As a result of the COVID-19 pandemic, many universities have shifted to non-face-to-face classes resulting in numerous changes in the educational system. Since programming education includes a greater proportion of practice than theory-oriented courses, non-face-to-face classes have several constraints. As a result, to properly execute software education and enhance educational performance for non-major students, it is required to conduct research. Students’ psychological moods and activities collected in online classrooms were used to investigate factors impacting academic success as measured by scores and grades. Multiple regression analysis and logistic regression analysis were conducted by using data mining analytical approach. Attendance, effort expectancy, hedonic motivation, confidence, frequency of communication in mobile chat rooms, and Python programming intention factors were retrieved as an outcome of the performance. The relevance of the factors was confirmed, and it was revealed that hedonic motivation was crucial for students in Class A, while attendance had a significant impact on academic progress for students in the other grades. The goal of this research is to assist university organizations in making decisions by enhancing computer liberal arts education and offering implications for future non-face-to-face teaching environments such as during the COVID-19 pandemic.