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.

This study aims to analyze whether technological changes in the fourth industrial revolution (4IR), as independent variables, can influence employment, a dependent variable. It categorizes scientific technology changes in the 4IR based on related research, and identifies six factors and corresponding research hypotheses. The paths for the six hypotheses were analyzed using 275 effective samples. Results show that life-convenience technology and blockchain technology variables significantly influenced employment (p < 0.001). Additionally, innovation technology, interface technology, human life technology, and 3D technology variables significantly influenced employment (p < 0.01). The power of the total variance explanation (69.596%) for the employment influence was very high. Seven items—self-driving cars, decision-making using big data, Internet of Things, Wearable Internet, Designer Beings, 3D printing technology and human health, and Bitcoin and blockchain—were statistically significant for the employment effect. The study obtained effective paths for the employment influence of fundamental technologies and derived the demographic variable presenting a meaningful difference among groups. This research seeks a policy direction that enables preparation for 4IR deployment. It also contributes to the academic sphere in meaningfully and empirically classifying the technology factors of the 4IR.

In this paper, the development trajectory of accounting information systems was analyzed using business method patents and main path analysis based on knowledge persistence. Knowledge persistence-based main path analysis can dramatically minimize the complexity of a knowledge network without omission of the dominant knowledge flows, and so it is a suitable selection for analyzing the business method patents on accounting information systems. The main findings from the empirical results are as follows: the empirical results show that accounting information system patents were developed along with the software patent of knowledge injected from the outside. Bookkeeping/accounting and taxation systems expanded from the basic calculation, storage, and payment technique to scalability to other techniques and functions in more complex situations. This research has found that technological advancement has facilitated and been supporting the development of accounting information system over the years, as shown in the patents filed under business solutions. There is a clear indication of the growing complexity of those patents, signifying the moves/advancement of corporate business information systems from financial accounting-oriented systems to more complex ERP systems.