Abstract
In recent university education, instructional approaches such as active learning have been varied, and inter-university opportunities to take classes have expanded due to the spread of MOOCs. Therefore, students have to refer to many syllabi, which significantly increases their burden when taking classes. In order to support syllabus browsing, a method of inter-university analysis of syllabi should be useful. However, the way universities describe the course syllabus and its related instructional approaches are not uniform among universities, it is not sufficient to simply search by apparent words in the syllabus texts, and it is necessary to capture latent relationships among words. In order to solve these problems, the authors propose Joint Topic Model with Selected Side Information (SS-JTM) to obtain relationships between the contents and instructional approaches of classes of several universities by selecting instructional approaches to be used as side information from syllabi. Functional extension of topic models has the possibility of performance degradation, but the results of evaluation experiments using Web syllabi from several universities have confirmed that SS-JTM performs as well as the baseline topic models such as LDA or JTM, and thus the functionality has been successfully extended.
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Acknowledgement
The Web syllabus used in the experiment was obtained from the University of Tokyo and Mie University. This research work was supported by JSPS KAKENHI Grant Number JP22K12312.
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Shiga, K., Morimoto, N. (2023). Joint Topic Model with Selected Side Information for Inter-University Syllabus Analysis Focusing on the Variety of Instructional Approaches. In: Frasson, C., Mylonas, P., Troussas, C. (eds) Augmented Intelligence and Intelligent Tutoring Systems. ITS 2023. Lecture Notes in Computer Science, vol 13891. Springer, Cham. https://doi.org/10.1007/978-3-031-32883-1_56
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DOI: https://doi.org/10.1007/978-3-031-32883-1_56
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