Analysis of the Effectiveness of Online Education of Liberal Arts Coding Classes in the AI Era: The Mediating Effect of Learning Flow

Abstract

Purpose: This study aims to analyze the effectiveness of online education in liberal arts coding classes that have different characteristics from general online theory classes. Through this, it is a study conducted to find out what factors to pay attention to in order to increase the educational effect of online liberal arts coding classes and to derive various implications for improving learners learning outcomes in online liberal arts coding classes. Method: To this end, a survey was conducted on 125 college students who took coding classes. SPSS/WIN 23.0 was used for data analysis, and multiple regression analysis was performed by using learning motivation, class content, and learning flow as independent variables, and inputting learning satisfaction and academic achievement as dependent variables. In order to find out the mediating effect of learning flow, a hierarchical regression analysis proposed by Baron and Kenny was conducted, and the significance of the mediating effect was additionally verified through the Sobel test. Results: In online liberal arts coding classes, learning motivation and class content were found to have a direct effect on academic achievement. There was no mediating effect of learning flow in the relationship between learn-ing motivation, class content, and academic achievement. In online liberal arts coding classes, learning flow was found to have a complete mediating effect in the relationship between learning motivation and learning satisfaction. In addition, learning flow was found to partially mediate between class content and learning satisfaction. Conclusion: Through this study, it can be confirmed that learning motivation and class content factors directly affect academic achievement and significantly affect learning satisfaction of online liberal arts coding classes through learning flow.