Abstract
Students have produced a large number of data in the teaching life of colleges and universities. At present, the development trend of university data is to gradually form a high-dimensional data storage system composed of student status information, educational administration information, behavior information, etc. It is of great significance to make use of the existing data of students in Colleges and universities to carry out deep-seated and personalized data mining for college education decision-making, implementation of education and teaching programs, and evaluation of education and teaching. Student portrait is the extension of user portrait in the application of education data mining. According to the data of students’ behavior in school, a labeled student model is abstracted. To address above problems, a hybrid neural network model is designed and implemented to mine the data of college students and build their portraits, so as to help students’ academic development and improve the quality of college teaching. In this paper, experiments are carried out on real datasets (the basic data of a college’s students in Beijing and the behavior data in the second half of 2018–2019 academic year). The results show that the hybrid neural network model is effective.
Supported by the National Key R&D Program of China under grant number 2017YFC0803300.
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Ding, Z., Li, X. (2021). College Students’ Portrait Technology Based on Hybrid Neural Network. In: Meng, X., Xie, X., Yue, Y., Ding, Z. (eds) Spatial Data and Intelligence. SpatialDI 2020. Lecture Notes in Computer Science(), vol 12567. Springer, Cham. https://doi.org/10.1007/978-3-030-69873-7_12
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