References
- 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.
- 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.
- 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
- 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.
- 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
- Chowdhary, K., & Chowdhary, K. R. (2020). Natural language processing. Fundamentals of artificial intelligence, 603-649.
- 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
- 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.
- 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
- 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
- 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
- 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.
- Isotani, S., Millan, E., Ogan, A., Hastings, P., McLaren, B., & Luckin, R. (2019). Artificial intelligence in education. Chicago: Springer International Publishing.
- 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
- 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.
- Jockers, M. L., & Thalken, R. (2020). Text analysis with R. New York: Springer International Publishing.
- 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.
- 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
- 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
- Lantz, B. (2019). Machine learning with R: expert techniques for predictive modeling. UK: Packt publishing ltd.
- 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
- 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
- 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.
- 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.
- 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.
- Lim, H. (2019). Natural language processing bible. Seoul: Human Science.
- Litman, D. (2016). Natural language processing for enhancing teaching and learning. In Proceedings of the AAAI conference on artificial intelligence, 30(1), 4170-4176.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- Park, C., Kim, Y., Kim, J., Song, J., & Choi, H. (2015). R data mining. Seoul: Kyowoo.
- 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.
- 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.
- Sarkar, D. (2019). Text analytics with Python: a practitioner's guide to natural language processing. Bangalore: Apress.
- 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
- 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
- Shin, E. (2022). Science teachers' motivation and perception of science⋅AI convergence education. The Korean Society for School Science, 16(3), 398-412.
- 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.
- 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
- 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.
- 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.
- 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
- 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.
- 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
- 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.
- 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
- 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.
- 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