Exploring the Path of Cultivating College Students’ Sense of Moral Responsibility Based on Social Practice

The state attaches great importance to the cultivation of college students’ social responsibility, and college students, as the fresh blood to promote the development of the country, the level of their sense of moral responsibility directly affects the social stability, and also relates to their own growth and success. However, the current college students are a generation with unique and innovative ideas, and due to the influence of social adverse impacts, they lack the ability to judge things, which also leads to their low sense of moral responsibility [1–2]. Therefore, college students should constantly enhance their sense of moral responsibility and take up the great responsibility and mission to realize social stability. Moral responsibility is the subject’s care for the object in the heart and senses, responsibility bearing and self-discipline [3]. It contains four elements: responsibility awareness, responsibility emotion, responsibility will, and responsibility behavior, which are intertwined and mutually influential. Among them, responsibility cognition, responsibility emotion, and responsibility will are the subjective consciousness processes (cognition, evaluation, and decision-making) of people’s understanding of the world, with the purpose of solving the questions of “what”, “what is the use” and “how to do”, which constitute the three basic subjective psychological activities of human beings (cognition, emotion, and will), which are the basic connotation of moral responsibility, and the premise, foundation, and key to forming a sense of moral responsibility [4–7]. Responsible behavior is the lasting performance under the guidance of responsible understanding, driven by responsible emotion and insisted by responsible will, which is also the external performance of moral responsibility [8–10]. University is the key period for young students to cultivate their moral sense of life, we should follow the law of moral education and talent training, grasp the characteristics of students at different stages of development, persevere in moral education, subtle influence to help students form a lofty ideals, sound personality, dedication and commitment to the spirit of mastery of the sense of responsibility to the community [11–13]. Morality is regarded as the basis of being a human being, the basis of life and work [14]. Learning to be a human being, learning to be a real person is the key and the core of the four pillars of education, as well as the goal and fundamentals of education. And social practice as an important way to cultivate the moral responsibility of college students [15], through the social practice in the continuous exercise of their own growth and success, for the development of the country and society to contribute to their own strength.

This paper studies the path of cultivating college students’ sense of responsibility based on social practice. Through the steps of elaborating the factor analysis method, distributing questionnaires, analyzing the content of the questionnaires, and naming the common factors, we clarify the 9 common factors affecting college students’ sense of moral responsibility. And further analyze the 7 dimensions of the internal structure of college students’ sense of moral responsibility. Through the correlation test and analysis research, the correlation relationship between the sense of social responsibility, the sense of belonging to the school and the sense of moral responsibility of college students is concluded, and based on this, the path of cultivating the sense of moral responsibility of college students is proposed.

2

Factor analysis of university students’ sense of moral responsibility

To explore the path of cultivating college students’ sense of moral responsibility, it is necessary to find the influencing factors of college students’ sense of moral responsibility first. This paper collects and analyzes college students’ viewpoints and understanding of moral responsibility by means of questionnaires, and extracts nine influencing public factors and seven dimensions of college students’ sense of moral responsibility.

2.1

Description of Factor Analysis

2.1.1

Mathematical modeling of the factor analysis approach

Assuming that p variable x₁,x₂,……x_p with possible correlation contains m independent common factors F₁,F₂,……F_n(m≦p), each variable x_i contains special factors e_i(i = 1……p), each special factor is uncorrelated with each other, and special factors e_i(i = 1……p) and F_j(j = 1……m) are also uncorrelated with each other, each x_i can be a linear combination of the m common factors and its own corresponding special factor e_i, the mathematical model expression for factor analysis is: 1 $\begin{matrix} x_{1} = a_{11} F_{1} + a_{12} F_{2} + \dots \dots + a_{1 n} F_{n} + e_{1} \\ \dots \\ x_{2} = a_{12} F_{1} + a_{22} F_{2} + \dots \dots + a_{2 n} F_{n} + e_{2} \\ x_{p} = a_{p 1} F_{1} + a_{p 2} F_{2} + \dots \dots + a_{p a} F_{n} + e_{p} \end{matrix}$

It can also be written in the form of a matrix: 2 $X = A F + e$

Here: $X = [\begin{array}{l} x_{1} \\ x_{2} \\ ⋮ \\ x_{p} \end{array}], A = [\begin{matrix} a_{11} & a_{12} & \dots & a_{1 m} \\ a_{21} & a_{22} & \dots & a_{2 m} \\ \dots & \dots & \dots & \dots \\ a_{p 1} & a_{p 2} & \dots & a_{p m} \end{matrix}], F = [\begin{array}{l} F_{1} \\ F_{2} \\ ⋮ \\ F_{m} \end{array}], e = [\begin{array}{l} e_{1} \\ e_{2} \\ ⋮ \\ e_{p} \end{array}]$

And it satisfies: 1)

m≦p;

2)

Cov(F,e) = 0, which means that the common and special factors are uncorrelated;

3)

$D_{F} = D (F) = [\begin{matrix} 1 & 0 & \dots & 0 \\ 0 & 1 & \dots & 0 \\ \dots & \dots & \dots & \dots \\ 0 & 0 & \dots & 1 \end{matrix}] = I_{n}$ , which means that the individual common factors are uncorrelated and have a variance of 1;

4)

$D e = D (e) = [\begin{matrix} σ_{1}^{2} & 0 & \dots & 0 \\ 0 & σ_{2}^{2} & \dots & 0 \\ \dots & \dots & \dots & \dots \\ 0 & 0 & \dots & σ_{p}^{2} \end{matrix}]$ , which means that the individual special factors are uncorrelated and the variances are not required to be equal.

In the above mathematical model, F is the factor variable or common factor, which can be interpreted as m mutually perpendicular axes in a high-dimensional space; A is called the factor loading matrix, and a_ij is called the factor “loadings” which are the loadings of the i th variable on the j th factor. If variable x_i is considered as a vector in the m-dimensional factor space, then a_i represents the projection of x_i onto axis F_j. e is called a special factor and represents the part of the original variable that cannot be explained by the common factor.

2.1.2

Basic steps of the factor analysis method

The factor analysis method generally includes the following main steps: Step1:

Test whether the original variables are suitable for factor analysis

As one of the main tasks of factor analysis is to condense the original variables, i.e. to extract and synthesize the overlapping information in the original variables into factors, so as to ultimately realize the purpose of reducing the number of variables. In this regard, it requires that there should be a strong correlation between the original variables. Otherwise, if the original variables are independent of each other, there is no overlap of information, then it is impossible to synthesize and condense them, and there is no need for factor analysis. This step is exactly what is desired to analyze whether the original variables are correlated or not and whether they are suitable for factor analysis or not through various methods.

SPSS provides several methods to help test the suitability of variables for factor analysis:

1)

Bart1ette’s test of sphericity

This method uses the correlation coefficient matrix of the variables as a starting point. Its null hypothesis is H₀: the correlation coefficient matrix is a unit array. The statistic is calculated based on the determinant of the correlation coefficient matrix. If the value of the statistic is large and the probability value is less than the significance level, then H₀ should be rejected as the correlation coefficient matrix is not a unit array and therefore suitable for factor analysis. Conversely, H₀ should be accepted as the correlation coefficient matrix is a unit array and therefore not suitable for factor analysis.

2)

KMO test

The KMO statistic is used to compare the simple correlation coefficients and partial correlation coefficients between the variables and it is calculated as: 3 $K M O = \frac{\sum \sum_{i \neq j} r_{i j}^{2}}{\sum \sum_{i \neq j} r_{i j}^{2} + \sum \sum_{i \neq j} p_{i j}^{2}}$

Where: r_ij is the simple correlation coefficient between variable i and variable j and p_ij is the partial correlation coefficient between variable i and variable j. From the formula it is always easy to see that the KMO takes values between 0 and 1. The larger the KMO, the closer it is to 1, the more suitable it is for factor analysis. On the contrary, the smaller the KMO, the less suitable it is for factor analysis.

The metrics on KMO are:

(1)

0.9-very good;

(2)

0.8-Rewardable;

(3)

0.7-okay;

(4)

0.6-Moderate;

(5)

0.5-awful;

(6)

<0.5-unacceptable;

3)

Reflective image correlation matrix test

This method takes the partial correlation coefficient matrix of the variables as the starting point, inverts each element of the partial correlation coefficient matrix, and thus obtains the reflective image correlation matrix. If the absolute value of some factors in the reflective image correlation matrix is large, it means that these variables are not suitable for factor analysis. On the contrary, it is suitable for factor analysis.

Step2:

Dimensionless Processing of Indicators

Evaluation indicators usually have units of measurement, so the covariance matrix or correlation matrix of the observed data of these indicators will inevitably be affected by the dimension of the indicators. Therefore, in order to avoid the calculation results affected by the index outline and order of magnitude, the raw data must be dimensionless processing. Its calculation formula is: 4 $x_{i j} = \frac{X_{i j} - \bar{X_{j}}}{S_{i j}}$

Where: x_ij is the data after dimensionless processing; X_ij is the original data; $\bar{X_{j}}$ is the mean of the j th indicator; S_ij is the standard deviation.

Step3:

Solve the correlation coefficient matrix of standardized variables R and its eigenroots and eigenvectors

Step4:

Extract the common factor

Solve the variance contribution ratio and cumulative variance contribution ratio of correlation matrix R.

Step5:

Factor naming and factor rotation

One of the purposes of factor analysis is to provide a reasonable naming interpretation of the actual meaning of the abstract factors extracted. Factor naming is based on the objective situation of each raw indicator and the facts it reflects, combined with the loadings of each raw indicator acting on each main factor to name and interpret each common factor.

In order to ensure that a factor variable can become a typical representative of a certain variable in practice, and can explain the vast majority of the information of this variable, it is necessary to realize through factor rotation. The methods of factor rotation include orthogonal rotation method and oblique rotation method. Orthogonal rotation methods are usually quadratic maximization, variance maximization, and equal maximization. The more commonly used method is the variance great method.

Step6:

Calculate factor scores

The purpose of constructing factor variables is to finally realize the factor score. With the factor score, the analysis work for the original variables can be simplified into a study only for the factor score variables. The functional expression for the factor score is: 5 $F_{j} = β_{j 1} x_{1} + β_{j 2} x_{2} + \dots \dots + β_{j p} x_{p} (j = 1, 2, 3, \dots, m)$

Where: β is the matrix of correlation coefficients of the original variables.

Methods for estimating factor scores include regression, Bart1ette method, Anderson-Rubin method, and so on.

Step7:

Calculate the comprehensive evaluation value

The formula for calculating the comprehensive evaluation value is: 6 $F = \sum_{j = 1}^{m} a_{j} F_{j}$

Where: F represents the composite assessment value.

2.2

Questionnaire survey on university students’ sense of moral responsibility

In order to find the influencing public factors of college students’ sense of moral responsibility, this paper uses a survey questionnaire to collect and analyze the contents that influence college students’ sense of moral responsibility. The following are the specifics of the questionnaire and the related research.

2.2.1

Objects of study

The prediction questionnaire survey selects a total of 583 college students from freshman to junior year of a university for prediction.

The formal questionnaire survey adopts the method of whole group stratified sampling, randomly selecting about 20,000 college students from freshmen to junior college students of one key university and one ordinary university in Guangzhou City as the survey object. A total of 21,500 questionnaires were distributed and 16,189 were collected, with an effective recovery rate of 75.3%. The specific composition of the subjects was as follows: (1) school type: 10,500 from key universities, 9,500 from ordinary universities; (2) grade composition: 6,836 freshmen, 6,670 sophomores, and 6,494 juniors; (3) gender composition: 8,733 males, 1,108 females, and 159 missing; and (4) family origin: urban 10236, rural 9627, missing 137. (5) 14,055 only children, 5,817 non-only children, 128 missing; (6) 2,858 student cadres, 16,888 non-student cadres, 254 missing.

2.2.2

Examination of Sample Suitability

In this study, we examined the appropriateness of factor analysis of the “College Students’ Moral Responsibility Questionnaire” through the correlation coefficient of the items, KMO test and Bartlett’s test of sphericity. The results showed that the correlation coefficients between some of the items were high and significant, which indicated that a common factor could be found for easy interpretation; the KMO coefficient was 0.894 and the chi-square value of the Bartlett’s test of sphericity was 16,573.426, with the significance of.001. Therefore, this sample is very suitable for factor analysis.

Combined with Table 1, it can be seen that the exploratory factor analysis finally extracted nine common factors, obtaining a total of 46 question items, which can explain 49.345% of the total variance of the items, which shows that these nine factors have a good rate of explaining the questions tested. F1 consists of seven items, which are named “Critical” as the content such as “I believe that everyone in the society should fulfill their moral obligations” reflects the individual’s awareness and evaluation of the moral responsibility viewpoints or behaviors. F2 consists of six items, such as “When faced with an impasse, I am able to make up my mind quickly and resolve the conflict”, which describes the individual’s cognition and understanding of the things around him or her, and is named “Reactivity”. The five items of F3, such as “I can restrain myself from interrupting other people’s rest or study”, are related to an individual’s control over his or her own behavior or emotions, and are named “self-control”. The five items of F4, such as “When I have a fight with someone, I can regain my composure relatively quickly”, are related to the individual’s ability to regulate and manage his/her bad emotions, and are named “mobility”. The four items of F5, such as “I secretly blame myself when I do something that harms social morality”, describe some emotions such as happiness, sympathy, and self-blame that individuals experience in responsible situations or behaviors, and are named “acuity”. The six items of F6, such as “I insist on turning off the lights”, are related to the individual’s ability to overcome difficulties after taking responsible behavior until the goal is achieved, and are named “resilience”. The four items of F7, such as “I have been proactive in caring about social and ethical events”, are related to the individual’s proactivity in adopting some responsible behaviors in a responsible situation, and are named “proactivity”. The content described in the five items of F8, such as “When the flag is hoisted, my patriotic passion often makes me stand at attention, salute, and listen carefully to the national anthem,” is related to some responsible behaviors adopted by individuals under the influence of emotions, and is named “efficacy”. F9 includes four question items, such as “I try to make my own judgments about some of the social and moral issues I encounter.” “When doing something new, I like to do it alone” describes the characteristics of individuals who think or act independently about responsible events, have their own opinions, and do not rely on others, and is named “self-reliance”.

Table 1.

Rotation factor characteristic value and contribution rate of each factor

Divisor	Eigenvalue	Contribution rate(%)	Cumulative contribution rate(%)
F1	7.753	16.841	16.841
F2	3.589	7.825	24.666
F3	2.214	4.801	29.467
F4	1.950	4.226	33.693
F5	1.716	3.725	37.418
F6	1.592	3.458	40.876
F7	1.387	3.012	43.888
F8	1.287	2.786	46.674
F9	1.227	2.671	49.345

Therefore, the nine common factors that affect the moral responsibility of college students can be named as “criticism”, “reactivity”, “self-control”, “flexibility”, “sensitivity”, “tenacity”, “initiative”, “efficacy” and “self-reliance”.

2.2.3

Structural validity of the questionnaire

Since there is no readily available questionnaire that can be used as an external reference, structural validity was used in this study to examine the validity of the questionnaire. The structural validity of the questionnaire was verified using factor analysis, which on the one hand can be seen from the factor loadings of the factors. On the other hand, factor analysis theory suggests that the structural validity of the questionnaire can also be seen from the correlation between the factors and the correlation coefficients between the factors and the total score of the questionnaire. According to psychologists, a good questionnaire should have a correlation between the factors and the total score between 0.30-0.80 and a correlation between the factors between 0.10-0.60. The correlation between the factors of the questionnaire on moral responsibility of university students and the matrix of correlation coefficients between the factors and the total score of the questionnaire are shown in Fig. 1.

As can be seen from Figure 1, the correlation between the factors of college students’ sense of moral responsibility ranges from 0.01-0.397, which is a moderately low degree of correlation, and is lower than the correlation between the factors and the total score of the questionnaire. The correlation between the factors and the total score of the questionnaire ranges from 0.487-0.699, with a moderately high degree of correlation; this indicates that the questionnaire has high content validity.

2.2.4

Analysis of the internal structure of university students’ sense of moral responsibility

The nine public factors and 46 question items above were analyzed and categorized to derive seven dimensions of college students’ sense of moral responsibility, and the means, minima and maxima of these seven dimensions were further analyzed as shown in Table 2.

Table 2.

Internal structure of college students’ sense of moral responsibility

	Mean value	minimum	Maximum
moral responsibility for oneself	3.392	5	27
moral responsibility to the family	3.879	9	31
moral responsibility for others	3.865	10	31
moral responsibility for community	3.672	6	20
moral responsibility to society	4.258	8	29
moral responsibility to the country	3.368	7	28
ethical responsibility for the environment	4.416	4	17
Moral responsibility in general	3.8344	49	183

By descriptively analyzing the scores on the seven dimensions of college students’ moral responsibility, we found that there are significant differences in the levels of the internal structure of college students’ moral responsibility. The mean value of “moral responsibility to the environment” and “moral responsibility to society” was higher than 4.1, especially the mean value of “moral responsibility to the environment” was 4.4146, indicating that college students have a strong sense of moral responsibility to the environment and society. The mean values of college students in the two dimensions of “moral responsibility to family” and “moral responsibility to others” were close to 3.9, which showed that college students had a strong sense of moral responsibility to family and others. The mean value of the dimension of “moral responsibility to the collective” of college students is relatively low, indicating that the moral responsibility of college students to the collective is relatively weak. However, the mean values of college students in the two dimensions of “moral responsibility to self” and “moral responsibility to the country” are much lower than those of the other five dimensions, indicating that college students’ sense of moral responsibility for themselves and the country is insufficient, and the situation is not ideal.

3

Mining the elements influencing the sense of moral responsibility of university students

Combining the 9 male factors and 7 dimensions of the sense of moral responsibility, it can be seen that college students’ sense of moral responsibility is influenced by many aspects. In the following section, correlation analysis will be used to clarify the relationship between college students’ sense of moral responsibility and their sense of social responsibility and sense of belonging to school, and to identify the influence of the sense of social responsibility and the sense of belonging to school on these nine public factors and seven dimensions.

3.1

Description of correlation analysis

3.1.1

Principles of correlation analysis

The principle of correlation analysis is the process of analyzing the degree of correlation between two or more variables and expressing the results of the calculations by means of appropriate indicators. Variable selection generally consists of two parts: rules and query mechanisms. By rules, we mean the rules for evaluating the joint correlation between variables in a set of variables. The main purpose of the query mechanism is to delete or add variables in the set of variables. Evaluation rules, also known as evaluation criteria, play a decisive role in variable selection. Both parts and even feature construction are inseparable from correlation. Correlation is a general concept used to describe the degree of data closeness between variables and the amount of mutual information contained, including asymmetric causal and driving relationships. The basic idea of feature selection based on correlation analysis is to select effective variables with higher correlation with the prediction target, i.e., feature variables contributing to the performance of the prediction model, by analyzing the strong or weak correlation relationship between variables.

Correlation coefficient is a commonly used method to analyze the correlation of binary variables, the value of correlation coefficient is used to measure the degree of correlation, the strength of linearity or non-linearity between two variables, which is commonly used in feature engineering for machine learning and deep learning. Usually, the higher absolute value of the correlation coefficient indicates the stronger correlation between the variables, and the correlation analysis methods mainly include Pearson correlation coefficient, Spearman rank correlation, gray correlation analysis, distance correlation and maximum information coefficient.

3.1.2

Commonly used correlation coefficients

1)

Pearson correlation coefficient

Define the aggregate of the two-dimensional variable X,Y as (X,Y)^T,(x₁,y₁)^T,(x₂,y₂)^T,…,(x_n,y_n)^T is the experimental sample of variable X,Y, which is also the observed data, to obtain the observation matrix M. 7 $M = {[\begin{array}{l} x_{1} & x_{2} & \dots & x_{n} \\ y_{1} & y_{2} & \dots & y_{n} \end{array}]}^{T}$

Calculate the mean of variable X and variable Y separately 8 $\bar{x} = \frac{1}{n} \sum_{i = 1}^{n} x_{i}$ 9 $\bar{y} = \frac{1}{n} \sum_{i = 1}^{n} y_{i}$

Equation (8) represents the mean of variable X observations and equation (9) is the mean of variable Y observations. The data variance was further calculated: 10 $S_{x x} = \frac{1}{n - 1} \sum_{i = 1}^{n} {(x_{i} - \bar{x})}^{2}$ 11 $S_{y y} = \frac{1}{n - 1} \sum_{i = 1}^{n} {(y_{i} - \bar{y})}^{2}$ 12 $S_{x y} = \frac{1}{n - 1} \sum_{i = 1}^{n} (x_{i} - \bar{x}) (y_{i} - \bar{y})$

Equations (10), (11), and (12) compute the covariance of the observations for variable X, variable Y, and two-dimensional variable X,Y, respectively, and defining S as the covariance matrix of the observations, the 13 $S = [\begin{array}{l} S_{x x} & S_{x y} \\ S_{y x} & S_{y y} \end{array}]$

In matrix S, there are S_xy = S_yx, so S diagonal elements are equal for a symmetric matrix. In addition, according to Schwarz’s inequality, there are 14 $S_{x y}^{2} \leq S_{x x} S_{y y}$

Thus matrix S is a positive definite matrix. The correlation coefficient for the observations of Definition X,Y is given by 15 $r_{x y} = \frac{S_{x y}}{\sqrt{S_{x x}} \sqrt{S_{y y}}}$

According to Schwarz’s inequality, |r_xy|≤1 holds. r_xy is used to measure the degree of linear correlation of variable X,Y. Let the overall of two-dimensional variable X,Y be (X,Y)^T, then the correlation coefficient of (X,Y)^T is calculated as 16 $r_{X Y} = \frac{C o v (X, Y)}{\sqrt{V a r (X)} \sqrt{V a r (Y)}}$

Where Var(X) and Var(Y) are the variances of variables X and Y respectively, and Cov(X,Y) is the covariance of the two-dimensional population (X,Y)^T. The Pearson correlation coefficient has a range of r∈[–1,1], and the absolute value of the Pearson coefficient is usually used to determine the correlation between the two variables, with the following rules: r > 0 indicates a positive correlation, r < 0 indicates a negative correlation; |r| = 0 indicates that there is no linear relationship; and |r| = 1 indicates a complete linear correlation.

2)

In short, the absolute value of Pearson’s coefficient is close to 1, which proves that the correlation between the two variables is stronger; on the contrary, the smaller the absolute value is, the weaker the correlation between the variables is, and the stronger the independence is. At the same time, it should be noted that the use of Pearson’s correlation coefficient needs to follow the following principles

(1)

Pearson’s correlation coefficient is only applicable to linear correlation data analysis;

(2)

Pearson’s coefficient will have a greater impact on the results of the variables in the presence of extreme values of the observed data;

(3)

Using the Pearson correlation coefficient, the two-dimensional totals (X, Y)^T being tested are required to follow a bivariate normal distribution.

3.2

Research on the influencing elements of the realization of college students’ sense of moral responsibility

3.2.1

Objects of study

Psychological volunteers were recruited as members of the experimental and control groups of this empirical study in a university in Beijing. A total of 258 psychological volunteers were recruited, and 119 were randomly assigned to the experimental group and another 119 to the control group. The total number of members in the experimental group was 119, and the actual effective number was 106. In the control group, there were 119 members, and the actual number of effective members was 98. The reason for the number of invalid members in the experimental and control groups is that some members in the experimental and control groups did not participate in the post-survey questionnaire after completing the pre-survey questionnaire, i.e., the post-survey questionnaire, and the questionnaires of the pre-survey and post-survey questionnaires lacked either one of them, which is invalid, and therefore were not counted for the statistical analysis of the experimental data, thus generating the number of invalid members.

3.2.2

Analysis of empirical research data

Table 3 shows the correlation coefficients between the sense of moral responsibility and the sense of social responsibility. As can be seen from Table 3, in the analysis of the experimental group’s posttest about the correlation between the sense of moral responsibility and the sense of social responsibility, there is a positive and significant correlation between the three parts of the moral rule consciousness and the judgment of responsibility competence, the moral autonomous behavior and the sense of responsibility efficacy, and the moral self-regulation and the responsibility behavior at the level. This shows that most students, when taking up a certain position or undertaking a certain job, will understand what must be accomplished and done well in the position they are taking up, clearly understand the content of the job, clarify their own responsibilities, and at the same time have a clear perception of their own level of competence, so that they will have a sense of mission in their bodies, which will constantly drive them forward to work hard to make progress, and will pay attention to their own words and deeds in the process and check their own words and deeds against the standard of moral behavior. In the process, they will pay attention to their own words and deeds, check their own words and deeds with the standard of ethical behavior, and have a personal understanding of the ethical guidelines, thus gradually strengthening their own sense of ethical rules. At the same time, the student union will always check and adjust themselves according to the work requirements in the process of serving everyone after clarifying what they should do effectively, which also regulates and standardizes their own moral behavior unconsciously. Most of the students, if they deepen their understanding of the content of the work they undertake, will do a good job with all their heart and soul, and the understanding and recognition of a job can push the students to do their work with a more enthusiastic working attitude, to work meticulously and conscientiously, so that they can develop a positive and enterprising attitude towards doing things and correct and healthy behavioral and living habits, and their words and deeds are in line with the standards of moral behavior. The lack of significance in other dimensions between moral responsibility and social responsibility may be due to the fact that moral responsibility focuses more on the internal understanding of moral requirements and the internalization of moral standards, while social responsibility is more on the external perception of the collective society and the country, which is why such a situation occurs. In conclusion, there is a correlation between moral responsibility and social responsibility in some dimensions.

Table 3.

Correlation coefficient between moral responsibility and social responsibility

	Rule awareness post test	Self-determined post-test	Self-regulating post-test	Autonomous behavior post-test
Social good post test	0.018	-0.024	0.017	0.109
Social obligation post-test	0.105	0.044	-0.007	0.073
Responsibility ability judgment post test	.154*	0.109	0.146	0.168
Post-test of accountability efficacy	0.110	0.127	0.116	.227*
Responsible behavior post-test	0.173	0.088	.215*	0.121
Willing to judge after testing	0.015	-0.015	0.004	0.013

Represents a significant correlation at the 0.03 level (bilateral)

Represents a significant correlation at the 0.01 level (bilateral)

Table 4 shows the correlation coefficients between the sense of moral responsibility and sense of belonging to school. It can be seen from Table 4 that in the analysis of the post-test of the experimental group regarding the correlation between moral responsibility and sense of belonging, the two components of moral responsibility, namely moral rule consciousness and sense of belonging to school, moral self-regulation and attachment, are significant at the 0.03 level and show a positive correlation at the 0.03 level. Meanwhile, it can also be seen from Table 4 that there is no significant significant correlation between the four dimensions of identity and moral responsibility, i.e., moral rule consciousness, moral self-determination, moral self-regulation, and moral autonomous behavior, which may be due to the fact that the students’ sense of identity with the school is mainly embodied in the recognition and affirmation of the campus culture, learning atmosphere, and campus construction of the school they are in. The sense of moral responsibility is mainly reflected in the fact that students can understand and accept the knowledge of moral theories, choose and decide the standard of moral behavior that they want to follow as their own moral beliefs, absorb and digest the moral standard into their own positive personal qualities and correct behavioral lifestyle, and express the moral standard that has been deeply rooted in their hearts and minds through their own outward speech and behavior, so that they can In the absence of supervision and control by others, they can consciously and actively make behaviors in line with the moral requirements. Moral responsibility in the sense of rules, self-determination, self-regulation, autonomous behavior reflects a person to realize the sense of moral responsibility need to have the prerequisites to achieve a sense of moral responsibility is to improve the level of self-moral literacy is essential and indispensable link. Moral responsibility is concerned with one’s own moral status, while identity focuses on the perception of the external school community. There is a big gap between the two in terms of awareness. There is a significant correlation between the sense of moral responsibility, the sense of moral rules and the sense of belonging to the school, which indicates that students live and study in the school for a long time, gradually deepen the all-round understanding of the school, in the process they gradually open their arms and like to live and study every day in the collective, but also feel the warmth of this big family to bring their warmth and tolerance, which makes them want to do their own for the forward progress of the collective, so they will do their part in themselves. Therefore, they will start from themselves, strictly discipline themselves in school, standardize their words and deeds, and gradually have and deepen their sense of moral rules while constantly adjusting and improving their own words and deeds. At the same time, there is a significant correlation between self-regulation in moral responsibility and attachment in sense of belonging to school, which may be due to the fact that after students gradually accept the school as a big family, they will become attached to this big collective, and they will be willing to stay in school for a long time, and feel that learning and living in the school is a happy and joyful thing, and therefore they will voluntarily and actively act in accordance with the rules and regulations of school, and will be responsible for themselves and the school. Therefore, they will voluntarily and actively act in accordance with the rules and regulations required by the school, and will consciously and actively make adjustments and improvements to those aspects of themselves that are not in line with the school’s standards and requirements in order to make themselves conform to the school’s requirements. In conclusion, there is a correlation between moral responsibility and sense of belonging to school in some dimensions.

Table 4.

Correlation coefficient between moral responsibility and sense of belonging

	Rule awareness post test	Self-determined post-test	Self-regulating post-test	Autonomous behavior post-test
Belongingness post test	.289*	0.073	0.204	0.049
Identity post test	0.187	0.154	0.047	0.138
Attachment post test	0.186	0.173	.289*	0.086

Represents a significant correlation at the 0.03 level (bilateral)

Represents a significant correlation at the 0.01 level (bilateral)

4

Paths for the development of a sense of moral responsibility among university students

Combined with the previous research, it can be seen that the sense of social responsibility and the sense of school belonging will affect the 9 male factors and 7 dimensions of college students’ sense of moral responsibility. To cultivate, improve and realize college students’ sense of moral responsibility, this paper believes that we can cultivate college students’ sense of social responsibility and sense of school belonging in two aspects.

Innovative education mode, deepen the sense of social responsibility education. Burying one’s head in the sand can’t correctly recognize the social status quo, and college students’ sense of social responsibility needs to be cultivated in the interaction with the outside world. When educators cultivate college students’ sense of social responsibility, they should not only stay at the theoretical level, but also deepen students’ understanding and recognition of social responsibility through vivid and interesting practical activities. Educators can design interactive social responsibility education courses, integrating story narration, real case analysis, and with the help of film and television works, so that college students can realize the power of social responsibility in the interaction and understand the social responsibility in the case analysis. In addition, educators can also set up “small lecture halls on social responsibility”, inviting local social models to share their personal experiences, so that college students can feel the power of positive role models through teaching by example. In terms of social practice, educators should encourage college students to participate in regular volunteer services, such as environmental protection and cleaning, helping the elderly and the disabled, etc., so that college students can experience the joy of giving and receiving in the service, understand the connotation of social responsibility and commitment, and thus enhance their sense of social responsibility.

Student-oriented, enhance students’ sense of belonging to the school. Today’s university education basically focuses on personal success, competition and winning, which has created indifference to the sense of moral responsibility in the educational practice itself, and this indifference has led to college students treating school as a place of competition, making it difficult for them to improve their sense of belonging to school. The lack of a sense of belonging to the school also further leads to the neglect of the sense of moral responsibility of college students. In order to enhance college students’ sense of belonging to school, educators need to shift from utilitarianism to “student-oriented” education, treat students as members of the school, start from the actual situation of the students, solve the learning and life problems for the students, and carry out campus activities that students enjoy, so that the students will become attached to the school in all aspects of their learning and life. In all aspects of study and life, the students become attached to the school, which will enhance their sense of belonging to the school, and consciously and actively regulate their own behavior, thus enhancing their sense of moral responsibility.

Enhancing college students’ sense of social responsibility and sense of belonging to the school can make college students consciously and actively pay attention to their own words and behavior, pay attention to others, and cultivate and improve their own sense of moral responsibility.

5

Conclusion

This paper mainly combines social practice to explore the path of cultivating college students’ sense of moral responsibility. Combined with factor analysis and correlation analysis, nine common factors affecting college students’ sense of moral responsibility were extracted through questionnaire survey and controlled experiments, which were “criticism”, “reactivity”, “self-control”, “flexibility”, “sensitivity”, “tenacity”, “initiative”, “efficacy” and “self-reliance”. The descriptive analysis of the scores of college students’ moral responsibility in seven dimensions showed that the mean values of college students in the two dimensions of “moral responsibility to the environment” and “moral responsibility to society” were higher than 4.1. Further correlation experiments and analyses yielded that social responsibility and school belongingness were significant and showed a positive correlation at the 0.03 level, indicating that there is a correlation between college students’ sense of moral responsibility and their sense of social responsibility and school belongingness.

Based on the findings of this paper, the path of cultivating college students’ sense of moral responsibility is proposed as “innovating education mode, deepening education of social responsibility” and “student-oriented, enhancing students’ sense of belonging to school”, so that students can improve their own sense of identity and self-confidence in the collision and practice with the society. In the process of encountering and practicing with the society, students can enhance their sense of identity and self-confidence, improve their ability to care for and participate in others, family and society, and further strengthen their sense of moral responsibility.

Lingua:: Inglese

Frequenza di pubblicazione:: 1 volte all'anno
Argomenti della rivista:: Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro

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Exploring the Path of Cultivating College Students’ Sense of Moral Responsibility Based on Social Practice

Miaomiao Wang

Ping Wang

Haibin Liu

Pubblicato online: 19 mar 2025

Ricevuto: 15 ott 2024

Accettato: 08 feb 2025

DOI: https://doi.org/10.2478/amns-2025-0501

Parole chiaveMoral responsibility, Factor analysis, Correlation analysis, Social responsibility

© 2025 Miaomiao Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Moral responsibility, Factor analysis, Correlation analysis, Social responsibility