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Analysis of ‘Better Class’ Characteristics and Patterns from College Lecture Evaluation by Longitudinal Big Data

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초록

The purpose of this study was to analyze characteristics and patterns of ‘better class’ by using the longitudinal text mining big data analysis technique from subjective lecture evaluation comments. First, this study classified upper 30% classes to deduce certain characteristics and patterns from every five-year subjective text data for 10 years. A total of 47,177courses (100%) from spring semester 2005 to fall semester 2014 were analyzed from a university at a metropolitan city in the mid area of South Korea. This study extracted meaningful words such as good, course, professor, appreciation, lecture, interesting, useful, know, easy, improvement, progress, teaching material, passion, and concern from the order of frequency 2005-2009. The other set of words were class, appreciation, professor, good, course, interesting, understanding, useful, help, student, effort, thinking, not difficult, explanation, lecture, hard, pleasant, easy, study, examination, like, various, fun, and knowledge 2010-2014. This study suggests that the characteristics and patterns of ‘better class’ at college, should be analyzed according to different academic code such as liberal arts, fine arts, social science, engineering, math and science, and etc.

Abstract

The purpose of this study was to analyze characteristics and patterns of ‘better class’ by using the longitudinal text mining big data analysis technique from subjective lecture evaluation comments. First, this study classified upper 30% classes to deduce certain characteristics and patterns from every five-year subjective text data for 10 years. A total of 47,177courses (100%) from spring semester 2005 to fall semester 2014 were analyzed from a university at a metropolitan city in the mid area of South Korea. This study extracted meaningful words such as good, course, professor, appreciation, lecture, interesting, useful, know, easy, improvement, progress, teaching material, passion, and concern from the order of frequency 2005-2009. The other set of words were class, appreciation, professor, good, course, interesting, understanding, useful, help, student, effort, thinking, not difficult, explanation, lecture, hard, pleasant, easy, study, examination, like, various, fun, and knowledge 2010-2014. This study suggests that the characteristics and patterns of ‘better class’ at college, should be analyzed according to different academic code such as liberal arts, fine arts, social science, engineering, math and science, and etc.

참고문헌 (11)

  1. Y. J. Jang, "Analysis of the Faculty and Student's Perspectives on Student Course Evaluation", 석사, pp.2012
  2. 민혜리, "대학교육의 질 향상을 위한 좋은 강의의 기준 탐구", 교육정보미디어연구, 11(3), pp.41~65 (2005), 한국교육정보미디어학회
  3. 한신일, "한국대학의 강의평가실태 분석", 교육행정학연구, 23(3), pp.379~404 (2005), 한국교육행정학회
  4. 남민우, "대학 강의평가에서 수강인원에 따른 차이 검증 및 가중치 적용 방안 연구", 학습과학연구, 8(2), pp.153~168 (2014), 교육공학연구소
  5. 이해듬, "대학 강의평가 주관식 결과의 텍스트마이닝을 통한 전공 계열별 좋은 수업 특성 분석", 한국유아교육연구, 20(2), pp.21~41 (2018), 유아교육연구소[DOI]
  6. 백은주, "누리과정 내용의 PBL ‘유아과학교육’ 수업에 참여한 예비유아교사의 과학교수 효능감 및 태도", 어린이미디어연구, 15(2), pp.1~20 (2016), 한국어린이미디어학회[DOI]
  7. 이슬기, "작문 평가에서 텍스트 마이닝의 활용 가능성 탐색", 작문연구, (35), pp.99~131 (2017), 한국작문학회[DOI]
  8. J. H. Leem, "Teaching & Learning Strategies for increasing the effectiveness of instruction using ICT", Journal of elementary education studies, 9(2), pp.19~42 (2002)
  9. Edunet Policy Research, "Korea Education and Research Information Service", ~ (2002)
  10. "Morphological analysis and POS tagging" (2014)[URL]
  11. Korean Educational Development Institute, "Best lectures: SBS TV special program about 100 famous college lectures" (2014), Hakjisa

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