Factors Affecting Student Learning Outcomes in the Context of an Art and Science Program Integrated with Artificial Intelligence
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As artificial intelligence (AI) has been rapidly adopted in educational fields, significant efforts have been made to foster the integration of AI into K-12 educational contexts. In this study, we aimed to design and implement a science, technology, engineering, arts, and mathematics (STEAM) educational program that incorporates AI under the theme of space exploration and to examine the relationships among the factors affecting student learning outcomes. Two hundred and twenty-seven seventh graders participated in the AI-STEAM program under the theme of space exploration. Students were invited to respond to an instrument examining self-efficacy, task value, learning engagement, and several learning outcome variables. Path analysis was conducted to verify the appropriateness of the set path and to investigate the relationship between variables. The results indicate that students’ self-efficacy in learning AI had a significant direct impact on their learning engagement and an indirect impact on learning satisfaction and career aspiration to AI fields. Additionally, students’ perception of task value and learning engagement directly affected their learning satisfaction and career aspiration to AI fields. Interestingly, students’ conceptual understanding of AI was not directly or indirectly predicted by any factors. The results provide pedagogical implications for teaching practitioners in designing and implementing AI-integrated lessons in curriculums.
Abstract
As artificial intelligence (AI) has been rapidly adopted in educational fields, significant efforts have been made to foster the integration of AI into K-12 educational contexts. In this study, we aimed to design and implement a science, technology, engineering, arts, and mathematics (STEAM) educational program that incorporates AI under the theme of space exploration and to examine the relationships among the factors affecting student learning outcomes. Two hundred and twenty-seven seventh graders participated in the AI-STEAM program under the theme of space exploration. Students were invited to respond to an instrument examining self-efficacy, task value, learning engagement, and several learning outcome variables. Path analysis was conducted to verify the appropriateness of the set path and to investigate the relationship between variables. The results indicate that students’ self-efficacy in learning AI had a significant direct impact on their learning engagement and an indirect impact on learning satisfaction and career aspiration to AI fields. Additionally, students’ perception of task value and learning engagement directly affected their learning satisfaction and career aspiration to AI fields. Interestingly, students’ conceptual understanding of AI was not directly or indirectly predicted by any factors. The results provide pedagogical implications for teaching practitioners in designing and implementing AI-integrated lessons in curriculums.
참고문헌 (61)
- Acuta, "Actua’s Artificial Intelligence(AI)Education Handbook" (2020), Actua
- Ali, S., "Constructionism, Ethics, and Creativity: Developing Primary and Middle School Artificial Intelligence Education", the International Workshop on Education in Artificial Intelligence K-12 (EDUAI-19) (2019)
- Ardies, J., "Reconstructing the Pupils Attitude towards Technology-survey", Design and Technology Education, 18(1), pp.8~19 (2013)
- Bandura, A., "Self-efficacy : Toward a Unifying Theory of Behavioral Change", Psychological Review, 84, pp.191~215 (1977)
- Bandura, A., "Social Foundations of Thought and Action : A Social Cognitive Theory" (1986), Prentice Hall
- Becker, F. S., "Why Don’t Young People Want to Become Engineers? Rational Reasons for Disappointing Decisions", European Journal of Engineering Education, 35(4), pp.349~366 (2010)
- Blotnicky, K. A., "A Study of the Correlation between STEM Career Knowledge, Mathematics Self-efficacy, Career Interests, and Career Activities on the Likelihood of Pursuing a STEM Career among Middle School Students", International Journal of STEM Education, 5(22) (2018)
- Byun, A., "The Effect of Design Classes Using Artificial Intelligence in the Era of COVID-19 on Social Responsibility of High School Students", Archives of Design Research, 35(4), pp.251~266 (2022)
- Chan, C. K. Y., "Students’ Perceptions of Engineers : Dimensionality and Influences on Career Aspiration in Engineering", International Journal of Technology and Design Education, 29(3), pp.421~439 (2019)
- Chang, Y. S., "Integrating Artificial Intelligence into Steam Education", International Cognitive Cities Conference 2019 (2019)
- Chassignol, M., "Artificial Intelligence Trends in Education : A Narrative Overview", Procedia Computer Science, 136, pp.16~24 (2018)
- Chen, L., "Artificial Intelligence in Education : A Review", IEEE Access, 8, pp.75264~75278 (2020)
- Chiu, T. K., "Sustainable Curriculum Planning for Artificial Intelligence Education : A Self-determination Theory Perspective", Sustainability, 12(14), pp.5568~5585 (2020)
- Cole, J. S., "Predicting Student Achievement for Low Stakes Tests with Effort and Task Value", Contemporary Educational Psychology, 33(4), pp.609~624 (2008)
- CSET, "AI Innovation Action Plan for Institutions of Higher Education", ~ (2019)
- Cui, X., "How Online Task Value and Collaboration Preference Affect Learning Engagement in Flipped Learning : Mediating Effects of Self-regulated Learning Ability", Global Creative Leader : Education & Learning, 9(6), pp.93~117 (2019)
- Curran, P. J., "The Robustness of Test Statistics to Nonnormality and Specification Error in Confirmatory Factor Analysis", Psychological Methods, 1(1), pp.16~29 (1996)
- Dogan, U., "Student Engagement, Academic Self-efficacy, and Academic Motivation as Predictors of Academic Performance", The Anthropologist, 20(3), pp.553~561 (2015)
- Dotterer, A. M., "Classroom Context, School Engagement, and Academic Achievement in Early Adolescence", Journal of Youth and Adolescence, 40(12), pp.1649~1660 (2011)
- Eccles, J. S., "Achievement and Achievement Motives: Psychological and Sociological Approaches" (1983), Freeman
- Eccles, J. S., "In the Mind of the Actor : The Structure of Adolescents’ Achievement Task Values and Expectancy-related Beliefs", Personality and Social Psychology Bulletin, 21(3), pp.215~225 (1995)
- Fredricks, J. A., "School Engagement : Potential of the Concept, State of the Evidence", Review of Educational Research, 74(1), pp.59~109 (2004)
- Fredricks, J. A., "Supporting Girls’ and Boys’ Engagement in Math and Science Learning : A Mixed Methods Study", Journal of Research in Science Teaching, 55(2), pp.271~298 (2018)
- Gardner, M., "Interpreting Integrated STEM : Sustaining Pedagogical Innovation within a Public Middle School Context", International Journal of Science and Mathematics Education, 17, pp.1283~1300 (2018)
- Gherheş, V., "Why are We Afraid of Artificial Intelligence(AI)?", European Review of Applied Sociology, 11(17), pp.6~15 (2018)
- Han, J., "Changes in Attitudes and Efficacy of AI Learners According to the Level of Programming Skill and Project Interest in AI Project", Journal of the Korean Association of Information Education, 24(4), pp.391~400 (2020)
- Heo, H., "Development and Application of the ‘Advanced Science Research’ Section Class Program in the Science Inquiry Experiment on the Subject of Artificial Intelligence", Journal of Learner-Centered Curriculum and Instruction, 22(8), pp.173~190 (2022)
- Hwang, Y., "Development of AI Convergence Lesson for Elementary Theater Unit with ChatGPT", Journal of The Korean Association of information Education, 27(5), pp.625~634 (2023)
- Jho, H., "Understanding of Generative Artificial Intelligence Based on Textual Data and Discussion for Its Application in Science Education", Journal of the Korean Association for Science Education, 43(3), pp.307~319 (2023)
- Kim, H., "The Influence of Convergence Design Lessons Using Artificial Intelligence(AI)on Middle School Students’ Self-directed Learning : Focusing on Game Design Class That Removes Space Debris", Archives of Design Research, 34(2), pp.89~103 (2021)
- Kim, J., "A Study on Educative Utilization of Generative AI : Focusing on ChatGPT Utilization", Journal of The Korean Association of information Education, 27(6), pp.691~704 (2023)
- Kim, S., "Relationships among Self-regulation, Teaching Presence, Perceived Interaction, and Learning Outcomes in a Flipped Learning Environment : Mediating Effects of Perceived Interaction", The Korean Journal of Educational Methodology Studies, 28(4), pp.743~766 (2016)
- Kutnick, P. J., "Engineering Education Opportunities, Perceptions and Career Choice of Secondary School Students in Hong Kong SAR, China", the 2012 American Society for Engineering Education Annual Conference and Exposition (2012)
- Kwon, K., "An Analysis of Relationship among Academic Self-efficacy, Task Value, Learning Engagement, Academic Achievement and Learning Satisfaction in the Flipped Learning Environment", Journal of Educational Technology, 34(3), pp.407~439 (2018)
- Lee, H., "Comparison of Educational Goals between Two Majors, AI Convergence Education and Computer Education in Graduate School of Education", The Journal of Korean Association of Computer Education, 25(1), pp.63~66 (2021)
- Lee, J., "An Analysis of Educational Effectiveness of Elementary lLevel AI Convergence Education Program", Journal of The Korean Association of Information Education, 25(3), pp.471~181 (2021)
- Lin, T. J., "Multi-dimensional Explorations into the Relationships between High School Students’ Science Learning Self-efficacy and Engagement", International Journal of Science Education, 43(8), pp.1~15 (2021)
- Ministry of Education, "Direction of Education Policy and Core Tasks in the Age of Artificial Intelligence", ~ (2020)
- Ministry of Education, "2022 Revised Korean National Curriculum (No. 2022-33)", ~ (2022)
- Pajares, F., "Role of Self-efficacy and Self-concept Beliefs in Mathematical Problem Solving : A Path Analysis", Journal of Educational Psychology, 86(2), pp.193~203 (1994)
- Patrick, H., "Early Adolescents’ Perceptions of the Classroom Social Environment, Motivational Beliefs, and Engagement", Journal of Educational Psychology, 99(1), pp.83~98 (2007)
- Pintrich, P. R., "Student Perceptions in the Classroom: Causes and Consequences" (1992), Lawrence Erlbaum
- Pintrich, P. R., "Motivational and Self-regulated Learning Components of Classroom Academic Performance", Journal of Educational Psychology, 82(1), pp.33~40 (1990)
- Pintrich, P. R., "A Manual for the Use of the Motivated Strategies for Learning Questionnaire (MSLQ)", University of Michigan, National Center for Research to Improve Postsecondary Teaching and Learning, ~ (1991)
- Pintrich, P. R., "Reliability and Predictive Validity of the Motivated Strategies for Learning Questionnaire(MSLQ", Educational and Psychological Measurement, 53(3), pp.801~813 (1993)
- Reeve, J., "How Students Create Motivationally Supportive Learning Environments for Themselves : The Concept of Agentic Engagement", Journal of Educational Psychology, 105, pp.579~595 (2013)
- Reeve, J., "Agency as a Fourth Aspect of Students’ Engagement During Learning Activities", Contemporary Educational Psychology, 36(4), pp.257~267 (2011)
- Ryu, M., "The Educational Perception on Artificial Intelligence by Elementary School Teachers", Journal of the Korean Association of Information Education, 22(3), pp.317~324 (2018)
- Schunk, D. H., "Self-efficacy and Academic Motivation", Educational Psychologist, 26(3-4), pp.207~231 (1991)
- Shen, D., "Unpacking Online Learning Experiences : Online Learning Self-efficacy and Learning Satisfaction", The Internet and Higher Education, 19, pp.10~17 (2013)
- Shin, S., "High School Students’ Perception of Artificial Intelligence : Focusing on Conceptual Understanding, Emotion and Risk Perception", Journal of Learner-Centered Curriculum and Instruction, 17(21), pp.289~312 (2017)
- Sobel, M. E., "Asymptotic Confidence Intervals for Indirect Effects in Structural Models", Sociological Methodology, 13, pp.290~312 (1982)
- Tas, Y., "The Contribution of Perceived Classroom Learning Environment and Motivation to Student Engagement in Science", European Journal of Psychology of Education, 31(4), pp.557~577 (2016)
- Touretzky, D., "Envisioning AI for K-12: What Should Every Child Know about AI?", Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (2019)
- Turner, S. L., "Family Factors Associated with Sixth-grade Adolescents’ Math and Science Career Interests", The Career Development Quarterly, 53(1), pp.41~52 (2004)
- Uçar, F. M., "The Role of Perceived Classroom Goal Structures, Self-efficacy, and Engagement in Student Science Achievement", Research in Science & Technological Education, 35(2), pp.149~168 (2017)
- Wang, M. T., "Adolescents’ Perceptions of School Environment, Engagement, and aAcademic Achievement in Middle School", American Educational Research Journal, 47(3), pp.633~662 (2010)
- Wang, M. T., "The Math and Science Engagement Scales : Scale Development, Validation, and Psychometric Properties", Learning and Instruction, 43, pp.16~26 (2016)
- Wigfield, A., "The Development of Achievement Task Values : A Theoretical Analysis", Developmental Review, 12(3), pp.265~310 (1992)
- Xu, W., "The Application of AI Technologies in STEM Education : A Systematic Review from 2011 to 2021", International Journal of STEM Education, 9(1), pp.1~20 (2022)
- Yoon, T., "Effects of Primary ELLs’ Affective Factors and Satisfaction Through AI-based Speaking Activity", The Journal of the Korea Contents Association, 21(9), pp.34~41 (2021)