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College professional course score prediction method based on deep learning

A technology of deep learning and prediction method, applied in the direction of instrumentation, design optimization/simulation, calculation, etc., can solve problems such as inability to mine information, inaccurate grade prediction, etc., and achieve the effect of ensuring accuracy

Pending Publication Date: 2022-04-22
SHANGHAI NORMAL UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The qualitative analysis of performance prediction is still widely based on empirical judgment or traditional numerical correlation analysis, and the existing prediction methods are all based on small sample data or small sample sampling surveys, and cannot learn from massive student data. Mining the information related to the prediction, resulting in inaccurate performance prediction

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  • College professional course score prediction method based on deep learning
  • College professional course score prediction method based on deep learning
  • College professional course score prediction method based on deep learning

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Embodiment Construction

[0035] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0036] This embodiment provides a method for predicting grades of professional courses in colleges and universities based on deep learning, such as figure 1 As shown, it specifically includes the following steps:

[0037] Step S1. Select the course whose grade needs to be predicted as the successor course, and obtain the correlation support between the successor course and the predecessor course set according to the historical data of the student's grade.

[0038] Wherein, the precursor course set may be one precursor course, or may be composed of multiple precursor courses, and the ...

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Abstract

The invention relates to a college professional course score prediction method based on deep learning, and the method comprises the following steps: selecting a course of which the score needs to be predicted as a subsequent course, and obtaining the correlation support degree of the subsequent course and a leading course set according to the historical data of student scores; selecting a leading course set with the maximum association support degree with the subsequent courses, and establishing a subsequent course score prediction neural network model; training the subsequent course score prediction neural network model to obtain a final training model; and inputting the score of the leading course set into the final training model to obtain a subsequent course prediction score. Compared with the prior art, the method has the advantages of accurate prediction result and the like.

Description

technical field [0001] The present invention relates to the field of smart education based on big data, in particular to a method for predicting grades of professional courses in colleges and universities based on deep learning. Background technique [0002] The curriculum system is the core of college students' professional learning, so the rationality and applicability of the curriculum will directly affect the level of students' professional ability and comprehensive quality. The curriculum setting of various majors in colleges and universities pays special attention to the succession relationship of courses and the combing of "pre-sequence courses-successor courses". With the massive increase of educational data, how to use the massive student learning big data accumulated in the education process to tap the correlation between courses is a key issue for colleges and universities to realize smart education. [0003] At the same time, how to predict the grades of student...

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Application Information

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IPC IPC(8): G06F30/27G06K9/62G06F119/02
CPCG06F30/27G06F2119/02G06F18/214
Inventor 张波徐立雍睿涵
Owner SHANGHAI NORMAL UNIVERSITY