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eISSN
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# Analysis and Research on Influencing Factors of Ideological and Political Education Teaching Effectiveness Based on Linear Equation

###### Przyjęty: 11 May 2022
Informacje o czasopiśmie
Format
Czasopismo
eISSN
2444-8656
Pierwsze wydanie
01 Jan 2016
Częstotliwość wydawania
2 razy w roku
Języki
Angielski
Linear equations
solution
Definition 1

Xj is the unknown, AIj is the coefficient, bi is the constant term. Suppose x1=c1, x2=c2... And xn=cn can be substituted into the equation given, then (c1, c2... , cn) is regarded as a solution. If C1, C2,... Cn is not all zero, then (c1, c2... , cn) as a non-zero solution. Assuming that the constant terms are zero, we can call it a homogeneous linear system. In two equations, if the number of unknowns is the same and the solution set is the same, it can be regarded as a co-solution system. This paper uses linear equation to study the influencing factors of ideological and political education in colleges and universities, mainly discusses three problems: first, equations and oil elder sisters, secondly, the number of solving equations, and finally solving equations.

Common solutions are divided into two kinds. One is Clem's rule, which uses inverse matrix to solve linear equations. However, due to the large amount of actual work, it is only used for theoretical analysis. The other is the matrix elimination method, which transforms the augmented matrix of linear equations into a simplified row ladder matrix by using the elementary of rows, which represents the statistics of the linear equations and the original equations of the augmented matrix. In the case that the equations have solutions, the unknown quantities corresponding to the unit column vectors can be regarded as non-free unknowns, and the other unknowns will be regarded as free unknowns, so the solution of the linear equation can be obtained.

Generally speaking, the first-order equations of unknown quantities are in the following form: $a11x1+a12x2+…+a1nxn=b1,a21x1+a22x2+…+a2nxn=b2,⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯am1x1+am2x2+…+amnxn=bm}$ \left. \matrix{{a_{11}}{x_1} + {a_{12}}{x_2} + \ldots + {a_{1n}}{x_n} = {b_1}, \hfill \cr {a_{21}}{x_1} + {a_{22}}{x_2} + \ldots + {a_{2n}}{x_n} = {b_2}, \hfill \cr \cdots \cdots \cdots \cdots \cdots \cdots \cdots \cdots \cdots \cdots \cdots \hfill \cr {a_{m1}}{x_1} + {a_{m2}}{x_2} + \ldots + {a_{mn}}{x_n} = {b_m} \hfill \cr} \right\}

Theorem 1

IIn the above formula, x1, x2,... , xn represents unknown quantity, AIj (1≤ I ≤m, 1≤j≤n) represents equation coefficient, bi (1≤ I ≤m) represents constant term. Coefficients and constant terms belong to all complex numbers or to elements in a field.

In constant terms B1, B2... When bn and bn are all equal to zero, the system of equations is called homogeneous linear equations, and the matrix of m rows and n columns formed by the coefficients is shown as follows: $A=[a11a12…a1na21a22…a2n⋮⋮⋮am1am2⋯amn]$ A = \left[{\matrix{{{a_{11}}} & {{a_{12}} \ldots} & {{a_{1n}}} \cr {{a_{21}}} & {{a_{22}} \ldots} & {{a_{2n}}} \cr \vdots & \vdots & \vdots \cr {{a_{m1}}} & {{a_{m2}} \cdots} & {{a_{mn}}} \cr}} \right]

The above formula represents the coefficient matrix of the system. Add columns with constant terms in A to obtain m row n+1 column matrix, as shown below: $A¯=[a11a12⋯a1nb1a21a22⋯a2nb2⋮⋮⋮⋮am1am2⋯amnbm]$ \bar A = \left[{\matrix{{{a_{11}}} & {{a_{12}} \cdots} & {{a_{1n}}} & {{b_1}} \cr {{a_{21}}} & {{a_{22}} \cdots} & {{a_{2n}}} & {{b_2}} \cr \vdots & \vdots & \vdots & \vdots \cr {{a_{m1}}} & {{a_{m2}} \cdots} & {{a_{mn}}} & {{b_m}} \cr}} \right]

The above equations represent the augmented matrix.

Proposition 2

The study of linear equations shows that there are two main results: one is that the necessary conditions for the existence of solutions in linear equations are that the coefficient matrix and the augmented matrix have the same rank; The other is that when the rank of A and is greater than zero, A has A sub-formula D of order R that is not equal to zero, assuming: $D=[a11a12⋯a1ra21a22⋯a2r⋮⋮ar2ar2⋯arr]≠0$ D = \left[{\matrix{{{a_{11}}} & {{a_{12}}} & \cdots & {{a_{1r}}} \cr {{a_{21}}} & {{a_{22}}} & \cdots & {{a_{2r}}} \cr \vdots & \vdots & {} & {} \cr {{a_{r2}}} & {{a_{r2}}} & \cdots & {{a_{rr}}} \cr}} \right] \ne 0

At this point, the equation set and the equation set containing only the first R equations have the same solution, so the first R equations can be rewritten as: $a11x1+a12x2+…+a1rxr=b1−a1r+1xr+1−…−a1nxn,a21x1+a22x2+…+a2rxr=b2−a2r+1xr+1−…−a2nxn,⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯ar1x1+ar2x2+…+arrxr−br−arr+1xr+1−…−arnxn}$ \left. \matrix{{a_{11}}{x_1} + {a_{12}}{x_2} + \ldots + {a_{1r}}{x_r} = {b_1} - {a_{1r + 1}}{x_{r + 1}} - \ldots - {a_{1n}}{x_n}, \hfill \cr {a_{21}}{x_1} + {a_{22}}{x_2} + \ldots + {a_{2r}}{x_r} = {b_2} - {a_{2r + 1}}{x_{r + 1}} - \ldots - {a_{2n}}{x_n}, \hfill \cr \cdots \cdots \cdots \cdots \cdots \cdots \cdots \cdots \cdots \cdots \cdots \cdots \cdots \cdots \cdots \cdots \cdots \cdots \cdots \hfill \cr {a_{r1}}{x_1} + {a_{r2}}{x_2} + \ldots + {a_{rr}}{x_r} - {b_r} - {a_{rr + 1}}{x_{r + 1}} - \ldots - {a_{rn}}{x_n} \hfill \cr} \right\}

The calculation formula of the above equation is: $X1=D1/D,x2=D2/D,…,xr=Dr/D$ {\rm{X}}1 = {\rm{D}}1/{\rm{D}},{\rm{x}}2 = {\rm{D}}2/{\rm{D}}, \ldots,{\rm{xr}} = {\rm{Dr}}/{\rm{D}}

In the above formula, Dj (j= 1,2... , r) Replace the JTH column of D with an r-order determinant obtained from the right end column of the system, as shown below: $Dj=|a11…b1−G1r+1xr+1−…−G1nxn…a1ra21…b2−Grr+1xr+1−…−G2nxn…a2r.................................................................ar1…bn−arr+1xr+1−…−Grnxn…arr︸(The first column j)|$ {D_j} = \left| {\matrix{{{a_{11}} \ldots {b_1} - {G_{1r + 1}}{x_{r + 1}} - \ldots - {G_{1n}}{x_n} \ldots {a_{1r}}} \cr {{a_{21}} \ldots {b_2} - {G_{rr + 1}}{x_{r + 1}} - \ldots - {G_{2n}}{x_n} \ldots {a_{2r}}} \cr {.........................................} \cr {\underbrace {{a_{r1}} \ldots {b_n} - {a_{rr + 1}}{x_{r + 1}} - \ldots - {G_{rn}}{x_n} \ldots {a_{rr}}}_{\left({{The}\,{first}\,{column}\,{j}} \right)}} \cr}} \right|

In the above formula, x1, x2,... , xn represents the rest of the unknowns, xr+1, xr+2... Xn stands for linear free as mass. Under the condition of r Under the condition of r=n, the equations do not contain free unknowns, so the unique solution of the equations should be obtained by combining the above research. Under the condition of m=n=r, the above formula can be regarded as Clem's rule.

Equation Model
Lemma 3

Since there are many influencing factors when constructing the research model of ideological and political education teaching effectiveness based on linear equation, multiple linear regression model should be used for deep analysis.

Suppose some dependent variable y is dependent on k independent variables x1, x2... And xN, where the observed values of group N are (YA, X1A, X2a... , xka), then the structural equation of the multiple linear regression model is shown as follows: $ya=βo+β1x1a+β2x2a+…+βkxka+εa$ {y_a} = {\beta _o} + {\beta _1}{x_{1a}} + {\beta _2}{x_{2a}} + \ldots + {\beta _k}{x_{ka}} + {\varepsilon _a}

In the above formula, β0, β1,..., βk represents undetermined parameter, εa represents random variable, and conforms to a= 1,2... N.

Suppose b0, B1,... Bk stands for β0, β1,..., βk fitting value, then the corresponding regression equation is: $y^=b0+b1x1+b2x2+…+bkxk$ \hat y = {b_0} + {b_1}{x_1} + {b_2}{x_2} + \ldots + {b_k}{x_k}

Corollary 4

In the above formula, B0 stands for constants, b1, B2... Bk stands for partial regression coefficient.

Combining with the least square method, (I = 0,1,2... The estimated value of k should be bi (I = 0,1,2... , k), and get: $Q=∑a=1n(ya−y^a)2=∑a=1n[ya−(b0+b1x1a+b2x2a+…+bkxka)]2→min$ Q = \sum\limits_{a = 1}^n {{{\left({{y_a} - {{\hat y}_a}} \right)}^2} = \sum\limits_{a = 1}^n {{{\left[{{y_a} - \left({{b_0} + {b_1}{x_{1a}} + {b_2}{x_{2a}} + \ldots + {b_k}{x_{ka}}} \right)} \right]}^2} \to \min}}

The necessary conditions for calculating extreme values are: ${∂Q∂b0=−2∑a=1n(ya−y^a)=0∂Q∂bj=−2∑a=1n(ya−y^a)xja=0(j=1,2,…,k)$ \left\{\matrix{{{\partial Q} \over {\partial {b_0}}} = - 2\sum\limits_{a = 1}^n {\left({{y_a} - {{\hat y}_a}} \right) = 0} \hfill \cr {{\partial Q} \over {\partial {b_j}}} = - 2\sum\limits_{a = 1}^n {\left({{y_a} - {{\hat y}_a}} \right){x_{ja}} = 0\left({j = 1,2, \ldots,k} \right)} \hfill \cr} \right.

After sorting out the above equations, we can get: $[nb0+(∑a=1nx1a)b1+(∑a=1nx2a)b2+…+(∑a=1nxkn)bk+∑a=1nya(∑a=1nx1a)b0+(∑a=1nx1a2)b1+(∑a=1nx1ax2a)b2+…+(∑a=1nx1axka)bk=∑a=1nx1aya(∑a=1nx2n)b0+(∑a=1nx1ax2a)b1+(∑a=1nx2n2)b2+…+(∑a=1nx2axka)bk=∑a=1nx2aya(∑a=1nxka)b0+(∑a=1nx1axka)b1+(∑a=1nx2axka)b2+…+(∑a=1nxkn2)bk=∑a=1nxkaya$ \left[\matrix{n{b_0} + \left({\sum\limits_{a = 1}^n {{x_{1a}}}} \right){b_1} + \left({\sum\limits_{a = 1}^n {{x_{2a}}}} \right){b_2} + \ldots + \left({\sum\limits_{a = 1}^n {{x_{kn}}}} \right){b_k} + \sum\limits_{a = 1}^n {{y_a}} \hfill \cr \left({\sum\limits_{a = 1}^n {{x_{1a}}}} \right){b_0} + \left({\sum\limits_{a = 1}^n {x_{1a}^2}} \right){b_1} + \left({\sum\limits_{a = 1}^n {{x_{1a}}{x_{2a}}}} \right){b_2} + \ldots + \left({\sum\limits_{a = 1}^n {{x_{1a}}{x_{ka}}}} \right){b_k} = \sum\limits_{a = 1}^n {{x_{1a}}{y_a}} \hfill \cr \left({\sum\limits_{a = 1}^n {{x_{2n}}}} \right){b_0} + \left({\sum\limits_{a = 1}^n {{x_{1a}}{x_{2a}}}} \right){b_1} + \left({\sum\limits_{a = 1}^n {x_{2n}^2}} \right){b_2} + \ldots + \left({\sum\limits_{a = 1}^n {{x_{2a}}{x_{ka}}}} \right){b_k} = \sum\limits_{a = 1}^n {{x_{2a}}{y_a}} \hfill \cr \left({\sum\limits_{a = 1}^n {{x_{ka}}}} \right){b_0} + \left({\sum\limits_{a = 1}^n {{x_{1a}}{x_{ka}}}} \right){b_1} + \left({\sum\limits_{a = 1}^n {{x_{2a}}{x_{ka}}}} \right){b_2} + \ldots + \left({\sum\limits_{a = 1}^n {x_{kn}^2}} \right){b_k} = \sum\limits_{a = 1}^n {{x_{ka}}{y_a}} \hfill \cr} \right.

The above equations are called normal equations. Assuming that a vector and matrix are introduced, it can be obtained: $b=(b0b1b2…bk),Y=(y1y2…yn),X=(1x11x21⋯xk11x12x22⋯xk21x13x23⋯xk3...................................1x1nx2n⋯xkn)$ b = \left({\matrix{{{b_0}} \cr {{b_1}} \cr {{b_2}} \cr \ldots \cr {{b_k}} \cr}} \right),Y = \left({\matrix{{{y_1}} \cr {{y_2}} \cr \ldots \cr {{y_n}} \cr}} \right),X = \left({\matrix{{\matrix{1 & {{x_{11}}} & {{x_{21}}} & \cdots & {{x_{k1}}} \cr 1 & {{x_{12}}} & {{x_{22}}} & \cdots & {{x_{k2}}} \cr 1 & {{x_{13}}} & {{x_{23}}} & \cdots & {{x_{k3}}} \cr}} \cr {\matrix{{......................} \cr {\matrix{1 & {{x_{1n}}} & {{x_{2n}}} & \cdots & {{x_{kn}}} \cr}} \cr}} \cr}} \right) $A=XTX=(111⋯1x11x12x13⋯x1nx21x22x23⋯x2n................................xk1xk2xk3…xkn)(1x11x21⋯xk11x12x22⋯xk21x13x23⋯xk3.............................1x1nx2n⋯xkn)$ A = {X^T}X = \left({\matrix{{\matrix{1 & 1 & 1 & \cdots & 1 \cr {{x_{11}}} & {{x_{12}}} & {{x_{13}}} & \cdots & {{x_{1n}}} \cr {{x_{21}}} & {{x_{22}}} & {{x_{23}}} & \cdots & {{x_{2n}}} \cr}} \cr {\matrix{{.........................} \cr {\matrix{{{x_{k1}}} & {{x_{k2}}} & {{x_{k3}}} & \ldots & {{x_{kn}}} \cr}} \cr}} \cr}} \right)\left({\matrix{{\matrix{1 & {{x_{11}}} & {{x_{21}}} & \cdots & {{x_{k1}}} \cr 1 & {{x_{12}}} & {{x_{22}}} & \cdots & {{x_{k2}}} \cr 1 & {{x_{13}}} & {{x_{23}}} & \cdots & {{x_{k3}}} \cr}} \cr {\matrix{{......................} \cr {\matrix{1 & {{x_{1n}}} & {{x_{2n}}} & \cdots & {{x_{kn}}} \cr}} \cr}} \cr}} \right) $B=XTY=(111⋯1x11x12x13⋯x1nx21x22x23⋯x2n................................xk1xk2xk3…xkn)(y1y2y3⋯yn)(∑a=1nya∑a=1nx1aya∑a=1nx2aya∑a−1nxkaya)$ B = {X^T}Y = \left({\matrix{{\matrix{1 & 1 & 1 & \cdots & 1 \cr {{x_{11}}} & {{x_{12}}} & {{x_{13}}} & \cdots & {{x_{1n}}} \cr {{x_{21}}} & {{x_{22}}} & {{x_{23}}} & \cdots & {{x_{2n}}} \cr}} \cr {\matrix{{........................} \cr {\matrix{{{x_{k1}}} & {{x_{k2}}} & {{x_{k3}}} & \ldots & {{x_{kn}}} \cr}} \cr}} \cr}} \right)\left({\matrix{{{y_1}} \cr {{y_2}} \cr {{y_3}} \cr \cdots \cr {{y_n}} \cr}} \right)\left({\matrix{{\sum\limits_{a = 1}^n {{y_a}}} \cr {\sum\limits_{a = 1}^n {{x_{1a}}{y_a}}} \cr {\sum\limits_{a = 1}^n {{x_{2a}}{y_a}}} \cr {\sum\limits_{a - 1}^n {{x_{ka}}{y_a}}} \cr}} \right)

Conjecture 5. Transforming the normal equations into matrix form, it can be obtained: $Ab=B$ {\rm{Ab}} = {\rm{B}}

The actual solution is: $b=A−1B=(XTX)−1XTY$ b = {A^{- 1}}B = {\left({{X^T}X} \right)^{- 1}}{X^T}Y

Suppose the notation is introduced: $Lij=Lji=∑a=1n(xia−x¯i)(xja−x¯j)(i,j=1,2,…,k)$ {L_{ij}} = {L_{ji}} = \sum\limits_{a = 1}^n {\left({{x_{ia}} - {{\bar x}_i}} \right)\left({{x_{ja}} - {{\bar x}_j}} \right)\left({i,j = 1,2, \ldots,k} \right)} $Lij=∑a=1n(xia−x¯i)(ya−y¯)(i=1,2,…,k)$ {L_{ij}} = \sum\limits_{a = 1}^n {\left({{x_{ia}} - {{\bar x}_i}} \right)\left({{y_a} - \bar y} \right)\left({i = 1,2, \ldots,k} \right)}

Then the normal equations can also be shown as follows: ${L11b1+L12b2+…+L1kbk=L1yL21b1+L22b2+…+L2kbk=L2y..............................................Lk1b1+Lk2b2+…+Lkkbk=Lkyb0=y¯−b1x¯1−b2x¯2−…−bkx¯k$ \left\{\matrix{{L_{11}}{b_1} + {L_{12}}{b_2} + \ldots + {L_{1k}}{b_k} = {L_{1y}} \hfill \cr {L_{21}}{b_1} + {L_{22}}{b_2} + \ldots + {L_{2k}}{b_k} = {L_{2y}} \hfill \cr................................. \hfill \cr {L_{k1}}{b_1} + {L_{k2}}{b_2} + \ldots + {L_{kk}}{b_k} = {L_{ky}} \hfill \cr {b_0} = \bar y - {b_1}{{\bar x}_1} - {b_2}{{\bar x}_2} - \ldots - {b_k}{{\bar x}_k} \hfill \cr} \right.

Example 6

Using multiple linear regression model to study the effectiveness of ideological and political education teaching needs to be tested in empirical research. Because the change of observed value of dependent variable is caused by two factors, on the one hand, the value of independent variable is different, on the other hand, it changes under the influence of other random factors, so in order to effectively distinguish, anOVA should be conducted on regression model, the specific formula is as follows: $Sr=Lyy=U+Q$ {S_r} = {L_{yy}} = U + Q

In the above formula, Sr represents the sum of the squares of deviations, Lyy represents the regression model, U represents the sum of the squares of regression, and Q represents the sum of the remaining squares.

In the analysis of multiple linear regression, the sum of regression squares represents the overall influence of all K variables on the variation of Y, and the actual formula is as follows: $U=∑a=1n(y^a−y¯)2=∑i=1kbiLiy$ U = \sum\limits_{a = 1}^n {{{\left({{{\hat y}_a} - \bar y} \right)}^2} = \sum\limits_{i = 1}^k {{b_i}{L_{iy}}}}

The formula for calculating the sum of the remaining squares is as follows: $Q=∑a=1n(ya−y^a)2=Lyy−U$ Q = \sum\limits_{a = 1}^n {{{\left({{y_a} - {{\hat y}_a}} \right)}^2} = {L_{yy}} - U}

At the same time, in multiple linear regression analysis, the degrees of freedom of the sum of squares are different. The degrees of freedom of the sum of squares of regression and U are equal to the number of independent variable K, so the degrees of freedom of the remaining sum of squares are m-K-1. The calculation formula of F statistic is as follows: $F=U/KQ/(n−k−1)$ F = {{U/K} \over {Q/\left({n - k - 1} \right)}}

After the statistics are defined, significance test analysis can be performed on the equation model according to the distribution results of F.

Analysis of influencing factors of ideological and political education teaching effectiveness based on linear equation
Research Hypothesis

In essence, the effectiveness of ideological and political education teaching refers to the attraction and influence of various activities on students during practical teaching. By integrating and referring to the research results proposed by current research scholars, this paper analyzes the effectiveness of ideological and political education from three perspectives: knowledge mastery, teaching effect and overall satisfaction, and puts forward the following three hypotheses: [1]First, students have a positive impact on the effectiveness of ideological and political education teaching; Secondly, students have a positive influence on ideological and political education teaching factors; Finally, students have a positive influence on the teacher factor of ideological and political education teaching. [2.3]

Research Design

Using modern network technology and thematic questionnaire survey method, this paper conducts research and analysis on 26 universities in many provinces of China, among which school types include double first-class universities, provincial key universities, ordinary undergraduate, key higher vocational colleges and ordinary higher vocational colleges. Finally, 2,021 valid questionnaires are obtained. [4]

Combined with the above analytical process of multiple linear equations, a structural model is constructed to study the teaching effectiveness of ideological and political education and its influencing factors, as shown in Figure 1 below [5]:

Research Methods

The structural equation model is shown below: $E[η]=Bη+Γξ+ξ$ E\left[\eta \right] = B\eta + \Gamma \xi + \xi

Observe the condition E(η) = 0, E(ξ) = 0, E(ζ) = 0, η and ζ do not interfere with each other.

In the above formula, E(η) represents the expectation of the latent variable, ξ represents the latent variable of internal cause, ζ represents the latent variable of external cause, ξ represents residual error, and B represents the structural coefficient matrix of the latent variable of internal cause, which refers to the linear relationship between the latent variables of internal cause. Γ represents the structural coefficient matrix of latent variables of external factors, presenting a linear relationship between ζ and η.

Thus, the measurement model of factors influencing the effectiveness of ideological and political education is clarified, and the relationship between latent variables and index variables is shown. It is mainly divided into two aspects: on the one hand, the exogenous latent variable ζ should be measured; on the other hand, the endogenous latent variable η should be measured. The specific formula is as follows [6]: $X=Λxξ+δγ=Λyη+ε$ \eqalign{& X = \Lambda x\xi + \delta \cr & \gamma = \Lambda y\eta + \varepsilon \cr}

The above formula meets the condition of E(η) = 0, E(ξ) = 0, E(ε) = 0; ε and η, and there is no relationship between the variables. Where X represents the observation index of η, R represents the observation index of η, ε represents the measurement error of X, ε represents the measurement error of R, Λx refers to the compound quantity of external shallow variables, which is composed of and of X's factors on ξ; Λy represents the compound quantity of internal shallow variables, consisting of the factor load of R on η.

Result analysis
Data Analysis

The equation model proposed in this paper and statistical software AMOS 21.0 are used to verify and analyze the model equation proposed in the figure above, and the output results are finally obtained as shown in Figure 2 below:

According to the above analysis, it is found that in the equation model, the practical effect of ideological and political teaching (1), leading factor (2), main factor (1) and teaching factor (3) are latent variables, which do not have negative error variance, and the standard coefficient is controlled between 0.77 and 0.96. At the same time, the model equations were evaluated and analyzed in combination with the evaluation indexes of the three statistics of absolute adaptation, value-added adaptation and reduced adaptation, and the final results were found to meet the judgment criteria, as shown in Table 1 below [7]:

Fitting results of the equation model

GFI AGFI RMSEA NFI IFI REI PNFI PGFI λ2/df
The initial model 0.897 0.833 0.038 0.942 0.944 0.920 0.685 0.686 2.389
GFI AGFI RMSEA NFI IFI REI PNFI PGFI λ2/df
The judgment standard >0.8 >0.8 >0.08 >0.8 >0.8 >0.8 >0.5 >0.5 It's between one and five
Impact Analysis

According to the research results of equation model, external factor variables can be regarded as the direct effect, while internal factor variables can be regarded as the indirect effect. According to the output results of Figure 2, the influence of student factors on the effectiveness of ideological and political education is direct, and the actual value can reach 0.16. The influence of teacher factors on the effectiveness of ideological and political education is also direct, and the actual value can reach 0.23. The influence of teaching factors on the effectiveness of ideological and political education is also direct, and the actual value can reach 0.68. Student factor also has direct influence on teacher factor, the actual value is 0.41; Students also have a direct influence on teaching factors. The actual value is 0.15 and the influence of good teachers is also direct, and the actual value can reach 0.60 [8].

From the perspective of indirect effects, if exogenous variables do not directly affect endogenous variables, then intermediary variables are bound to have an impact. Among them, the student factor can have an indirect impact on the effectiveness of ideological and political education. The actual path can be divided into two ways: one is based on the teacher factor, and the actual value is 0.094; The other is based on teaching factors, and the actual value is 0.102. A comprehensive study of these two indirect influence paths shows that the value of students' indirect influence on the effectiveness of ideological and political education is 0.196. The indirect influence path of teacher factors on the effectiveness of ideological and political education should be realized with teaching factors as the core, and the actual value is 0.408.

From the perspective of overall influence, the total influence value of student factors on the effectiveness of ideological and political education can reach 0.356; The total influence of teachers on the effectiveness of ideological and political education can reach 0.683; The total influence of teaching factors on the effectiveness of ideological and political education can reach 0.68. It can be seen that the ranking of the three factors is teaching, teachers and students. The specific results are shown in Table 2 below: [9]

Analysis of influencing factors and effects of ideological and political education's effectiveness

The student factors Teacher factors Teaching factors
Direct effect Teacher factors 0.41*** --- ---
Teaching factors 0.15*** --- ---
Practical effect of ideological and political teaching in colleges and universities 0.16*** 0.23*** 0.68***
The indirect effect Teacher factors --- --- ---
Teaching factors --- --- ---
Practical effect of ideological and political teaching in colleges and universities 0.196*** 0.408*** ---
The total effect The student factors Teacher factors Teaching factors
Practical effect of ideological and political teaching in colleges and universities 0.356*** 0.638*** 0.68***

Note:

P<0.08,

P<0.05,

P<0.01

Conclusion Analysis

According to the above research, the hypothesis verification results are shown in Table 3 below, which proves that student factors have a positive and direct impact on the effectiveness of ideological and political education, and can have a positive and indirect impact on the effectiveness of education by directly affecting teachers and teaching. At the same time, the teacher factor has two influences on the practical effect of ideological and political education, one is direct and the other is indirect. Teaching factors will have a positive and direct impact on teaching effectiveness [10.11].

Research hypothesis and verification results

Research hypothesis Assume the specific content The verification results
H1 The student factor has a positive influence on the practical effect of ideological and political course teaching through
H2 Student factors have a positive influence on the teaching factors of ideological and political subject teaching in colleges and universities through
H3 Student factors have a positive influence on the teaching factors of ideological and political subject teaching in colleges and universities through
H4 The student factor has a positive influence on the practical effect of ideological and political course teaching through
H5 Student factors have a positive influence on the teaching factors of ideological and political subject teaching in colleges and universities through
H6 The student factor has a positive influence on the practical effect of ideological and political course teaching through

Meanwhile, this paper also conducted investigation and research on how to improve the effectiveness of ideological and political education, and the final results are shown in the following table:

Survey results of improving the effectiveness of teaching

move frequency Proportion / %
Enrich teaching content 1255 26.70
Strengthen classroom management 596 12.68
Enhance classroom interaction 841 17.89
Introducing current events 942 20.04
Change the existing thematic teaching methods 396 8.43
Arrange students to teach 99 2.11
Organize online group discussions 326 6.94

According to the survey results in the table above, ideological and political education in colleges and universities should be carried out from the following points: First of all, classroom teaching content should be continuously enriched, and scientific adjustment should be made according to the learning needs and abilities of teachers and students in colleges and universities. According to the survey data, 26.7% of students think that ideological and political education needs to be enriched, and the research results of this paper prove that the influence value of teaching content on teaching factors can reach 0.90. Therefore, it is necessary not only to apply diversified teaching cases during classroom teaching, but also to expand the classroom learning path according to the needs of the development of The Times. Secondly, it is necessary to innovate ideological and political education model, get rid of the limitation of traditional teaching concept, and pay attention to the construction of targeted and interesting thematic teaching platform for college teachers and students, so as to generate knowledge and emotional resonance in the interaction and communication. Finally, we should pay attention to the effective optimization of teaching organization and teaching atmosphere, and build a teaching symbiotic mechanism based on the needs of ideological and political education in colleges and universities. Under new era background, platform based on Internet technology constantly optimize the traditional ideological education mode, the correct understanding of mechanism of sharing information resources, information processing, virtual communication content such as the impact on the modern education reform and requirements, in college ideological education guidance during the formation of thinking, interaction, experience of integration of learning mode, build the good learning atmosphere, Pass on excellent ideological and political culture. [12]

Conclusion

To sum up, the linear equation is used to construct a research model for influencing factors of the timeliness of ideological and political education and teaching, so as to systematically understand the guidance status and relevant values of ideological and political education in colleges and universities in recent years. On the basis of clarifying the main influencing factors, the main reasons affecting the timeliness of education and teaching are deeply explored. This article research results show that three factors are students, teachers, teaching has direct and indirect impact on timeliness, so in the education reform should start from building with pertinence and diversity of classroom teaching, active application of advanced means of education and science and technology, only in this way can truly meet the demand of students and education education innovation.

#### Fitting results of the equation model

GFI AGFI RMSEA NFI IFI REI PNFI PGFI λ2/df
The initial model 0.897 0.833 0.038 0.942 0.944 0.920 0.685 0.686 2.389
GFI AGFI RMSEA NFI IFI REI PNFI PGFI λ2/df
The judgment standard >0.8 >0.8 >0.08 >0.8 >0.8 >0.8 >0.5 >0.5 It's between one and five

#### Research hypothesis and verification results

Research hypothesis Assume the specific content The verification results
H1 The student factor has a positive influence on the practical effect of ideological and political course teaching through
H2 Student factors have a positive influence on the teaching factors of ideological and political subject teaching in colleges and universities through
H3 Student factors have a positive influence on the teaching factors of ideological and political subject teaching in colleges and universities through
H4 The student factor has a positive influence on the practical effect of ideological and political course teaching through
H5 Student factors have a positive influence on the teaching factors of ideological and political subject teaching in colleges and universities through
H6 The student factor has a positive influence on the practical effect of ideological and political course teaching through

#### Survey results of improving the effectiveness of teaching

move frequency Proportion / %
Enrich teaching content 1255 26.70
Strengthen classroom management 596 12.68
Enhance classroom interaction 841 17.89
Introducing current events 942 20.04
Change the existing thematic teaching methods 396 8.43
Arrange students to teach 99 2.11
Organize online group discussions 326 6.94

#### Analysis of influencing factors and effects of ideological and political education's effectiveness

The student factors Teacher factors Teaching factors
Direct effect Teacher factors 0.41*** --- ---
Teaching factors 0.15*** --- ---
Practical effect of ideological and political teaching in colleges and universities 0.16*** 0.23*** 0.68***
The indirect effect Teacher factors --- --- ---
Teaching factors --- --- ---
Practical effect of ideological and political teaching in colleges and universities 0.196*** 0.408*** ---
The total effect The student factors Teacher factors Teaching factors
Practical effect of ideological and political teaching in colleges and universities 0.356*** 0.638*** 0.68***

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