Abstract
In order to help colleges better understand students’ learning personality and attitude, better guide students to learn and improve the quality of teaching. This paper uses K-Means, MiniBatchKMeans, and Birch to analyze students’ learning personality and attitude. Compared with the three algorithms, we analyze the clustering results of K-means, divide students’ learning personalities into 3 categories: “Active”, “Normal”, and “Dull”, and the attitudes of students are divided into four categories: “Negative and lazy”, “Perfunctory and active”, “Medium-general”, and “Proactive”. The function model is fitted by multiple linear regression to predict students’ scores.
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Xu, T., Zou, M., Fan, Z., Chen, Y., Zhang, Y., Min, P. (2022). Study on the Portrait of Online Learners’ Personality and Attitude. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13339. Springer, Cham. https://doi.org/10.1007/978-3-031-06788-4_35
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