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Children personalized behavior statistical analysis system and method based on latent variable model

A statistical analysis and latent variable technology, applied in computing, computer parts, character and pattern recognition, etc., can solve the problems of low data fitting accuracy, small sample size, and lack of objectivity of data.

Active Publication Date: 2019-09-13
CHONGQING UNIV OF EDUCATION
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AI Technical Summary

Problems solved by technology

[0004] Problem 1: The sample size is small and the data lacks objectivity
[0005] Problem 2. Traditional research methods lead to low accuracy of data fitting
The current expression recognition technology can handle the expression recognition of a single face, and this data can form a one-to-one correspondence with the explanatory variables of the corresponding subjects, but the data obtained from the expression recognition of multiple faces must be established with the matching explanatory variables The one-to-one correspondence is the difficulty of this scheme two

Method used

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  • Children personalized behavior statistical analysis system and method based on latent variable model
  • Children personalized behavior statistical analysis system and method based on latent variable model
  • Children personalized behavior statistical analysis system and method based on latent variable model

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Embodiment 1

[0092] 1. Constructing a theoretical model of personalized behavior-ability of children aged 0-12 To build an analysis model of children's personalized behavior, it is first necessary to construct a three-dimensional structural diagram of age, behavior, and ability, such as Figure 4 . The second is the crescendo-type category of children's personality behavior and abilities with age.

[0093] 2. The latent variable of individualized behavior of children aged 0-12 is based on the three-dimensional correlation structure of age-behavior-ability, the latent variable of individualized behavior with age, and the logic between latent variables. Latent variables are variables that cannot be directly measured. In the process of children's education and growth, according to the differences in latent variable description objects, latent variables that are commonly used or may be used in personalized behavior are divided into three categories: ability latent variables, personal percepti...

Embodiment 2

[0151] 1. An example of children's face recognition

[0152] This embodiment uses python third-party library sk-learn machine learning+principal component analysis (PCA)+support vector machine (SVM) to realize face recognition.

[0153] First prepare the photos of the children to be trained, and do grayscale processing on the images. Use the Image function to read in the data set, divide the data set, one part is used for the training set train, and the other part is used for the test set test. Using the idea of ​​principal component analysis, select the number of retained principal components n_components, select the "randomized" SVD method, and use the "whiten" method in data preprocessing to obtain the projection coefficients of the training set and the test set. Use the training set to train an SVM classifier for the identification of the test set. Finally, use the trained SVM classifier for face recognition on the test set.

[0154] If it is a student in the class, it ...

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Abstract

The invention belongs to the technical field of children personalized behavior analysis, and discloses a children personalized behavior statistical analysis system and method based on a latent variable model. The children personalized behavior statistical analysis method includes the steps: according to an established latent variable model, applying a latent variable to analysis of a personalizedbehavior problem through a mathematical model; making a main tool-scale for latent variable measurement, the scales including an evaluation scale and an attitude scale, and the scales being divided into a three-point scale, a five-point scale and a seven-point scale from the perspective of questionnaire question options; and analyzing the internal relation between the difference influencing the personalized behaviors of the children and the latent variable factors, discovering the potential ability of the children from the behavior performance of the children, and providing scientific suggestions for the personalized development of the children. According to the children personalized behavior statistical analysis method, the special ability of some children who do not reach the standard can be discovered, or the potential ability of personalized children can be explored, so that scientific suggestions and guidance directions suitable for personalized development of children can be given; and in cooperation with an enterprise, an education product is developed according to the predicted personalized behavior preference result of the children.

Description

technical field [0001] The invention belongs to the technical field of children's individualized behavior analysis, in particular to a system and method for statistically analyzing children's individualized behavior based on a latent variable model. Background technique [0002] At present, the closest existing technology: In recent years, the research results of preschool education and primary education in my country have been quite remarkable, and the research on children's personality education is an important interdisciplinary research field of pedagogy, psychology, and behavior. attention and in-depth discussion. The Eysenck Personality Questionnaire was used to explore the influence of parental education style and student personality on primary school students' academic performance. Personality development, personality education, personality training. New ideas for building personalized classrooms. Wu Fati and Mou Zhijia proposed to build a personalized behavior anal...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/174G06V20/52G06F18/241
Inventor 邹杨韦鹏程冉维
Owner CHONGQING UNIV OF EDUCATION
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