• 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.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.223-231
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• 2021

Humanity faces the 4th industrial revolution, many countries are providing systematic and continuous SW education to foster talent with AI capabilities. On the other hand, there are high concerns that the SW gap will widen and polarize as domestic reality does not provide systematic and continuous SW education to learners. Against this background, this study started to investigate the influence of learners' SW experience on SW education. As a result of the study targeting science and engineering students, the effect of SW experience on SW education was significant. With theses results, it is expected that various discussions will be actively conducted so that systematic and continuous SW education can be provided in elementary and secondary schools.

• 한국정보교육학회:학술대회논문집
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• 2009.08a
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• pp.79-84
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• 2009

세계의 모든 나라들이 미래의 IT강국을 꿈꾸며, 기술개발과 신제품 생산에 국가에 운명을 걸고 있고, 장기적인 안목에서 볼 때 이러한 시대적 상황을 선도할 수 있는 힘은 결국 교육에서 나온다. 정보가 최고의 가치로 바뀌었고, 삶의 질이 지식.정보에 의해 좌우되는 사회에서 정보화 교육이 절실히 요구된다. 하지만 그에 따라 학생들에게 다양한 정보소양능력을 길러 줄 수 있는 자료가 부족한 실정이다. 본 연구를 통하여 학생들이 컴퓨터와 관련된 인물을 통하여, 컴퓨터가 어떻게 발전해 왔는지를 살펴보고, 정보통신에 대한 전반적인 교양수준을 넓히게 하기 위하여 다음과 같은 자료를 개발 하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.717-718
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• 2022

본 연구에서는 예비교사의 인공지능 융합교육 역량 강화를 위한 캡스톤 디자인 기법 활용 인공지능 융합교육 프로그램을 개발하고 효과를 검증하였다. 개발된 교육 프로그램은 예비교사들이 스크래치 프로그래밍과 머신러닝포키즈, 캡스톤 디자인의 이해를 바탕으로, 인공지능 활용 융합 수업을 위한 주제 선정, 수업 설계 및 개발 후, 마이크로티칭을 하고 동료 평가 및 피드백을 하도록 조직되었다. 이는 2022년 1학기 K대학의 교양 강좌를 수강하는 예비교사들에게 처치되었다. 그 결과, 실험 대상자들의 인공지능 교수효능감의 사전-사후 t-검정에서 통계적으로 유의한 효과가 있음을 확인되었다.

• Journal of The Korean Association of Information Education
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• v.17 no.2
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• pp.157-166
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• 2013

In order to study on improvement of a composition of the computer education curriculum for the universities of education, we analyzed the operating situation of the computer education curriculums of the 10 universities across the country by dividing the curriculums into 3 types, such as a general education course, specialization course and advanced course. In addition, we did a survey on the information utilization abilities of the first year students of Jeonju National University of Education. The suggestions for designing effective computer education in the curriculum of the universities of education were proposed.

• Proceedings of the Korean Society for Information Management Conference
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• 2018.08a
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• pp.21-24
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• 2018

본 연구는 대학도서관 사서가 지각하는 근거기반실무 지식의 정도와 영향요인을 식별하고자 하는 연구이다. 근거기반실무 지식에 유의한 차이가 나타난 변인은 연령, 교육 정도, 고용 유형, 직위, 학술지 읽기, 학술대회 참여이었다. 근거기반실무 지식에 영향을 미치는 요인은 교육 정도, 학술지 읽기로 나타났다. 본 연구는 대학도서관 사서의 근거기반실무 지식은 대학도서관과 사서직의 지속적 발전과 혁신의 촉진 요인이기 때문에 근거기반실무 역량을 향상시킬 수 있도록 연구 관련 활동을 지원하는 조직단위, 도서관계, 학계 차원의 지원과 정책이 필요하다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.259-267
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• 2020

The purpose of this study is to analyze the effects of computational thinking class using ARCS model to increase students' learning motivation. Then, this study designed the detailed instruction strategy according to each motivation factor(Attention, Relevance, Confidence, Satisfaction) of ARCS through the previous study on computational thinking education and ARCS model. The results of the ARCS test were compared between the experimental group to which the ARCS model was applied and the control group to which the general class was conducted. As a result, students in the experimental group showed significantly higher motivation for learning about computational thinking. In particular, the learning motivation of computer-related majors was significantly higher than that of the control group. In addition, majors were found to have high relevance(R) and non-majors had high satisfaction(S). Therefore, based on these findings, this study suggests an improvement for effective computational thinking class in liberal arts education.

• Journal of The Korean Association of Information Education
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• v.17 no.4
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• pp.433-447
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• 2013

The purpose of this study is to find out the factors of the students' achievement on the computer training courses which are based on computer practice. In order to improve the academic achievement of the students, it is necessary to analyze the factors affecting academic achievement and apply the results of the analysis to education. In particular, it is necessary to study for finding out factors of the academic achievement in practical computer training courses, because these courses are different from other courses focusing on the theory. In this study, in order to find out the factors, the logistic regression analysis and the decision tree analysis which is the field of data mining were peformed. For the experimental data, the test results of the MOS certification of the S university in seoul were used. Through logistic regression analysis it is found that the factors of the professors, class size, lecture time, group(lecture period) are important in order. Through decision tree analysis of data mining, it is found that there are some additional factors ; entrance year, whether the course is retaken, and the classroom environment. and these various factors effect the academic achievement compositively as identified through the model tree. The tree model was presented as a result of the analysis, and the importance of the factors is expressed numerically from multiple tree models by using the proposed mathematical formula.

• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.947-960
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• 2021

In this study, we examine the AI ethics perception of university students to explore the direction of AI ethics education. For this, 83 students wrote their thoughts about 5 discussion topics on online bulletin board. We analyzed it using language networks, one of the text mining techniques. As a result, 62.5% of students spoke the future of the AI society positively. Second, if there is a self-driving car accident, 39.2% of students thought it is the vehicle owner's responsibility at the current level of autonomous driving. Third, invasion of privacy, abuse of technology, and unbalanced information acquisition were cited as dysfunctions of the development of AI. It was mentioned that ethical education for both AI users and developers is required as a way to minimize malfunctions, and institutional preparations should be carried out in parallel. Fourth, only 19.2% of students showed a positive opinion about a society where face recognition technology is universal. Finally, there was a common opinion that when collecting data including personal information, only the part with the consent should be used. Regarding the use of AI without moral standards, they emphasized the ethical literacy of both users and developers. This study is meaningful in that it provides information necessary to design the contents of artificial intelligence ethics education in liberal arts education.

• The Journal of Information Systems
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• v.30 no.3
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• pp.177-199
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• 2021

Purpose This study explores the development direction of the artificial intelligence curriculum as a universal education that enhances the ability of college students to flexibly use artificial intelligence these days, where artificial intelligence education is spreading, and the educational components based on this are subdivided according to the characteristics of each major. Design/methodology/approach In order to develop the educational purpose of the subject and the detailed educational curriculum suitable for the subject of education, we first analyzed domestic and foreign prior research related to artificial intelligence liberal arts education. As the main components derived by experts, the basic concept of artificial intelligence converges to literacy to read and write for everyday problem solving, as well as problem-solving ability to manipulate real data and software. Findings The results showed that In the artificial intelligence literacy module, trends and prospects of artificial intelligence and necessary competencies were checked, and cases applied to major fields were examined. In the AI utilization and application part, basic data analysis items and content composition were composed through creative thinking, logical thinking, and intelligence. In order to design the curriculum, a software development language suitable for each major area was first selected, and AI education content areas, elements, and packages were defined and designed for each major area to meet the objectives of the subject.