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A student dropout prediction method based on attention deep learning model

A technology of deep learning and prediction methods, applied in neural learning methods, prediction, biological neural network models, etc., can solve problems such as low accuracy

Active Publication Date: 2022-06-07
GUILIN UNIV OF ELECTRONIC TECH
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a student dropout prediction method based on the Attention deep learning model, aiming to solve the problem of low accuracy of the traditional dropout prediction method

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  • A student dropout prediction method based on attention deep learning model
  • A student dropout prediction method based on attention deep learning model
  • A student dropout prediction method based on attention deep learning model

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Embodiment Construction

[0040] The following describes in detail the embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary, and are intended to explain the present invention and should not be construed as limiting the present invention.

[0041] see figure 1 , figure 1 It is a schematic flowchart of a method for predicting student dropout based on the Attention deep learning model provided by the present invention. Specifically, the method for predicting student dropout based on the Attention deep learning model may include the following steps:

[0042] S101. Screen and preprocess a data set of original online learning behavior-related data;

[0043] In the embodiment of the present invention, please refer to figure ...

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Abstract

The invention discloses a method for predicting student dropouts based on an Attention deep learning model, which includes encoding and time windowing of behavior records to generate multiple time slice behavior vector matrices; the improved convolution layer based on CNN network is multi- A time slice behavior vector matrix is ​​processed to obtain multiple eigenvector matrices with local correlations; based on the BI‑GRU model, multiple feature vector matrices with local correlations are extracted for time-series characteristics, and multiple time-series relationships are generated. Behavior feature vector matrix; based on the Attention mechanism, assign different weights to the hidden layer features at each time, weighted and sum the hidden layer states and weights at different times, generate behavior feature representation vectors, and input them into the classification layer, and use the Sigmoid function to perform Forecast and get the forecast result. By considering the relationship between students' learning behaviors and the influence of different behavioral characteristics on dropout prediction, the prediction is realized and the accuracy of dropout prediction is improved.

Description

technical field [0001] The invention relates to the technical fields of machine learning, deep learning and data mining, in particular to a student dropout prediction method based on an Attention deep learning model. Background technique [0002] With the rapid development of the Internet, the educational model has begun to change, and more and more different groups of people have different demands for educational quality and educational content. Therefore, a large number of online course learning platforms have emerged as the times require. Since MOOC, a large online learning platform, has emerged since 2012, it includes famous schools from all over the world publishing courses on MOOC. The learning platform transcends the limitations of time, space and even identity. As long as people who are willing to learn, no matter what profession they are engaged in, they can conduct online learning by registering an account, but this also brings serious problems. The dropout rate is...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/20G06N3/04G06N3/08
CPCG06Q10/04G06Q50/205G06N3/08G06N3/045
Inventor 常亮张艳刘铁园古天龙
Owner GUILIN UNIV OF ELECTRONIC TECH
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