• The Journal of Korean Association of Computer Education
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• v.20 no.1
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• pp.1-11
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• 2017

As an emphasis has been put on the importance of computational thinking, universities have opened software educational programs as required basic courses.. Therefore this study aimed to examine non-major students' perceptions of programming before and after they had programing education. To this end, this study performed programming education for 15 weeks using the Scratch programming language, and then conducted a questionnaire survey. This study analyzed responses from 214 students. According to the results of the analysis, 74 % of the non-major students had no previous experience with programming, 87% felt that programming was difficult, and 69.7% answered that they did not need programming education. To change these negative perceptions of programming, this study made the following suggestions. First, the professor should clearly convey the needs, purposes, and content of programming education to students prior to class. Second, programming should be designated as an optional course rather than required one. Third, it is necessary to develop content integrated with majors, or educational programs or content connected to getting a job or starting a business.

• The Journal of Industrial Distribution & Business
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• v.10 no.11
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• pp.71-80
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• 2019

Purpose: The purpose of this study is to design the curriculum of Basic College Software Programming to develop creative and logical-thinking. This course is guided by algorithmic thinking and logical thinking that can be solved by computing for problem-solving, and it helps to develop by software through basic programming education. Through the stage of problem analysis, abstraction, algorithm, data structure, and algorithm implementation, the curriculum is designed to help learners experience algorithm problem-solving in various areas to develop diffusion thinking. For Learners aim to achieve the balanced development of divergent and convergent-thinking needed in their creative problem-solving skills. Research design, data and methodology: This study is to design a basic software education for improving algorithm-thinking for non-major. The curriculum designed in this paper is necessary to non-majors students who have completed the 'Creative Thinking and Coding Course' Design Thinking based are targeted. For this, contents were extracted through advanced research analysis at home and abroad, and experts in computer education, computer engineering, SW education, and education were surveyed in the form of quasi-openness. Results: In this study, based on ADD Thinking's algorithm thinking, we divided the unit college majors into five groups so that students of each major could accomplish the goal of "the ability to internalize their own ideas into computing," and extracted and designed different content areas, content elements and sub-components from each group. Through three expert surveys, we established a strategy for characterization by demand analysis and major/textbook category and verified the appropriateness of the design direction to ensure that the subjects and contents of the curriculum are appropriate for each family in order to improve algorithm-thinking. Conclusions: This study helps develop software by enhancing the ability of students who practice various subjects and exercises to explore creative expressions in various areas, such as 'how to think like a computer' that can implement and execute their ideas in computing. And it helps increase the ability to think logical and algorithmic computing based on creative solutions, improving problem-solving ability based on computing thinking and fundamental understanding of computer coding and development of logical thinking ability through programming.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.31-38
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• 2023

In this paper, a basic machine learning model learning and utilization education curriculum for non-majors is proposed, and an education method using Orange machine learning model learning and analysis tools is proposed. Orange is an open-source machine learning and data visualization tool that can create machine learning models by learning data using visual widgets without complex programming. Orange is a platform that is widely used by non-major undergraduates to expert groups. In this paper, a basic machine learning model learning and utilization education curriculum and weekly practice contents for one semester are proposed. In addition, in order to demonstrate the reality of practice contents for machine learning model learning and utilization, we used the Orange tool to learn machine learning models from categorical data samples and numerical data samples, and utilized the models. Thus, use cases for predicting the outcome of the population were proposed. Finally, the educational satisfaction of this curriculum is surveyed and analyzed for non-majors.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.186-191
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• 2020

Artificial Intelligence (AI) technology is used in a variety of fields because it can make inferences and plans through learning processes. In the field of coding technologies, AI has been introduced as a tool for personalized and customized education to provide new educational environments. Also, it can be used as a virtual assistant in coding operations for easier and more efficient coding. Currently, as coding education becomes mandatory around the world, students' interest in programming is heightened. The purpose of coding education is to develop the ability to solve problems and fuse different academic fields through computational thinking and creative thinking to cultivate talented persons who can adapt well to the Fourth Industrial Revolution era. However, new non-computer science major students who take software-related subjects as compulsory liberal arts subjects at university came to experience many difficulties in these subjects, which they are experiencing for the first time. AI based coding technologies can be used to solve their difficulties and to increase the learning effect of non-computer majors who come across software for the first time. Therefore, this study examines the current state of AI based coding technologies and suggests the direction of future coding education.

• The Journal of Korean Association of Computer Education
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• v.23 no.2
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• pp.41-51
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• 2020

The goal of this study is to test the hypothesis that the practicality of EXCEL VBA will be beneficial for SW education for SW non-majors. To this end, we planned the education for non-majors using the EXCEL VBA and conducted 15 weeks of education. According to a follow-up survey conducted after the 15-week education period, 72.21% of the survey respondents said EXCEL was practical. Also, learners who were aware of the necessity of SW education and the importance of SW competence recognized that the computational thinking education using EXCEL VBA had a positive effect on the improvement of computer-related knowledge and experience. Also, learners recognized that learning with EXCEL was easy, while learning with VBA was difficult. The learning process using VBA needs to be composed of project-oriented educational contents that can give a sense of achievement to learners rather than programming-oriented education. And continuous research on project-based learning is needed.

• Proceedings of The KACE
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• 2018.08a
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• pp.121-124
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• 2018

본 논문에서는 비전공자 대상의 학습자 눈높이에서 흥미와 재미를 유발하고, 실제 비전공자의 컴퓨팅사고력(Computational Thinking)을 향상시키는데 콘텐츠에 따라 학생의 관심도가 달라질 수 있다는 분석결과를 보여준다. 기존의 SW교육 시스템(EPL: Education Programming Language)의 단점인 실행 중심의 SW교육 시스템에서 벗어나 실제 생활에서의 기초적인 컴퓨팅 사고 관련 예제로서 모델별 단계적 접근을 시도하면서, 실제 생활 속 컴퓨팅 사고력 기반의 문제해결력을 높일 수 있도록 피드백 방식이 비전공자 SW교육에 끼치는 영향력을 확인하였다.

• The Journal of Korean Association of Computer Education
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• v.20 no.2
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• pp.1-11
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• 2017

The demands of computer science education for non-majors in higher education is increasing but relevant evaluation tools for the students' computational artifacts are lack. This research aims to develop a scoring rubric to assess student's computational artifacts in non-major programming course at Computational Thinking point of view. The rubric was developed based on 'CT Practice Design Pattern' as a framework. The rubric consists of 'domain, skills, evaluation, evaluating resources, and scales'. Domains are 'Design of abstract model', 'Design and application of creative artifacts', and 'Analysis of the artifacts'. Experts reviewed the rubric to ensure contents validity. The rubric is resulted in reliable for consistency. This rubric can be revised and applied to application environment accordingly.

• The Journal of Korean Association of Computer Education
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• v.18 no.3
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• pp.49-57
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• 2015

The purpose of this study is to provide considerations through investigation and analysis about non-computer major learners' difficulties in computational thinking education. In recent, the importance of human resources development in convergence based on computational thinking is increasing internationally and a Korean university is selecting CT as a mandatory subject. I taught CT with Scratch at C university in Seoul for two semesters in 2014 and investigated and analyzed what difficulties non-Computer majors felt in the process of CT education. The result showed they felt the following some difficulties in order: the concept of variable and list; to think a idea and implement it; which commands should be selected. The pleasure and the interest can be apply to decrease difficulty, because they affect self-programming ability and self-CT capability each other statistically. Although Scratch is an easy and an intuitive programming language, it is needed to consider to provide appropriate learning time to student for using and applying commands.

• Journal of Practical Engineering Education
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• v.12 no.2
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• pp.265-273
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• 2020

In this paper, to design a basic data science practice curriculum as a liberal arts subject for non-majors, we proposed an educational method using an Excel(spreadsheet) data analysis tool. Tools for data collection, data processing, and data analysis include Excel, R, Python, and Structured Query Language (SQL). When it comes to practicing data science, R, Python and SQL need to understand programming languages and data structures together. On the other hand, the Excel tool is a data analysis tool familiar to the general public, and it does not have the burden of learning a programming language. And if you practice basic data science practice with Excel, you have the advantage of being able to concentrate on acquiring data science content. In this paper, a basic data science practice curriculum for one semester and weekly Excel practice contents were proposed. And, to demonstrate the substance of the educational content, examples of Linear Regression Analysis were presented using Excel data analysis tools.

• The Journal of the Convergence on Culture Technology
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• v.6 no.1
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• pp.185-190
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• 2020

The programming education of non-computer major undergraduates aims to increase the their problem-solving and coding skills so that the skills can be applied to various fields and motivate them to continuously study computer or programming. However, it difficult for them to recognize the necessity of programming knowledge and to find out how it can be used in their major. Therefore, the professor needs to give students a full explanation of their roles to play. In this paper, we revisit the necessity of programming knowledge for non-computer major undergraduates by looking at the convergence cases of ICT technology and the humanities and social arts fields. And we propose an instruction direction of programming learning for them.