The Inheritance and Innovation of Folk Culture in Visual Communication Design Education

Applying traditional folk culture expression forms to visual communication design can not only better protect folk intangible culture and provide brand-new ideas for design teaching, but also guide students to create art according to traditional folk culture, which is an important way to deeply excavate traditional folk culture works and give life to design works. In the process of practical application, firstly, students should analyze the characteristics and shapes of the design works according to their own impressions, and then the lecturer will guide and summarize them to effectively and quickly improve the students’ knowledge and understanding of the expression forms of the traditional folk culture, and finally let the students apply these graphics, colors and shapes to the design works to form a diversified expression form, and this kind of transformation from in-depth understanding to application design can subconsciously enhance the students’ understanding of the traditional folk culture. This transformation from in-depth understanding to application design can subconsciously improve students’ visual communication art level.

As the most direct way of expression in design, pattern elements are also loved by the general public. This requires design teachers to guide students to understand the principles of pattern symbols, creation techniques and theoretical knowledge as much as possible in the actual visual communication art teaching, and apply these contents to the actual operation, so that students can learn diversified visual communication art skills. For example, in art modeling, the rationality and standardization of the use of pattern colors can show the infectious force of visual communication art to the greatest extent, and also let students understand plants, animals and other contents in depth. In addition, strengthening students’ transformation of folklore patterns can also enable students to acquire more ability to change color and structure, and improve the effect of classroom teaching.

Most of the traditional folk cultures take the inheritance system as the main way of continuation. With the introduction of traditional folk culture inheritors as the premise, through their living teaching, it can make students majoring in visual communication design in colleges and universities better understand the original ecological fine arts. For example, the current decline in the use of Zhuang brocade fabrics has led to the gradual disappearance of this traditional folk material, and in the current situation, people can’t get in touch with this traditional content, which requires colleges and universities to combine the modern culture and traditional national characteristics, and introduce some traditional craftsmen into the college and university design classroom lectures, exchanges, or let them serve as part-time teachers and other ways, so that college and university students in the field of design can understand the more original art content. This will not only help students more directly experience the charm of traditional folk culture, but also deepen their knowledge of national culture and enhance the recognition of China’s traditional culture.

Latent variables that are not directly observable or theoretical variables are called latent variables.

A latent variable with an exogenous latent variable as the cause is called an exogenous latent variable and is denoted by the symbol ξ .

A latent variable with an endogenous latent variable as its effect is called an endogenous latent variable and is denoted by the symbol η .

Observed variables can be observed by the value of the actual indicator, often used to reflect the latent variable, in which the observed variable of the exogenous latent variable is denoted by the symbol X, and the observed variable of the endogenous latent variable is denoted by the symbol Y.

Mediating variable A potential variable is not only affected by the exogenous potential variable (at this time, the variable attribute is the endogenous potential variable), but also affects other variables (at this time, the variable attribute is the exogenous potential variable), at this time, this variable has the attribute of the exogenous potential variable as well as the attribute of the endogenous potential variable, and such a variable is a mediating variable. An example of a mediating variable is shown in Figure 1, where the endogenous latent variable 1 is the mediating variable, taking the three latent variables as an example.

Figure 1.

Schematic diagram of intermediary variables

Unstandardized path coefficients portray the quantitative relationship between observed and latent variables.

Standardized path coefficients describe the strength of the effect of observed variables on latent variables and exogenous latent variables on endogenous latent variables.

The direct effect effect is the direct effect of an exogenous latent variable on an endogenous latent variable in a one-way arrow, with no mediating variable, and the magnitude of the value is the standardized regression coefficient β value.

Indirect effect effect the effect of an exogenous latent variable on an endogenous latent variable through more than one mediating variable, the magnitude of the value is equal to the product obtained by multiplying the path coefficients of all direct effects.

The total effect value direct effect value plus the indirect effect value.

For example, if X₁ is the exogenous latent variable, X₂ and X₃ are the mediating variables, and X₄ is the endogenous latent variable, the effect of each variable is shown in Figure 2, and the value of the effect of each of the effects of X₁ on X₄ is shown in Table 1:

Figure 2.

Schematic diagram of impact effect

The mathematical expression for the measurement model is: (1) X=Λxξ+δ(Exogenous latent variable equations) (2) Y=Λ,η+ε(Endogenous latent variable equations) where ξ is a vector of order n×1 consisting of n exogenous latent variables; X is a vector of order q×1 consisting of q observed variables of exogenous latent ξ variables; and Λ_x denotes the coefficients of the linking X variables on the ξ variables, an q×n th order matrix; δ is a vector of order q×1 consisting of the q measurement errors of X; η is a vector of order m×1 consisting of the m observed variables of the endogenous latent variable, Y is a vector of order p×1 consisting of the p observed variables of the endogenous latent variable η ; Λ_y represents the coefficients of the linking Y variables on the variables of η the p×m th-order matrix; and ε is a vector of order p×1 consisting of the p measurement errors of Y the endogenous latent variable.

Mathematical expression of the structural model (3) η=Bη+Γξ+ζ Where ξ and η have the same meaning as above, B denotes the directional link coefficients between the η variables, a m×m th order matrix describing the interactions between the endogenous latent variables η ; Γ denotes the regression coefficients for the effect of the ξ variables on the η variables, a m×n th order matrix describing the effect of the exogenous latent variable ξ on the endogenous latent variable η ; and ζ denotes the error on the endogenous latent variable, a m×1th order vector.

The above occurrences of Λ_x, Λ_y, B_n and Γ all refer to unstandardized path coefficients.

The correlation analysis is mainly carried out to analyze whether the two two variables are correlated or not. Considering the characteristics of the variables in this analysis, Pearson’s correlation was mainly used to analyze, mainly considering the following points, firstly, whether it is correlated or not, mainly through the P-value, when the P-value is less than 0.05, it can be regarded as correlation between two and two variables, when the value is greater than 0.5, it can be regarded as irrelevant, and then it’s the question of whether the correlation is positive or negative, when the value of the coefficient is greater than 0, it is positive or negative, when the value is less than 0, it is negative. When the value of the coefficient is greater than 0, it means positive correlation, and less than 0, it means negative correlation, the strength of the degree of correlation is mainly based on the size of the absolute value of the coefficient, when the absolute value of the coefficient is closer to 1, it means that the stronger the correlation, and the closer the coefficient is to 0, it means that the weaker the correlation. The results of the correlation analysis of Pearson are shown in Table 9. Through the correlation analysis, it can be seen that morphological plurality, elemental integration, living teaching and teaching effectiveness are correlated, and all of them are positively correlated. Specifically, the correlation coefficients of morphological pluralism, elemental integration, living teaching and teaching effectiveness are 0.263, 0.361, 0.495, respectively, with P<0.05, so that morphological pluralism, elemental integration, living teaching and teaching effectiveness are related and all are positively correlated. As can be seen through the discriminant validity values, the values in the top row are all higher than the absolute value of the values below, indicating that the discriminant validity is better.