• Journal of The Korean Association of Information Education
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• v.11 no.3
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• pp.281-288
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• 2007

Collaborative filtering is a method to predict preference items of a user based on the evaluations of items provided by others with similar preferences. Collaborative filtering helps general people make smart decisions in today's information society where information can be easily accumulated and analyzed. We designed, implemented, and evaluated a course recommendation system experimentally. This system can help university students choose courses they prefer to. Firstly, the system needs to collect the course preferences from students and store in a database. Users showing similar preference patterns are considered into similar groups. We use Pearson correlation as a similarity measure. We select K-nearest students to predict the unknown preferences of the student and provide a ranked list of courses based on the course preferences of K-nearest students. We evaluated the accuracy of the recommendation by computing the mean absolute errors of predictions using a survey on the course preferences of students.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.9-15
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• 2023

Existing simple statistics-based recommendation systems rely solely on students' course enrollment history data, making it difficult to identify classes that match students' preferences. To address this issue, this study proposes a personalized major subject recommendation system based on deep reinforcement learning (DRL). This system gauges the similarity between students based on structured data, such as the student's department, grade level, and course history. Based on this information, it recommends the most suitable major subjects by comprehensively considering information about each available major subject and evaluations of the student's courses. We confirmed that this DRL-based recommendation system provides useful insights for university students while selecting their major subjects, and our simulation results indicate that it outperforms conventional statistics-based recommendation systems by approximately 20%. In light of these results, we propose a new system that offers personalized subject recommendations by incorporating students' course evaluations. This system is expected to assist students significantly in finding major subjects that align with their preferences and academic goals.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.973-976
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• 2004

IT 관련 기술의 발전은 'Any Time, Any Where, Any Service'를 사용자에게 제공할 수 있는 제반 여건을 마련하였다. 기존 웹 기반의 학사정보 시스템에서는 사용자의 이동성이 제한적이었고, 이를 해결하고자 한 무선 인터넷 기반의 학사정보 시스템은 클라이언트의 어플리케이션이 표준화된 환경에서 구축되지 않아서 모바일 기기의 플랫폼에 종속적이었다. 또한, 선택과목이 많은 학부제에서는 코스 코디네이터의 역할이 매우 중요하지만, 코스 코디네이터의 역할을 하는 지도교수와 학생 간의 커뮤니케이션의 부족으로 학생들은 도움을 받기 어렵다. 본 논문에서는 JAVA와 WIPI를 이용하여 플랫폼에 독립적이며 전공분야의 중요과목을 추천해 주는 과목 추천 시스템을 제안한다. 과목 추천 시스템은 학생들에게 수강과목에 대해 조언을 해 주는 코스 코디네이터의 역할을 대신할 수 있을 것이다. 또 학생들은 언제 어디서나 개인 휴대폰을 이용하여 수강신청에 관한 학사정보를 관리할 수 있고, 시스템의 추론에 따른 추천 과목을 수강하여 전공 분야에 대한 깊은 지식을 갖출 수 있을 것이다.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.134-143
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• 2021

Recently, with the development of mobile devices and social media services, contents recommendation schemes have been studied. They are typically applied to the job curation systems. Most existing university education content recommendation schemes only recommend the most frequently taken subjects based on the student's school and major. Therefore, they do not consider the type or field of employment that each student wants. In this paper, we propose a university educational contents recommendation scheme for job curation services. The proposed scheme extracts companies that a user is interested in by analyzing his/her activities in the job curation system. The proposed scheme selects graduates or mentors based on the reliability and similarity of graduates who have been employed at the companies of interest. The proposed scheme recommends customized subjects, comparative subjects, and autonomous activity lists to users through collaborative filtering.

• The KIPS Transactions:PartA
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• v.11A no.4
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• pp.285-296
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• 2004

The rapid progress of IT technologies promoted the foundation to offer users 'Any Time, Any Where, Any Service', and wireless internet services made it possible to use wired internet services while traveling. The previous academic administration management system having migrated from wired to wireless was dependent on mobile equipments' platform because of not being constructed on standard surroundings. And in the aspect of faculty system, course coordinator plays an significant role in building curricula and manage them, and finally counseling students with regard to them. But the course coordinator can't afford to advise students on which fields of their faculty fit them and which courses they have to take. We propose a mobile course coordinator system to help students learn profound courses of their major fields. Also the proposed system is implemented by using JAVA and WIPI technology, so that it is platform-independent. A mobile course coordinator system has an inference engine considering not only course trees which tell informations about the courses in every fields, but also personal courses that students have taken. The inference engine calculates three weights, representing the significance of each course considering every fields, the score of prerequisite courses which a student have taken, and the suitability in which department each student fits. When students apply for taking lectures, a mobile course coordinator system recommends them the most suitable courses. A mobile course coordinator system is able to substitute for the course coordinator who is counseling students. And the students with personal cellular phone are able to keep tracking their courses, and improve their knowledge about major with taking courses which the system's inference engine will advice.

• The Journal of Korean Association of Computer Education
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• v.8 no.5
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• pp.51-62
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• 2005

In the aspect of the faculty, a course coordinator plays an significant role in managing the curriculum and counseling students on academic matters and fostering their progress in the course. However, the course coordinator cannot afford to advise students on which fields of their faculty fit them and which courses they have to take. This paper proposes a mobile course coordinator system to help students learn courses of their major fields deeply. Also the proposed system is implemented by using WIPI technology, so that it is platform-independent and it is able to assist the course coordinator who is counseling students. And the students with personal cellular phones are able to keep tracking their courses, and improve their knowledge about major subjects by taking courses which the system's inference engine will advise.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.532-533
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• 2023

대학 생활을 하면서 자신이 필요한 정보를 모두 챙기기는 쉽지 않다. 매번 학교 홈페이지나 관련 사이트에 접속하여 확인하는 것은 번거롭기도 하고 신입생의 경우에는 그런 정보의 존재조차 잘 모르는 경우가 많다. 때문에 이 논문에서는 웹 크롤링 방식을 통해 다양한 사이트에서 필요한 정보를 수집하고, 기계학습 모델 중 N-GCN을 기반으로 한 추천시스템을 이용하여 본인에게 맞는 추천과목, 동아리 모집공고, 학술대회, 채용공고 등의 정보를 제공해주는 Kingomanager를 소개한다. Kingomanager는 학생들의 학년, 관심분야를 고려해서 개개인별 맞춤 정보를 추천해준다. 추천 받은 정보들은 메신저 형태의 어플리케이션을 통해서 확인할 수 있고, 해당 정보들은 언제든지 다시 검색하여 다시 찾아볼 수 있다. 어플리케이션 구현에서 Front-end는 React-Native를 사용하였고, Back-end는 Flask와 AWS 서비스를 사용하였다. 본 논문에서는 성균관대학교 소프트웨어학과 학생을 대상으로 하는 프로토타입 어플리케이션을 개발했다.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.35-44
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• 2021

Recent advances in the 4th Industrial Revolution have accelerated the change of the working environment, such that the paradigm of education has been shifted in accordance with career education including the free semester system and the high school credit system. While the purpose of those systems is students' self-motivated career exploration, educational limitations for teachers and students exist due to the rapid change of the information on education. Also, education technology research to tackle these limitations is relatively insufficient. To this end, this study first defines three requirements that education technologies for the career education system should consider. Then, through data-driven artificial intelligence technology, this study proposes a data system and an artificial intelligence recommendation model that incorporates the topics for career exploration, courses, and majors in one scheme. Finally, this study demonstrates that the set-based artificial intelligence model shows satisfactory performances on recommending career education contents such as courses and majors, and further confirms that the actual application of this system in the educational field is acceptable.

• Journal of The Korean Association For Science Education
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• v.30 no.6
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• pp.785-798
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• 2010

The purposes of this study are to investigate Korean science teachers' perception of the current science teacher preparation courses in Korea, especially focused on subjects of science education, and to induce implications for improvement of in-service program for science teachers. To do this, a questionnaire was developed by the authors and administered to the 215 science teachers sampled nationwide. The study concluded that science teachers perceived that the two compulsory subjects, 'science education theories' and 'science teaching-learning materials and teaching methods' were not enough for a professional science teacher. Particularly, they consisently insisted that more practices under the relationship with teaching science in schools were necessary when learning subjects of science education. Based on science teachers' response, we recommended that the following 4 subjects should be added in the course of pre-service program for science teachers: 'Development of experiment/demonstration devices', 'Teaching creativity and education for the gifted in science', 'Development of science teaching materials', and 'Science inquiry learning and teaching'.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.163-166
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• 2017

본 논문에서는 학생들에게 자신의 진로결정에 도움이 될 수 있는 비교과 및 교과 정보 제공 시스템을 제안한다. 이는 교수들의 학생 수강지도에 활용되어 정확한 진로 지도에 도움을 줄 수 있다. 이러한 시스템을 구현하기 위하여, 온톨로지 기반 지식베이스를 구축한다. 온톨로지 지식베이스는 강의, 역량, 능력단위, 직무, 기업 정보로 구성이 되어있으며 유지보수가 쉬운 구조로 설계하였다. 또한 온톨로지 지식베이스가 가진 정보로 새로운 지식들을 추론한다. 이 추론 결과를 웹 인터페이스를 활용해, 사용자가 개념들 간의 관계를 파악하고 자신에게 맞는 과목 및 직무를 추천받을 수 있도록 한다.