DOI QR코드

DOI QR Code

과학교육 분야 자연어 처리 기법의 연구동향 분석

An Analysis of Trends in Natural Language Processing Research in the Field of Science Education

  • 투고 : 2023.10.24
  • 심사 : 2024.01.09
  • 발행 : 2024.02.29

초록

본 연구는 2011년부터 2023년 9월까지 과학교육 분야에서 자연어 처리(NLP) 기법을 적용한 37건의 국내 및 해외 문헌을 분석하여 과학교육에서의 NLP 관련 연구 동향을 파악하고자 하였다. 특히 과학교육에서 NLP 기법의 주요 응용 분야, NLP 기법을 활용할 때 교사의 역할, 국내와 해외의 비교 측면에서 그 내용을 체계적으로 분석하였다. 분석 결과는 다음과 같다. 첫째, NLP 기법이 과학교육에서 형성평가, 자동 채점, 문헌 검토 및 분류, 패턴 추출에 중요하게 활용되고 있음을 확인하였다. 형성평가에서 NLP를 활용하면 학생들의 학습과정과 이해도를 실시간으로 분석할 수 있다. 이는 교사의 수업에 대한 부담을 줄이고, 학생들에게 정확하고 효과적인 피드백을 제공할 수 있다. 자동 채점에서는 학생들의 응답을 빠르고 정확하게 평가하는 데 기여한다. 문헌 검토 및 분류에서는 과학교육 관련 연구나 학생들의 보고서를 분석하여 주제와 트렌드를 효과적으로 분석하고, 미래 연구 방향을 설정하는 데 도움을 준다. NLP 기법을 패턴 추출에 활용하면 학생들의 생각과 반응에 나타난 공통점이나 패턴을 찾아 효과적으로 분석할 수 있다. 둘째, 과학교육에서 NLP 기법의 도입은 교사의 역할을 지식 전달자에서 학생들의 학습을 지원하고 촉진하는 지도자로 확장했고, 교사들에게 지속적인 전문성 개발을 요구한다. 셋째, 국내에서는 문헌 검토 및 분류에 집중되어 있어 국내 NLP 연구의 다양성을 위해 텍스트 데이터 수집이 용이한 환경 조성이 필요하다. 이러한 분석 결과를 바탕으로 과학교육에서 NLP 기법의 활용하는 방법에 대해 논의하였다.

This study aimed to examine research trends related to Natural Language Processing (NLP) in science education by analyzing 37 domestic and international documents that utilized NLP techniques in the field of science education from 2011 to September 2023. In particular, the study systematically analyzed the content, focusing on the main application areas of NLP techniques in science education, the role of teachers when utilizing NLP techniques, and a comparison of domestic and international perspectives. The analysis results are as follows: Firstly, it was confirmed that NLP techniques are significantly utilized in formative assessment, automatic scoring, literature review and classification, and pattern extraction in science education. Utilizing NLP in formative assessment allows for real-time analysis of students' learning processes and comprehension, reducing the burden on teachers' lessons and providing accurate, effective feedback to students. In automatic scoring, it contributes to the rapid and precise evaluation of students' responses. In literature review and classification using NLP, it helps to effectively analyze the topics and trends of research related to science education and student reports. It also helps to set future research directions. Utilizing NLP techniques in pattern extraction allows for effective analysis of commonalities or patterns in students' thoughts and responses. Secondly, the introduction of NLP techniques in science education has expanded the role of teachers from mere transmitters of knowledge to leaders who support and facilitate students' learning, requiring teachers to continuously develop their expertise. Thirdly, as domestic research on NLP is focused on literature review and classification, it is necessary to create an environment conducive to the easy collection of text data to diversify NLP research in Korea. Based on these analysis results, the study discussed ways to utilize NLP techniques in science education.

키워드

참고문헌

  1. Beggrow, E. P., Ha, M., Nehm, R. H., Pearl, D., & Boone, W. J. (2014). Assessing scientific practices using machine-learning methods: How closely do they match clinical interview performance?. Journal of Science education and Technology, 23, 160-182. 
  2. Brown, P. F., Della Pietra, V. J., Desouza, P. V., Lai, J. C., & Mercer, R. L. (1992). Class-based n-gram models of natural language. Computational Linguistics, 18(4), 467-480. 
  3. Chang, J., & Na, J. (2022). An examination of the topics and changes in the research papers published in the Journal of Korean Elementary Science Education using latent dirichlet allocation for the topic modeling analysis. Journal of Korean Elementary Science Education, 41(2), 356-372.  https://doi.org/10.15267/KESES.2022.41.2.356
  4. Chang, J., & Na, J. (2022). How the Journal of the Korean Association for Science Education(JKASE) changed for the past 44 years?: Topic modeling analysis using latent dirichlet allocation. Journal of the Korean Association for Science Education, 42(2), 185-200. 
  5. Choi, J., Song, H., & Nam, K. (2010). Formulaic expressions in korean. Discourse and Cognition, 17(2), 163-190.  https://doi.org/10.15718/DISCOG.2010.17.2.163
  6. Chowdhary, K., & Chowdhary, K. R. (2020). Natural language processing. Fundamentals of artificial intelligence, 603-649. 
  7. Deng, L., & Yu, D. (2014). Deep learning: methods and applications. Foundations and trends® in signal processing, 7(3-4), 197-387.  https://doi.org/10.1561/2000000039
  8. Donnelly, D. F., Vitale, J. M., & Linn, M. C. (2015). Automated guidance for thermodynamics essays: Critiquing versus revisiting. Journal of Science Education and Technology, 24, 861-874. 
  9. Gombert, S., Di Mitri, D., Karademir, O., Kubsch, M., Kolbe, H., Tautz, S., Grimm, A., Bohm, I., Neumann, K., & Drachsler, H. (2023). Coding energy knowledge in constructed responses with explainable NLP models. Journal of Computer Assisted Learning, 39(3), 767-786.  https://doi.org/10.1111/jcal.12767
  10. Ha, M., & Nehm, R. H. (2016). The impact of misspelled words on automated computer scoring: A case study of scientific explanations. Journal of Science Education and Technology, 25, 358-374.  https://doi.org/10.1007/s10956-015-9598-9
  11. Ha, M., Nehm, R. H., Urban-Lurain, M., & Merrill, J. E. (2011). Applying computerized-scoring models of written biological explanations across courses and colleges: Prospects and limitations. CBE-Life Sciences Education, 10(4), 379-393.  https://doi.org/10.1187/cbe.11-08-0081
  12. Han, S., Kim, Y., & Kim, H. (2020). A study on the conceptual changes of extra-solar planet in university students using text-mining techniques. Journal of Korean Society of Earth Science Education, 13(3), 305-316. 
  13. Isotani, S., Millan, E., Ogan, A., Hastings, P., McLaren, B., & Luckin, R. (2019). Artificial intelligence in education. Chicago: Springer International Publishing. 
  14. Jho, H. (2023). Understanding of generative artificial intelligence based on textual data and discussion for its application in science education. Journal of the Korean Association for Science Education, 43(3), 307-319.  https://doi.org/10.14697/JKASE.2023.43.3.307
  15. Jho, H., & Lee, B. (2022). Clustering science gifted students' graduation theses based on machine learning. Journal of Science Education for the Gifted, 14(1), 13-22. 
  16. Jockers, M. L., & Thalken, R. (2020). Text analysis with R. New York: Springer International Publishing. 
  17. Kang, M., Chaudhuri, S., Joshi, M., & Rose, C. (2008). Side: The summarization integrated development environment. In Proceedings of the ACL-08: HLT Demo Session, 24-27. 
  18. Kim, H., & Jhun, Y. (2021). Analysis of trends in science gifted education using topic modeling. Journal of Korean Elementary Science Education, 40(3), 283-294.  https://doi.org/10.15267/KESES.2021.40.3.283
  19. Kim, J., & Oh, Y. (2023). Content analysis of education activities of science high schools using network text analysis and topic modeling. Journal of Gifted/Talented Education, 33(3), 375-402.  https://doi.org/10.9722/JGTE.2023.33.3.375
  20. Lantz, B. (2019). Machine learning with R: expert techniques for predictive modeling. UK: Packt publishing ltd. 
  21. Lee, G., Ha H., Hong, H., & Kim, H. (2018). Exploratory research on automating the analysis of scientific argumentation using machine learning. Journal of the Korean Association for Science Education, 38(2), 219-234.  https://doi.org/10.14697/JKASE.2018.38.2.219
  22. Lee, H. S., Gweon, G. H., Lord, T., Paessel, N., Pallant, A., & Pryputniewicz, S. (2021). Machine learning-enabled automated feedback: Supporting students' revision of scientific arguments based on data drawn from simulation. Journal of Science Education and Technology, 30, 168-192.  https://doi.org/10.1007/s10956-020-09889-7
  23. Lee, H. S., Pallant, A., Pryputniewicz, S., Lord, T., Mulholland, M., & Liu, O. L. (2019). Automated text scoring and real-time adjustable feedback: Supporting revision of scientific arguments involving uncertainty. Science Education, 103(3), 590-622. 
  24. Lee, M., & Ryu, S. (2020). Automated scoring of scientific argumentation using expert morpheme classification approaches. Journal of the Korean Association for Science Education, 40(3), 321-336. 
  25. Lee, M., & Ryu, S. (2021). Automated scoring of argumentation levels and analysis of argumentation patterns using machine learning. Journal of the Korean Association for Science Education, 41(3), 203-220. 
  26. Lim, H. (2019). Natural language processing bible. Seoul: Human Science. 
  27. Litman, D. (2016). Natural language processing for enhancing teaching and learning. In Proceedings of the AAAI conference on artificial intelligence, 30(1), 4170-4176. 
  28. Liu, O. L., Brew, C., Blackmore, J., Gerard, L., Madhok, J., & Linn, M. C. (2014). Automated scoring of constructed-response science items: Prospects and obstacles. Educational Measurement: Issues and Practice, 33(2), 19-28. 
  29. Liu, O. L., Rios, J. A., Heilman, M., Gerard, L., & Linn, M. C. (20 16). Validation of automated scoring of science assessments. Journal of Research in Science Teaching, 53(2), 215-233. 
  30. Mao, L., Liu, O. L., Roohr, K., Belur, V., Mulholland, M., Lee, H. S., & Pallant, A. (2018). Validation of automated scoring for a formative assessment that employs scientific argumentation. Educational Assessment, 23(2), 121-138.  https://doi.org/10.1080/10627197.2018.1427570
  31. Michaud, L. N., & McCoy, K. F. (2006). Capturing the evolution of grammatical knowledge in a CALL system for deaf learners of English. International Journal of Artificial Intelligence in Education, 16(1), 65-97. 
  32. Min, G., & Yoo, J. (2022). Development of a middle school science Q&A chatbot using Doc2Vec and analysis of student's queries. The SNU Journal of Education Research, 31(3), 115-145. 
  33. Nakamura, C. M., Murphy, S. K., Christel, M. G., Stevens, S. M., & Zollman, D. A. (2016). Automated analysis of short responses in an interactive synthetic tutoring system for introductory physics. Physical Review Physics Education Research, 12(1), 010122. 
  34. Nehm, R. H., Ha, M., & Mayfield, E. (2012). Transforming biology assessment with machine learning: automated scoring of written evolutionary explanations. Journal of Science Education and Technology, 21, 183-196.  https://doi.org/10.1007/s10956-011-9300-9
  35. Odden, T. O. B., Marin, A., & Caballero, M. D. (2020). Thematic analysis of 18 years of physics education research conference proceedings using natural language processing. Physical Review Physics Education Research, 16(1), 010142. 
  36. Odden, T. O. B., Marin, A., & Rudolph, J. L. (2021). How has Science education changed over the last 100 years? An analysis using natural language processing. Science Education, 105(4), 653-680. 
  37. Oh, C., & Kang, N. (2021). Analyzing different contexts for energy terms through text mining of online science news articles. Journal of Science Education, 45(3), 292-303. 
  38. Park, C., Kim, Y., Kim, J., Song, J., & Choi, H. (2015). R data mining. Seoul: Kyowoo. 
  39. Reese, R. M., & Bhatia, A. (2018). Natural language processing with Java: Techniques for building machine learning and neural network models for NLP. Birmingham: Packt Publishing Ltd. 
  40. Rosenberg, J. M., & Krist, C. (2021). Combining machine learning and qualitative methods to elaborate students' ideas about the generality of their model-based explanations. Journal of Science Education and Technology, 30, 255-267. 
  41. Sarkar, D. (2019). Text analytics with Python: a practitioner's guide to natural language processing. Bangalore: Apress. 
  42. Shaik, T., Tao, X., Li, Y., Dann, C., McDonald, J., Redmond, P., & Galligan, L. (2022). A review of the trends and challenges in adopting natural language processing methods for education feedback analysis. IEEE Access, 10, 56720-56739.  https://doi.org/10.1109/ACCESS.2022.3177752
  43. Sherin, B. (2013). A computational study of commonsense science: An exploration in the automated analysis of clinical interview data. Journal of the Learning Sciences, 22(4), 600-638.  https://doi.org/10.1080/10508406.2013.836654
  44. Shin, E. (2022). Science teachers' motivation and perception of science⋅AI convergence education. The Korean Society for School Science, 16(3), 398-412. 
  45. Shin, S., Ha, M., & Lee, J. (2018). Rediscovering the interest of science education: Focus on the meaning and value of interest. Journal of the Korean Association for Science Education, 38(5), 705-720. 
  46. Sung, S. H., Li, C., Chen, G., Huang, X., Xie, C., Massicotte, J., & Shen, J. (2021). How does augmented observation facilitate multimodal representational thinking? Applying deep learning to decode complex student construct. Journal of Science Education and Technology, 30, 210-226.  https://doi.org/10.1007/s10956-020-09856-2
  47. Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15. 
  48. Wilson, J., Pollard, B., Aiken, J. M., Caballero, M. D., & Lewandowski, H. J. (2022). Classification of open-ended responses to a research-based assessment using natural language processing. Physical Review Physics Education Research, 18(1), 010141. 
  49. Wulff, P., Buschhuter, D., Westphal, A., Mientus, L., Nowak, A., & Borowski, A. (2022). Bridging the gap between qualitative and quantitative assessment in science education research with machine learning-A case for pretrained language models-based clustering. Journal of Science Education and Technology, 31(4), 490-513.  https://doi.org/10.1007/s10956-022-09969-w
  50. Wulff, P., Buschhuter, D., Westphal, A., Nowak, A., Becker, L., Robalino, H., Steda, M., & Borowski, A. (2021). Computer-based classification of preservice physics teachers' written reflections. Journal of Science Education and Technology, 30, 1-15. 
  51. Wulff, P., Mientus, L., Nowak, A., & Borowski, A. (2023). Utilizing a pretrained language model (BERT) to classify preservice physics teachers' written reflections. International Journal of Artificial Intelligence in Education, 33(3), 439-466.  https://doi.org/10.1007/s40593-022-00290-6
  52. Wulff, P., Westphal, A., Mientus, L., Nowak, A., & Borowski, A. (2023). Enhancing writing analytics in science education research with machine learning and natural language processing-Formative assessment of science and non-science preservice teachers' written reflections. In Frontiers in Education. 7, 1061461. 
  53. Yoo. J. E. (2019). Machine learning for large-scale/panel data and learning analytics data analysis. Journal of Educational Technology, 35(2), 313-338.  https://doi.org/10.17232/KSET.35.2.313
  54. Zhai, X., He, P., & Krajcik, J. (2022). Applying machine learning to automatically assess scientific models. Journal of Research in Science Teaching, 59(10), 1765-1794. 
  55. Zhu, M., Lee, H. S., Wang, T., Liu, O. L., Belur, V., & Pallant, A. (2017). Investigating the impact of automated feedback on students' scientific argumentation. International Journal of Science Education, 39(12), 1648-1668. https://doi.org/10.1080/09500693.2017.1347303