Learning Style for AI Convergence in Technology Education
AUTHORS
Mika Lim,Chungnam National University, Daejeon, Korea
ABSTRACT
The purpose of this study was to devise a model of learning style for individualized learning of AI convergence in technological problem solving, which is the main teaching-learning method of technology subject. Among the dimensions of recognition-judgment, creativity, execution, attitude, and interaction of technological problem-solving learning styles, the dimensions that act mainly according to the problem content and learning form were selected and constructed as a model. The method of research for this purpose was Kolb's research method, which derives four learning styles by synthesizing the correlation between two types of information perception methods and two types of information processing methods. Therefore, in this study, a creativity-execution model was devised in consideration of the ‘adaptation-innovation type’ of the creativity level and the ‘reflection-action type’ of the execution level. In addition, an interactive-attitude model was devised in which the 'independent-cooperative type' in the interaction dimension and the 'avoidance-participation type’ in the attitude dimension act dynamically. In addition, a five-factor learning style model for technological problem solving was devised and presented, with all five dimensions of recognition-judgment, creativity, execution, interaction, and attitude of the technological problem-solving learning style as factors. The learner characteristics defined through the model design suggested the direction of AI convergence education considering individual characteristics of learners and provided basic data for it. In the future, it is suggested that specific individualized teaching and learning development research be conducted in AI convergence education, taking into account the characteristics of learners according to the learning style of technological problem solving.
KEYWORDS
AI, AI convergence education, Individualized learning, Learning style model, Technological problem-solving
REFERENCES
[1] M. Lim, “A study on the direction of technology education in the age of artificial intelligence,” Journal of Korean Practical Arts Education, vol.33, no.4, pp.81-102, (2020)
[2] Oxford English Dictionary, http://www.oed.com/view/Entry/271625, (2018)
[3] W. Holmes, M. Bialik, and C. Fadel, “Artificial intelligence in education, Boston: Center for curriculum redesign,” (2019)
[4] H. Nam and H. Cho, “Categories of social studies education with artificial intelligence and ideas for teaching practice,” vol.31, pp119-S133, (2020
[5] W. Shin and D. Shin, “A study on the application of artificial intelligence in elementary science education,” vol.39, no.1, pp.117-132, (2020)
[6] H. M. Wu, B. C. Kuo, and S. C. Wang, “Computerized dynamic adaptive tests with immediately individualized feedback for primary school mathematics learning,” Journal of Educational Technology and Society, vol.20, no.1, pp.61-72, (2017)
[7] C. Wongwatkit, N. Srisawasdi, G. J. Hwang, and P. Panjaburee, “Influence of an integrated learning diagnosis and formative assessment-based personalized web learning approach on students learning performances and perceptions,” Interactive Learning Environments, vol.25, no.7, pp.889-903, (2017)
[8] M. Lim and J. Kim, “Derivation of learning styles for solving technological problems,” Test Engineering and Management, vol.83, pp.5632-5638, (2020)
[9] D. A. Kolb, “Learning style inventory,” LSI-Ⅱa, Boston: McBer and Company, (1993
[10] S. Nam, “The development of the technological thinking disposition measurement instrument department of industrial education,” Unpublished doctoral dissertation, Chungnam National University, Korea, (2010)