인공지능을 도입한 과학과 교수.학습 연구 동향의 메타분석
A Meta-Analysis on Research Trends in Science Teaching and Learning Using Artificial Intelligence
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Previous studies have reported that AI could contribute to supporting individualized lessons and assessments, project learning, problem-solving instructions, studying immersions, academic achievements, and the effects of teaching and learning. Focused on science teaching and learning with AI, the purpose of this study aimed to investigate research trends and analyze the frequencies, keyword topic modeling, and effect size of scholarly articles, published in domestic and overseas journals from 2018 to 2022. Through the databases for searching such as RISS, KISS, EBSCO, ProQuest, ERIC, SCOPUS, and Web of Science, the subject publications were selected by the main keywords ‘AI science education’ and ‘science teaching and learning with AI.’ From the 1104 articles of the first round, a total of 32 articles were chosen based on deduplication and examination by the criteria. The results showed that nine domestic and twelve overseas journals published cases on teaching and learning using AI in science classes. The frequency analysis was provided according to published years, journals, science topics, study approaches, contents, and subject groups. As the results of keyword analysis, high co-occurrence keywords were learning, education, intelligence, convergence, science education, scientific, machine learning, and model. In the 2-mode network modeling visualizations, the betweenness words were intelligence, scientific, AI, machine learning, students, meta-analysis, STEM education, mathematics, and reliability. The influential keywords by the mean of degree centrality revealed AI, machine learning, and convergence education. The LDA showed four topic groups in both domestic and overseas keywords. In terms of the types of AI technologies, education-specialized AI platforms, for example, Machine Learning for Kids and Teachable machine, were adapted to the lessons of the domestic publications, while primary studies of overseas papers covered developing teacher-supporting services using AI. Lastly, the effect sizes of science teaching and learning using AI indicated the mid-sized effect as 0.454 in domestic and 0.590 in overseas articles. Meanwhile, the highest effective variables from the studies were disclosed attitudes toward AI, scientific preference, and creative problem-solving skills. Considering the subject groups and the usage of AI as moderating variables, their effect did not show a significant difference. Through a comprehensive understanding of science teaching and learning using AI principles and technologies, further studies need to seek a better intention point for AI-integrated science lessons.
Abstract
Previous studies have reported that AI could contribute to supporting individualized lessons and assessments, project learning, problem-solving instructions, studying immersions, academic achievements, and the effects of teaching and learning. Focused on science teaching and learning with AI, the purpose of this study aimed to investigate research trends and analyze the frequencies, keyword topic modeling, and effect size of scholarly articles, published in domestic and overseas journals from 2018 to 2022. Through the databases for searching such as RISS, KISS, EBSCO, ProQuest, ERIC, SCOPUS, and Web of Science, the subject publications were selected by the main keywords ‘AI science education’ and ‘science teaching and learning with AI.’ From the 1104 articles of the first round, a total of 32 articles were chosen based on deduplication and examination by the criteria. The results showed that nine domestic and twelve overseas journals published cases on teaching and learning using AI in science classes. The frequency analysis was provided according to published years, journals, science topics, study approaches, contents, and subject groups. As the results of keyword analysis, high co-occurrence keywords were learning, education, intelligence, convergence, science education, scientific, machine learning, and model. In the 2-mode network modeling visualizations, the betweenness words were intelligence, scientific, AI, machine learning, students, meta-analysis, STEM education, mathematics, and reliability. The influential keywords by the mean of degree centrality revealed AI, machine learning, and convergence education. The LDA showed four topic groups in both domestic and overseas keywords. In terms of the types of AI technologies, education-specialized AI platforms, for example, Machine Learning for Kids and Teachable machine, were adapted to the lessons of the domestic publications, while primary studies of overseas papers covered developing teacher-supporting services using AI. Lastly, the effect sizes of science teaching and learning using AI indicated the mid-sized effect as 0.454 in domestic and 0.590 in overseas articles. Meanwhile, the highest effective variables from the studies were disclosed attitudes toward AI, scientific preference, and creative problem-solving skills. Considering the subject groups and the usage of AI as moderating variables, their effect did not show a significant difference. Through a comprehensive understanding of science teaching and learning using AI principles and technologies, further studies need to seek a better intention point for AI-integrated science lessons.
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