Journal of Platform Technology

ICT Platform Society (ICT플랫폼학회)
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Analysis of Descriptive Lecture Evaluation on Liberal Arts ICT utilization using Topic Modeling

토픽 모델링을 활용한 교양 ICT 활용과정 서술형 강의평가 분석

  • 김효숙 (목원대학교 지능정보융합학과)
  • Received : 2020.02.17
  • Accepted : 2020.03.29
  • Published : 2020.03.30
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Abstract

The purpose of this study is to identify factors in selecting the elective ICT utilization lecture and to find positive and negative elements of the lecture through conducting topic modeling analysis of text mining of the narrative lecture evaluation. In order to do so, from pre-processing of data, keyword frequency analysis to wordcloud visualization and topic modeling analysis have been conducted from 'reasons of selecting the lecture,' 'improvements to be made on the lecture,' and 'what I liked about the lecture' categories regarding the ICT utilization lecture which was opened in the second semester of 2019 at M University. The analysis results show that students mostly registered for the ICT utilization lecture at M University to obtain a certificate and the fact being certified and taking the lecture can be done simultaneously is a positive element of taking the lecture. On the other hand, negative element included inconvenience of the classroom setting environment.

본 연구의 목적은 교양 ICT활용 과정의 서술형 강의 평가에 대해 텍스트 마이닝의 토픽모델링 분석을 실시하여 수강생의 강의 선택 요인과 강의에 대한 긍정적·부정적 요소 파악을 하고자 하는데 있다. 이를 위해 M 대학교의 2019년 2학기에 개설된 ICT활용 과정 강의에 대해 '강의를 신청한 이유', '강의에서 개선되어야 할 점'과 '강의에서 좋았던 점'에 대한 데이터 전처리부터 키워드 빈도 분석, 워드 클라우드 시각화 및 토픽 모델링 분석을 실시하였다. 연구결과 M 대학의 2019년 2학기 ICT활용 과정은 자격증 취득을 위해 강의를 신청하며, 동시에 자격증을 취득할 수 있어 강의가 좋았다는 긍정적 분석을 알 수 있다. 부정적 요소로 강의실 사용 환경 불편에 대한 것을 알 수 있다.

Keywords

References

  1. O-Young Kwon, Young-Tae Park, Il-Kyu Hwang, Tae-Won Ahn, Kyeong-Sook Kim, "A Study on the Reliability Improvement for the Course Evaluation System," Journal of Engineering Education Research, Vol. 17, No. 2, pp. 35~41, March, 2014. https://doi.org/10.18108/JEER.2014.17.2.35
  2. Lee, Hoo-Hee, Lee, Sang-Soo, Lee, Soo-Sang, Kim, Eun-Jung, "Semantic Network Analysis of College Students," Journal of Educational Innovation Research, Vol. 28, No. 2, pp.237-262, 2018. https://doi.org/10.21024/pnuedi.28.2.201806.237
  3. Minho Kwak, Hyeree Min, Meereem Kim, "Analysis of Students Open-Ended Course Evaluation Using Topic Modeling," Asian Journal of Education, Vol. 20, No. 2, pp.491-522, June, 2019. https://doi.org/10.15753/aje.2019.06.20.2.491
  4. Khan, R.A. and Kanth, S. "Text Mining: Knowledge Discovery from Unstructured data, Artifical Intelligent Systems and Machine Learning", 8(2), 71-77, 2016
  5. Baek Young, Y. Text Mining using R, Paju: HanulAcademy, 2017.
  6. HaeDeum Lee, MinWoo Nam, "Better Class's Characteristics by Major Field based on the Analysis of Text Mining from College Course Evaluation Subjective Results", Research Institute for Early Childhood Education, Vol.20, no.2, pp. 21-41, 2018
  7. JungWoong Choi, Dongkyu An "A Study on the Data Analysis of the Written Comments in Lecture Evaluation", Journal of Digital Convergence Vol, 14, no.11, pp. 101-106, 2016 https://doi.org/10.14400/JDC.2016.14.11.101
  8. JongHo Shin, Jaewon Choi, "Text mining Analysis of College Students' Descriptive Course Evaluation", Journal of Learner-Centered Curriculum and Instruction, Vol.19, no.16, pp. 77-99, 2019
  9. SungHoon Seo, HakYeon Lee, "Fintech trend analysis using topic modeling of BM patents", The Korean Institute of Industrial Engineers fall conference, pp. 471-480, 2015
  10. David M. Blei, Andrew Y. Ng, Michael I. Jordan, "Latent Dirichlet Allocation", Journal of Machine Learning Research, Vol 3, pp. 993-1022, JAN, 2003
  11. JuSeop Park, SoonGoo Hong, JongWeon Kim. "A Study on Science Technology Trend and Prediction Using Topic Modeling", Journal of the Korea Industrial Information Systems Research, Vol 22, No 4, pp. 19-28, 2017 https://doi.org/10.9723/jksiis.2017.22.4.019
  12. Blei, D., Ng, A. and Jordan, M., 'Latent Dirichlet Allocation,' Journal of Machine Learning Research, Vol. 3, pp. 993- 1022, 2003.
  13. Blei, D. M. (2012). Probabilistic topic models, Communications of the ACM, 55(4), 77-84. https://doi.org/10.1145/2133806.2133826
  14. Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics, Proceedings of the National academy of Sciences , 101(suppl1), 5228-5235. https://doi.org/10.1073/pnas.0307752101
  15. SooSang Lee, "A Study on the Application of Topic Modeling for the Book Report Text", Journal of Korean Library and Information Science Society, Vol 47(4), pp. 1-18, 2016 https://doi.org/10.16981/kliss.47.4.201612.1
  16. Marwa Naili, Anja Chaibi, Henda Ghezala. "Arabic topic identification based on empirical studies of topic models", Revue Africaine de la Recherche en Informatique et Mathematiques Appliquees, 27. 45-49. 2017