실천공학교육논문지 (Journal of Practical Engineering Education)

  • 제15권3호
  • /
  • Pages.539-547
  • /
  • 2023
  • /
  • 2288-405X(pISSN)
  • /
  • 2288-4068(eISSN)
한국실천공학교육학회 (Korean Institute for Pratical Engineering Education)
권호 표지
DOI QR코드

DOI QR Code

토픽 모델링을 활용한 대학 강의평가 개방형 응답분석

Open-Ended Response Analysis for University Course Evaluations using Topic Modeling

  • Su-Hyun Ahn (College of General Education, Semyung University) ;
  • Sang-Jun Lee (College of General Education, Semyung University)
  • 투고 : 2023.12.19
  • 심사 : 2023.12.21
  • 발행 : 2023.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

최근 대학교육은 교육 패러다임의 변화와 함께 학습자 중심의 교육 모델을 강조하고 있다. 본 연구는 대학 강의평가의 개방형 응답을 토픽 모델링으로 분석하여 학생들의 다양한 의견을 탐색하고 교육의 질을 향상시키는데 목적이 있다. 이를 위해 비수도권 대학의 2017년부터 2022년까지 강의평가 결과를 바탕으로 총 45,001개의 개방형 응답을 전공과 교양으로 구분하여 단문에 최적화된 BTM(Biterm Topic Modeling) 분석을 진행하였다. 분석 결과 전공 강의는 "비대면 수업경험에 대한 태도", "질문과 토론에 대한 태도", "출석과 성적평가에 대한 태도", "실습활동 및 발표에 대한 태도", "소통과 협업에 대한 태도"로 구분되었고, 교양 강의는 "비대면 수업경험에 대한 태도", "성적 및 평가에 대한 태도", "출석 및 수업계획서에 대한 태도", "학문적 지식과 흥미에 대한 태도", "소통과 질문에 대한 태도"로 구분되었다. 학생들의 다양한 피드백을 분석한 본 연구결과를 토대로 전공과 교양 강의의 특성을 비교하고 강의개선 및 학습경험을 향상시키는데 도움이 될 수 있는 인사이트를 얻을 수 있을 것이다.

In recent years, university education has emphasized a learner-centered education model with a change in educational paradigm. This study aims to explore students' diverse opinions and improve the quality of education by analyzing the open-ended responses of university lecture evaluations using topic modeling. To this end, a total of 45,001 open-ended responses based on the results of lecture evaluations from 2017 to 2022 in non-metropolitan universities were divided into majors and liberal arts, and a short-form optimized Biterm Topic Modeling (BTM) analysis was conducted. As a result of the analysis, major lectures were divided into "attitude toward non-face-to-face classroom experience", "attitude toward questions and discussions", "attitude toward attendance and grading", "attitude toward practical activities and presentations", and "attitude toward communication and collaboration", while liberal arts lectures were divided into "attitude toward non-face-to-face classroom experience", "attitude toward grades and evaluations", "attitude toward attendance and syllabus", "attitude toward academic knowledge and interest", and "attitude toward communication and questions". The results of this study, which analyzed various feedback from students, provide insights that can be used to compare the characteristics of majors and liberal arts courses and improve teaching and learning experiences.

키워드

과제정보

이 논문은 2023학년도 세명대학교 교내학술연구비 지원에 의해 수행된 연구임

참고문헌

  1. Y. S. Ko, "Demand-oriented university structural reform," KDI Focus, vol. 122, pp. 1-12, 2023. 
  2. Y. S. Chang, D. J. Jung, and D. N. Kim, "A study on quality dimension and improvement priority for enhancing university educational service satisfaction," Journal of the Korean Society for Quality Management, vol. 45, no. 1, pp. 11-24, 2017.  https://doi.org/10.7469/JKSQM.2017.45.1.011
  3. K. H. Lee, "A study on validity and reliability of students' evaluation," Journal of the Korean Data and Information Science Society, vol. 21, no. 1, pp. 87-98, 2010. 
  4. K. S. Yang, "Meta-research on the influence of course, instructor, and student characteristics in student evaluation of teaching at universities," The Korean Journal of Educational Methodology Studies, vol. 26, no. 2, pp. 293- 322, 2014.  https://doi.org/10.17927/tkjems.2014.26.2.293
  5. S. Y. Kim, B. K. Kim, E. G. Lim, and Y. K. Lee, "A study of the patterns and characteristics of three-year lecture evaluation results for improving the quality of teaching," The Journal of Educational Studies, vol. 53, no. 2, pp. 1-26, 2022.  https://doi.org/10.15854/jes.2022.06.53.2.1
  6. M. H. Park and K. S. Yang, "A study on the student evaluation of teaching at universities by analytic hierarchy process," The Korean Journal of Educational Methodology Studies, vol. 24, no. 3, pp. 589-616, 2012.  https://doi.org/10.17927/TKJEMS.2012.24.3.589
  7. H. W. Lee and H. R. Min, "Development of concrete method through which the course evaluation feedback system could contribute to improving the quality of higher education," The Journal of Yeolin Education, vol. 21, no. 3, pp. 257-283, 2013. 
  8. K. S. Yang, "A study on the students' consistent responses to course evaluation at universities," Journal of Educational Evaluation, vol. 27, no. 2, pp. 255-278, 2014. 
  9. S. I. Han, "Analysis of students' written comments on university teaching," The Korean Educational Administration Society, vol. 21, no. 3, pp. 359-378, 2003. 
  10. T. A. Angelo and K. P. Cross, Classroom Assessment Techniques: A Handbook for College Teachers, Jossey Bass Wiley, 2012. 
  11. K. Krippendorff, Content Analysis: An Introduction to Its Methodology, Sage publications, 2019. 
  12. M. Steyvers and T. Griffiths, "Probabilistic topic models," Handbook of Latent Semantic Analysis, vol. 427, no. 7, pp. 424-440, 2007. 
  13. S. Y. Shin, H. B. Choi, and D. W. Kim, "Analyzing contents of open data in korean local governments: A textmining approach," The Korean Journal of Public Administration, vol. 30, no. 2, pp. 129-171, 2021.  https://doi.org/10.22897/KIPAJN.2021.30.2.005
  14. M. H. Kwak, H. R. Min, and M. R. Kim, "Analysis of students' open-ended course evaluation using topic modeling," Asian Journal of Education, vol. 20, no. 2, pp. 491-522, 2019.  https://doi.org/10.15753/aje.2019.06.20.2.491
  15. L. Hong and B. D. Davison, "Empirical study of topic modeling in twitter," In Proceedings of The First Workshop on Social Media Analytics, pp. 80-88, 2010. 
  16. Q. C. Fu, S. T. An, and H. N. Lee, "Analysis of user's comments of online suicide-related news reporting using topic modeling," Health and Social Welfare Review, vol. 41, no. 2, pp. 222-239, 2021.  https://doi.org/10.15709/HSWR.2021.41.2.222
  17. X. Yan, J. Guo, Y. Lan, and X. Cheng, "A biterm topic model for short texts," In Proceedings of the 22nd International Conference on World Wide Web, pp. 1445-1456, 2013. 
  18. G. S. Mun, "Evaluating LDA and BTM topic models for short texts," Journal of the Korean Data Analysis Society, vol. 23, no. 3, pp. 1313-1326, 2021.  https://doi.org/10.37727/jkdas.2021.23.3.1313
  19. Y. W. Kim, Do It! R Text Mining, Easyspub, 2021. 
  20. Y. M. Baek, Text-Mining using R, Hanul, 2017.