Estimating the Difficulty of Courses From Syllabuses
DOI:
https://doi.org/10.52731/liir.v001.036Keywords:
syllabus, topic modeling, support vector machineAbstract
Syllabuses of many universities are published on the Web, and in some cases, past average scores are also published for the convenience of student registration. In this study, we investigate the relationship between the content of the syllabuses and the average scores, and clarify whether the difficulty level of the courses can be estimated from the syllabuses. In the proposed method, first, the topics that make up the syllabuses are extracted using topic analysis. Then, we find out how much each syllabus contains these topics. The difficulty of courses is estimated from these features using support vector machine. 10-fold cross-validation was performed for the evaluation, and it became clear that the difficulty level can be estimated with an accuracy of 62.3%.
References
A. Moretti, J.P. González-Brenes, and K. McKnight, ”Data-Driven Curriculum De-sign: Mining the Web to Make Better Teaching Decisions, ” Proceedings of the 7th International Conference on Educational Data Mining, 2014, pp.421-422.
M. Yasukawa, H. Yokouchi, and K. Yamazaki, ”Syllabus Mining for Analysis of Searchable Information, ” International Journal of Institutional Research and Man-agement, Vol.4, No.1, 2020, pp.46–65.
T. Sekiya, Y. Matsuda, and K. Yamaguchi, ”Curriculum analysis of CS departments based on CS2013 by simplified, supervised LDA, ” Proceedings of the 5th Interna-tional Conference on Learning Analytics and Knowledge, 2015, pp. 330-339.
