Abstract
Background/Objectives: In the 21st century, communication and collaboration between people is an important element of talent. As artificial intelligence (AI), the cutting edge of computer science, develops, AI and collaboration will become important in the near future.
 Methods/Statistical analysis: To achieve this, it is necessary to understand how artificial AI based on computer science works, and how problem-based programming education is effective in computer science education. In this study, 177 college students who received programming education focused on problem-solving learning were identified with computational thinking (CT) at the beginning of the semester, and their satisfaction and post-education satisfaction survey showed that their attitudes and interests influenced their education.
 Findings: To pretest the learners, they were diagnosed using a measurement sheet. The learners’ current knowledge statuses were checked, and the correlation between the evaluation results, based on what was taught according to the problem-solving learning technique, was analyzed according to the proposed method. The analysis of the group average score of the learners showed that the learning effect was significant. The results of the measures of the students’ CT at the beginning of the semester were correlated with problem-solving learning, teaching method, lecture satisfaction, and other environmental factors. The ability to solve a variety of problems using CT will become increasingly important, so if students seek to improve their satisfaction with problem-solving learning techniques for computer science education, it will be possible for universities to develop convergence talent more efficiently.
 Improvements/Applications: if you pursue a problem-solving learning technique and a way to improve students’ satisfaction, it will help students improve their problem-solving skills. If the method of deriving and improving computational thinking ability in this paper is applied to computer education, it will induce student interest, thereby increasing the learning effect.
Highlights
Efforts to create new values by integrating computing technologies with various fields have been emphasized in modern society
Existing studies show that substantial research related to computational thinking (CT) and software education has been conducted [7, 8].Analysis confirms that while Python text coding language is highly applicable in CT, non-computer major students find it difficult to operate and serves as an entry barrier to programming education
We found that problem-solving learning extracted by analyzing CT ability and learner’s attitude through problem-solving learning based software education can raise students’ interest and improve their CT in order to facilitate software education in universities [1, 9, 10]
Summary
Efforts to create new values by integrating computing technologies with various fields have been emphasized in modern society. CT ability for this needs to be computer-based thinking, so it can be developed and improved through software education based on educational programming language [1, 2].In reality, non-computer-science majors students are unfamiliar with computers and have difficulty grasping software education. The basic goal of software education is to design and complete thoughts on a computer, resolve problems in the process of solving a given problem, and repeat and combine the process of finding a solution. Various abilities such as logical and/or procedural thinking ability for programming, information processing using computers, and problem-solving ability are improved. We will analyze how attitudes and interests of students are related to actual CT ability and analyze the relationship between satisfaction of the problem-solving learning process and CT ability and use it in various applications of future problem-solving learning
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