• 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.23 no.2
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• pp.1-11
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• 2020

As software and artificial intelligence education became more and more important, in December 2019, the Ministry of Science and ICT announced plans to expand software and AI education to mandatory education in elementary and secondary schools by 2022. In addition to elementary and secondary schools, most universities are actively engaged in software education for computer non-majors, but research on coding education for computer non-majors is insufficient. The purpose of this paper is to find an efficient teaching and learning method for coding education for computer non-majors. Nowadays, college students, called Millennial and Generation Z, prefer visual information and are familiar with computers as digital natives. Based on these characteristics, this study examined the visual literacy and thinking styles of college students and then examined whether the students' visual literacy and thinking styles influenced coding-based problem solving in coding subjects. Based on this, this paper proposes an alternative to do programming education more efficiently for students who are new to coding.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.901-910
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• 2023

In universities, programming language-based thinking and software education for non-majors are being implemented to cultivate creative and convergent talent capable of leading the digital convergence era in line with the Fourth Industrial Revolution. However, learners face difficulties in acquiring the unfamiliar syntax and programming languages. The purpose of this study is to propose a software education model to alleviate the challenges faced by non-major students during the learning process. By introducing algorithm techniques and diagram techniques based on programming language thinking and using the EZMKER kit as an instructional model, this study aims to overcome the lack of learning about programming languages and syntax. Consequently, a structured software education model has been designed and implemented as a top-down system learning model.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.81-90
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• 2019

Since 2015, the government has been striving to strengthen the software capabilities required for future talent through software-oriented university in Korea. In the university selected as a software-oriented university, basic software education is given to all departments such as humanities, social science, engineering, natural science, arts and the sports within the university in order to foster convergent human resources with different knowledge and software literacy. In this paper, we analyze the contents of basic software education for twenty universities selected as software-oriented universities. As a result of analysis, most of the basic software education which is carried out to the students of the non-majors students was aimed at improvement of problem solving ability centered on computational thinking for future society and improvement of convergence ability based on computer science. It uses block-based educational programming language and text-based advanced programming language to adjust the difficulty of programming contents and contents reflecting characteristics of each major. Problem-based learning, project-based learning, and discussion method were used as the teaching and learning methods for problem solving. In the future, this paper will help to establish the systematic direction for basic software education of non-majors students.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.447-448
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• 2023

본 연구에서는 비전공자들을 위한 디지털 교육을 위하여 노 코드 프로그램을 활용한 정보, 수학, 과학 융합교육 교양 강좌를 개발하였다. 노 코드 프로그램으로는 오렌지3 데이터 마이닝을 선정하였는데, 이는 데이터 분석, 시각화, 머신러닝 모델의 활용이 용이하다는 강점을 가지고 있다. 또한, 산업환경 변화에 대비하는 핵심 교과인 과학, 수학, 정보의 중요성과 데이터 분석과의 밀접성을 고려하여 교육 내용을 융합할 수 있도록 선정하였다. 개발된 교육 프로그램은 8인이 전문가 검토 결과 내용 타당도가 확보되었음을 확인할 수 있었다. 추후 연구에서는 이 강좌를 대학의 학부생에게 적용하여 그 효과성을 확인해 보고자 한다.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.899-906
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• 2021

Data science is a discipline comprised of the academic fields of statistics, computer science, information technology, and domain knowledge. It analyzes data and derives meaningful results using complex technologies. Data science, along with artificial intelligence, is a core technology of the 4th industrial revolution; consequently, universities and companies worldwide are actively developing programs to develop data scientists who require high levels of expertise. In line with this undertaking, the field of elementary education has recognized the importance of data science education and so various studies have been conducted to develop curricula designed to help students understand how to use data. This paper proposes a curriculum for the purpose of educating elementary school teachers who are mostly non-majors in the computer field about data science. Satisfaction analysis was conducted based on questionnaires collected from students to analyze the effectiveness of the data science education proposed in this paper.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.393-400
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• 2021

Data science is a scientific discipline that defines problems while finding meaningful information from collected data to solve problems. Along with artificial intelligence technology, the field of data utilization is gradually expanding, and awareness of the importance of data science education is also increasing. Despite the rapid growth of the domestic data industry market, it has recently been predicted that the shortfall of data experts will reach 31.4% within the next 5 years according to an analysis of the current status of the data industry by the Korea Data Agency. In the field of elementary education, various studies have been conducted to introduce data science in order to improve students' computational thinking and creativity. This paper proposed the contents of data science lectures developed for the purpose of educating elementary school teachers, who are mostly non-majors in the computer field. The developed contents were applied to a group of elementary school teachers attending graduate school for artificial intelligence convergence education. Points for improvement were derived by identifying the contents that were difficult for learners to understand and analyzing the causes of difficulty.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1502-1504
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• 2011

최근 소셜 네트워크를 이용한 학습 방법이 활발하게 연구되고 있다. 동일한 영역에 지식을 공유하고 새로운 정보를 웹에서 생성하는 등 집단지성 학습자들이 계속적으로 늘어가고 있으며, 특히 IT 학습을 위한 온라인기반 교육은 관심도와 직군에 따른 집단지성을 통한 효과적인 학습 성취도를 높일 수 있다. 따라서 본 연구는 웹 기반을 중심으로 한 IT집단지성 학습자들의 개인차를 파악하고, 각 특성에 따른 다양성을 적용하여 학습과정 중 자신에게 적합한 학습경로를 파악하여 학습의 지속적인 진행에 있어 정확한 자료를 제공하고 효율적인 학습의 진행이 이루어져 성취도를 높일 수 있도록 학습 성취도 예측 시스템을 연구 개발하는 데 그 목적이 있다. 이를 위해 먼저 IT습자들이 자신의 개인차를 파악하기 위해 학습의 유형, 학습몰입, 인지적 능력, 개인적 성향, 창의적 성향 등을 활용한 검사도구의 개발이 선행되어야 한다. 다음으로, IT 전공자 혹은 비전공자를 대상으로 예비조사를 실시하고 그 결과를 바탕으로 학습 성취도 예측을 가능하게 하기위한 시스템을 설계하고자 한다. 향후 본 연구의 결과로 학습자의 학습 성취도를 향상시키고, 예측 결과에 의한 집단지성 그룹을 좀 더 효과적으로 운영 할 수 있는 시스템을 구축할 수 있을 것으로 기대한다.

• Journal of The Korean Association of Information Education
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• v.24 no.2
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• pp.201-214
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• 2020

Big data is gradually expanding in diverse fields, with changing the data-related legislation. Moreover it would be interest in big data education. However, it requires a high level of knowledge and skills in order to utilize Big Data and it takes a long time for education spends a lot of money for training. We study that in order to define Universal Big Data used to the industrial field in a wide range. As a result, we make the paradigm for Big Data education for college students. We survey to the professional the Big Data definition and the Big Data perception. According to the survey, the Big Data related-professional recognize that is a wider definition than Computer Science Big Data is. Also they recognize that the Big Data Processing dose not be required Big Data Processing Frameworks or High Performance Computers. This means that in order to educate Big Data, it is necessary to focus on the analysis methods and application methods of Universal Big Data rather than computer science (Engineering) knowledge and skills. Based on the our research, we propose the Universal Big Data education on the new paradigm.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.423-436
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• 2021

In the future, artificial intelligence (AI) technology is expected to become a general-purpose technology (GPT), and it is predicted that AI competency will become an essential competency. Several nations around the world are fostering experts in the field of AI to achieve technological proficiency while working to develop the necessary infrastructure and educational environment. In this study, we investigated the status of software education at the liberal arts level at 31 universities in Seoul, along with precedents from domestic and foreign AI education research. Based on this, we concluded that an AI literacy education model is needed to link software education at the liberal arts level with professional AI education. And we classified 20 AI-related lectures released in the KOCW according to the AI literacy competencies required; based on the results of this classification, we propose a model for AI literacy education in the liberal arts for undergraduate students. The proposed AI literacy education model may be considered as AI·SW convergence to experience AI along with literacy in the humanities, deviating from the existing theoretical and computer-science-based approach. We expect that our proposed AI literacy education model can contribute to the proliferation of AI.