Student risk early warning model building technology based on social network

A technology of risk warning and social network, applied in the direction of biological neural network model, neural learning method, special data processing application, etc.

Pending Publication Date: 2021-02-12
TIANJIN UNIV
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  • Claims
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Problems solved by technology

However, few studies have considered social influence on academic per

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  • Student risk early warning model building technology based on social network
  • Student risk early warning model building technology based on social network
  • Student risk early warning model building technology based on social network

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Embodiment Construction

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention.

[0032] refer to Figure 1-3 , a social network-based student risk early warning model building technology, including:

[0033] S101: Obtain behavioral and psychological data of students, and perform data preprocessing and feature extraction.

[0034] Specifically, collect the student card data in one semester and encrypt the ID, and at the same time classify the data set according to the location of the student's card swiping. To protect privacy, the student's personal ID is first encrypted, and then the data is collected It contains many features. We need to extract the features needed to establish a co-occurrence network, such as ID, POS number, number of card swi...

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Abstract

The invention discloses a student risk early warning model building technology based on a social network, and the technology comprises the steps: firstly obtaining the behavior and psychological dataof a student, and carrying out the data preprocessing and feature extraction; carrying out feature selection and division on the obtained data, carrying out pre-training on two thirds of the data, andestablishing a co-occurrence network of students; carrying out feature fusion after the co-occurrence network is obtained, and obtaining input of a student risk early warning model; and finally, fusing the co-occurrence network and the time recurrent neural network, and fusing an Attention mechanism to perform student risk prediction. According to the invention, student risk prediction is achieved, early warning information is sent to college teachers, college academic achievement early warning, study and life guidance and advice, psychological counseling and other services are provided for colleges, and teachers can be helped to better understand students.

Description

technical field [0001] The invention relates to the technical field of data mining and processing, in particular to a technology for establishing a student risk early warning model based on a social network. Background technique [0002] Every year, college educators are baffled by students' poor academic performance and risk behaviors such as suicide due to mental illness and look for internal factors to prevent them. These risky behaviors largely affect students' graduation, job hunting, and even future development. There are some traditional methods to predict students’ academic performance as well as mental illness based on information from different sources, such as students’ self-reports, behavioral data obtained from smartphones, questionnaires, etc. The psychological and behavioral performance of students is the key to these risky behaviors. However, few studies have considered social influence on academic performance and the interaction between social and psycholo...

Claims

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Application Information

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IPC IPC(8): G06F16/9536G06N3/04G06N3/08G16H20/70
CPCG06F16/9536G16H20/70G06N3/08G06N3/045
Inventor 雍鑫焦鹏飞王文俊潘林孙越恒
Owner TIANJIN UNIV
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