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College Students' portrait technology based on hybrid neural network

EasyChair Preprint no. 2248

19 pagesDate: December 25, 2019


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. According to the 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. Based on the basic data of a college student in Beijing and the behavior data in the second half of 2018-2019 academic year, the classification accuracy of the model in the student portrait label is verified.

Keyphrases: Big Data, Data Mining, feedforward neural network, higher education, Recurrent Neural Network

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Zhiming Ding and Xuyang Li},
  title = {College Students' portrait technology based on hybrid neural network},
  howpublished = {EasyChair Preprint no. 2248},

  year = {EasyChair, 2019}}
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