Abstract
Analyzing the content and quality of teacher and students’ talk has been an active area of educational research. In this context, the importance of temporal analysis of teaching learning events has been growing up. Previous work has proposed a method that automatically describes teacher’s talk using an unsupervised machine learning model to infer topics from school textbooks. To describe teacher talk, the machine learning method measures the appearance of the inferred topics throughout each lesson. We propose a clustering method based on a modification of the method described above. The modification consists in considering super topics (Content, Administration/Feedback, Other), which will describe teacher talk more generally. Then, we cluster using ‘K-means’ with the Dynamic Time Warping metric since the lessons are dynamic phenomena that occur over time. Finally, we propose a way to visualize the center of the clusters to analyze them. We apply the proposed method to a collection of natural science lesson transcriptions, and we analyze and discuss the clusters obtained.
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References
Rymes, B.: Classroom Discourse Analysis: A tool for Critical Reflection. Routledge, Abingdon (2015)
Romero, C., Ventura, S.: Educational data mining: a review of the state of the art. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40(6), 601–618 (2010)
Knight, S., Wise, A.F., Chen, B.: Time for change: why learning analytics needs temporal analysis. J. Learn. Anal. 4(3), 7–17 (2017)
Mercer, N.: The seeds of time: why classroom dialogue needs a temporal analysis. J. Learn. Sci. 17(1), 33–59 (2008)
Carlsen, W.S.: Questioning in classrooms: a sociolinguistic perspective. Rev. Educ. Res. 61(2), 157–178 (1991)
Dantonio, M., Beisenherz, P.C.: Learning to Question, Questioning to Learn: Developing Effective Teacher Questioning Practices. Allyn & Bacon, Boston (2001)
Koizumi, Y.: Similarities and differences in teachers’ questioning in German and Japanese mathematics classrooms. ZDM Math. Educ. 45(1), 47–59 (2013)
Amershi, S., Conati, C.: Automatic recognition of learner groups in exploratory learning environments. In: International Conference on Intelligent Tutoring Systems. Springer, Heidelberg (2006)
Boyd-Graber, J., Hu,Y., Mimmo, D.: Applications of topic models. Found. Trends® Inf. Retrieval 11(2–3), 143–296 (2017)
Espinoza, C., Lämsä, J., Araya, R., Hämäläinen, R., Jiménez, A., Gormaz, R., Viiri, J.: Automatic content analysis in collaborative inquiry-based learning. In: ESERA 2019 (2019)
Araya, R., Plana, F., Dartnell, P., Soto-Andrade, J., Luci, G., Salinas, E., Araya, M.: Estimation of teacher practices based on text transcripts of teacher speech using a support vector machine algorithm. Br. J. Educ. Technol. 43(6), 837–846 (2012)
Caballero, D., Araya, R., Kronholm, H., Viiri, J., Mansikkaniemi, A., Lehesvuori, S., Kurimo, M.: ASR in classroom today: automatic visualization of conceptual network in science classrooms. In: European Conference on Technology Enhanced Learning, pp. 541–544. Springer, Cham (2017)
Senin, P.: Dynamic time warping algorithm review. Information and Computer Science Department University of Hawaii at Manoa Honolulu, USA, vol. 855, no. 1–23, p. 40 (2008)
Owens, M.T., Seidel, S.B., Wong, M., Bejines, T.E., Lietz, S., Perez, J.R., Balukjian, B.: Classroom sound can be used to classify teaching practices in college science courses. Proc. Nat. Acad. Sci. 114(12), 3085–3090 (2017)
Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media Inc, Sebastopol (2009)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Rehurek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks (2010)
Tavenard, R.: tslearn: a machine learning toolkit dedicated to time-series data (2017). https://github.com/rtavenar/tslearn
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Support from ANID/ PIA/ Basal Funds for Centers of Excellence FB0003 is gratefully acknowledged.
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Altamirano, M., Jiménez, A., Araya, R. (2020). Lessons Clustering Using Topics Inferred by Unsupervised Modeling from Textbooks. In: Vittorini, P., Di Mascio, T., Tarantino, L., Temperini, M., Gennari, R., De la Prieta, F. (eds) Methodologies and Intelligent Systems for Technology Enhanced Learning, 10th International Conference. MIS4TEL 2020. Advances in Intelligent Systems and Computing, vol 1241. Springer, Cham. https://doi.org/10.1007/978-3-030-52538-5_10
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DOI: https://doi.org/10.1007/978-3-030-52538-5_10
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