Abstract
Predicting students’ exam scores has been a popular topic both in educational psychology and data mining areas for many years. Currently, many researchers devote efforts to predict exam score precisely with student behavior data and exercise content data. In this paper, we present the Topic-Based Latent Variable Model (TB-LVM) to predict the midterm and final scores with students’ textbook reading notes. We compare the Topic-Based Latent Variable Model with the Two-Step LDA model. For TB-LVM, the standard deviations of the maximum likelihood estimation and method of moments for the midterm exam are 7.79 and 7.63, respectively. The two standard deviations for the final exam are 8.68 and 7.72, respectively. The results are much better than the results of the Two-Step LDA model, which is 14.38 for the midterm exam and 16.55 for the final exam. Finally, we also compare with the knowledge graph embedding method to predict exam scores.
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Pan, Z., Gao, Y., Ge, T. (2021). Revealing the Relation Between Students’ Reading Notes and Scores Examination with NLP Features. In: Stahlbock, R., Weiss, G.M., Abou-Nasr, M., Yang, CY., Arabnia, H.R., Deligiannidis, L. (eds) Advances in Data Science and Information Engineering. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-71704-9_2
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