In an industry, society gets more interest in software. In accordance with this trend, in the process of composing the university's curriculum, it is increasingly emphasized that problem-based learning through computational thinking and programming ability based on logical thinking is weighted. This study conducted a study on how to identify students' educational characteristics and study intention. In particular, a methodology to explore the study intention for Python programming from the characteristics of each student was reviewed. For this analysis, the relationship between new technology from their point of view and factors is analyzed, factors are identified for methodologies, and statistical methodologies are used to verify them. The purpose of this study is to find improvements for software education operation and to provide help in educational policy decision-making of university members who conduct computer software education.

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.

As the use of devices in mobile environments increases, network attacks such as DDoS have a malicious attempt to flood the network's regular traffic to overload the target and surrounding infrastructure. This research proposed machine learning and deep learning approaches to dealing with DDoS attacks, and the results are described as follows. First, this research successfully detected DDoS attacks through an LGBM with a 100% accuracy score. Second, the proposed model (GRU-LM), which consists of a trained Word2vec layer with the Python dataset, is far more effective than the standard GRU model. Since Python is quite similar to English, language model-based GRU yields superior results. Various preprocessing steps were performed through the NLTK package, and each number was assigned to the tokenized one for constructing the GRU language model. The result reveals that the proposed model achieved an accuracy score of 87% for predicting the following words in the source code, while the rest achieved below 30% accuracy. This conclusion is significant because its relatively simple and light structure overcomes tradeoff problems between time and accuracy and is adaptable to the mobile setting. Discovering traffic patterns for the underlying data of DDOS assaults and retrieving them using statistical data analysis is the value of this research. Furthermore, since public cloud application vulnerability assaults are rising due to expanding cloud infrastructure, this finding could be used in such attacks.

This study proposes a systematic design methodology for supporting field trips in software education. Various attempts have been made to enhance the educational effects of field trips, but it is difficult to continue them due to the limitations of educational resources. Virtual field trips (VFT) have emerged to supplement this, but they are still in need of development. This study designed a platform that integrates the VFT functions of each school’s educational system into one, which guarantees each system’s unique activities and supports the course of technological progression. This proposes a structure in which these processes continue to evolve, and the platform learns from this data to facilitate the evolution of the technological ecosystem. The system design methodology derived from this research can be applied not only to the educational domain but also to various industrial fields.

With the temporary transition from offline classes to online education due to the COVID-19 pandemic, students’ dissatisfaction has grown as face-to-face interactions have been disrupted. This paper conducts research on the delivery methods of online courses, with a focus on computer programming courses, to provide insights into the design of online courses. The study compares two groups, divided into synchronous and asynchronous delivery, in terms of academic achievement, perception of the delivery method, software attitudes, and satisfaction related to course objectives. Academic achievement utilized data from students’ final grades. In addition, perception, attitude, and satisfaction were surveyed at the beginning and end of the semester using self-reported questionnaires. The analysis results showed that only the perception of the delivery method was statistically significant on average. The purpose of this research is to provide insights for the design of online courses. It is hoped that the findings of this study will be beneficial in making decisions regarding online course design.