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김예술,박시온,박건웅,Kim, Yesool,Park, Sion,Park, Gunwoong 한국통계학회 2020 응용통계연구 Vol.33 No.6
Seoul public bike program facilitates access to bicycles and offers potential for greater mobility and health for users. Furthermore, it would have positive impacts on transport congestion, energy use, and the environment. Hence, it is important to find future rental locations by taking to account both bike-demand and regional imbalance. This paper first finds eligible candidates of rental locations with the required spatial conditions such as a sufficient sidewalk width and accessibility of bike pick-up vehicles. And then, estimates public bike daily usage for each selected location via random forest based on Seoul public bike historical usage, Seoul geographical features, regional characteristics, and populations. This study contributes to a better comprehension of the Seoul public bike program, and would be useful in determining new public bike rental locations. 서울시는 시민의 건강 증진과 이산화탄소 저감을 통한 저탄소 녹색성장 실현을 목표로 2015년부터 2020년 현재까지 공공자전거 대여소를 확장하고 있다. 매년 공공자전거에 대한 시민들의 접근성과 이용률이 증가하고 있으며, 이에 서울시는 수요와 접근성을 모두 고려한 공공자전거 대여소 신규 입지를 확장하고자 노력하고 있다. 공공자전거 대여소 위치는 주변 지형지물에 영향을 받으며, 수요량은 지역적 특성에 영향을 받으므로 이들을 고려한 신규 대여소 입지를 선정해야 할 필요성이 있다. 따라서 본 연구는 서울시 공공자전거의 새로운 입지 선정을 위하여 2019년 서울시 공공자전거 데이터와 지리정보체계, 대중교통, 인구 등의 데이터를 전처리하여 신규 대여소 거치가 가능한 장소를 선별하고, 랜덤 포레스트를 이용하여 신규 대여소의 이용량을 예측하였다. 이를 바탕으로 평균 경사도, 대중교통과의 거리, 특화 시설과의 거리, 하천과의 거리 등이 자전거 이용량에 영향을 미치는 주요한 요소임을 도출할 수 있었다. 본 결과는 신규 대여소 설치 지역을 결정하는데 객관적인 통계적 지표가 될 것으로 기대한다.
대학도서관 인공지능 관련 교육콘텐츠 추천 시스템 사용의도에 관한 연구 - 대학생과 사서의 인식을 중심으로 -
김성훈,박시온,박지원,오유진,Kim, Seonghun,Park, Sion,Parkk, Jiwon,Oh, Youjin 한국도서관정보학회 2022 한국도서관정보학회지 Vol.53 No.1
The understanding and capability to utilize artificial intelligence (AI) incorporated technology has become a required basic skillset for the people living in today's information age, and various members of the university have also increasingly become aware of the need for AI education. Amidst such shifting societal demands, both domestic and international university libraries have recognized the users' need for educational content centered on AI, but a user-centered service that aims to provide personalized recommendations of digital AI educational content is yet to become available. It is critical while the demand for AI education amongst university students is progressively growing that university libraries acquire a clear understanding of user intention towards an AI educational content recommender system and the potential factors contributing to its success. This study intended to ascertain the factors affecting acceptance of such system, using the Extended Technology Acceptance Model with added variables - innovativeness, self-efficacy, social influence, system quality and task-technology fit - in addition to perceived usefulness, perceived ease of use, and intention to use. Quantitative research was conducted via online research surveys for university students, and quantitative research was conducted through written interviews of university librarians. Results show that all groups, regardless of gender, year, or major, have the intention to use the AI-related Educational Content Recommendation System, with the task suitability factor being the most dominant variant to affect use intention. University librarians have also expressed agreement about the necessity of the recommendation system, and presented budget and content quality issues as realistic restrictions of the aforementioned system.
Lexical Frames in L2 Writing Development: A Longitudinal Study
신유경(Yu Kyoung Shin),박시온(Sion Park) 한국영어교과교육학회 2020 영어교과교육 Vol.19 No.4
Lexical frames are fixed, high-frequency sequences of function words, with free slots filled by lexical words (e.g., the * of the, in the * of). Most existing research on lexical frames focuses on the lexical words as the variable component, rather than on the function words; very little research has considered English learners’ grammatical errors with these “fixed” elements of lexical frames. This study investigates one EFL learner’s use of and accuracy with recurrent word sequences and lexical frames in terms of English articles over time, using longitudinal data of the academic texts from her high school and online university classes. The findings show that the learner has come to use more types of lexical frames, which typical of academic prose. In addition, the learner made more article errors in lexical frames in high school, which suggests the need for research to take account of such errors, rather than treating function words in L2 lexical frames as fixed. The study concludes with a discussion of pedagogical implications, including the effects of the shift from face-to-face to online courses at the university level.