메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
임연정 (단국대학교)
저널정보
중앙대학교 외국학연구소 외국학연구 외국학연구 제61호
발행연도
2022.9
수록면
223 - 250 (28page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
This study was conducted with the aim of exploring the direction of Chinese education in the post-COVID19 era through learner perception analysis. Due to COVID19, online education such as recorded video lectures and Zoom real-time lectures has rapidly spread. Experts also predict that a mix of non-face-to-face (online) and face-to-face (offline) education methods such as blended learning and flipped learning will become the center of university education in the future. However, before selecting an educational method, the process of analyzing the perception of learners, which is the center of education, needs to be preceded. Therefore, this researcher analyzed learners' perceptions of the beginner-level Chinese education method. A study was conducted on students who took the same Chinese subject as a Zoom real-time lecture in the second semester of 2021 and a classroom face-to-face lecture in the first semester of 2022. Satisfaction with non-face-to-face lectures and face-to-face lectures was compared and analyzed, and the thoughts of students who took most lectures online after entering university were considered. As a result of the analysis, satisfaction with face-to-face lectures was higher than that of non-face-to-face lectures. In addition, it was confirmed that the online lecture experience had an effect on satisfaction with face-to-face lectures and preference for lecture methods. It was also found that the most attention should be paid to feedback regardless of the future lecture method. It is expected that this study will be used as basic data for research and education design related to Chinese education in the post-COVID19 era.

목차

등록된 정보가 없습니다.

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0