 58-59.
- Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. (1957). The measurement of meaning (No. 47). University of Illinois press.
- Park, M., Lim, H., Kim, J., Lee, K. & Kim, M. (2020). 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. J. Korean Soc. Math. Ed. Ser. A: The Mathematical Education, 59(4), 373-387.
- Raschka, S. & Mirjalili, V. (2019). Python machine Learning: Machine learning and deep learning with python, scikit-learn, and TensorFlow. [머신 러닝 교과서 with 파이썬, 사이킷런, 텐서플로]. Seoul: Gilbut.
- Ryu, M. & Han, S. (2017). Image of artificial intelligence of elementary students by using semantic differential scale. Journal of the Korean Association of Information Education, 21(5), 527-535. https://doi.org/10.14352/jkaie.21.5.527
- Shin, S., Ha, M. & Lee, J. (2018). Exploring elementary school students' image of artificial intelligence. Elementary Science Education, 37(2), 126-146.
- Shin, W. S. & Shin, D. H. (2020). A study on the application of artificial intelligence in elementary science education. Elementary Science Education, 39(1), 117-132.
- Shin, W. S. (2020a). A case on application of artificial intelligence convergence education in elementary biological classification learning. Elementary Science Education, 39(2), 284-295.
- Shin, W. S. (2020b). Exploring the possibility of artificial intelligence science convergence education in energy and life unit. Energy and Climate Change Education, 10(1), 73-86.
- Sung, J. H. & Cho, Y. S. (2019). Machine learning approach for pattern analysis of energy consumption in factory, KIPS Trans. Comp. and Comm. Sys., 8(4), 87-92. https://doi.org/10.3745/KTCCS.2019.8.4.87
- Yoo, H. M. & Kim, J. G. (2006). A study on the biology ⅱ textbooks by analysing questions for the college scholastics ability test and the biology teachers' appointment test: Focused on the chapters of taxonomy and ecology. Biology Education, 34(3), 307-320.
- Yoon, S. H., Lee, S. H. & Kim, H. W. (2019). A study of the classification and application of digital broadcast program type based on machine learning. Knowledge Management Research, 20(3), 119-137.