Open AccessLearner Model for AI Convergence Education in Technology Subject
Author(s) -
Mika Lim
Publication year - 2022
Publication title -
webology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.259
H-Index - 18
ISSN - 1735-188X
DOI - 10.14704/web/v19i1/web19286
Subject(s) - creativity , convergence (economics) , computer science , dimension (graph theory) , adaptation (eye) , cognitive style , perception , action (physics) , information processing theory , learning styles , information processing , cognition , variation (astronomy) , style (visual arts) , mathematics education , psychology , human–computer interaction , cognitive psychology , social psychology , mathematics , physics , archaeology , quantum mechanics , neuroscience , astrophysics , pure mathematics , economics , history , economic growth
This study devises a model of learner for individualized learning of AI convergence in technological problem solving on technology subjects. It uses the 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. 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. Furthermore, a three-factor learner model was developed considering the dimensions of cognitive judgment, creativity, and execution. Also, a five-factor learner model for technological problem-solving was devised and presented, with all five dimensions of cognition-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, considering the characteristics of learners according to the learning style of technological problem-solving.