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Article

Online Education and Subjective Well-Being in China: Multiple Mediating Roles of Social Class Mobility and Social Tolerance

by
Shuang Liu
1,
Yan Cao
2 and
Hao Zhang
3,*
1
School of Marxism, Fujian Jiangxia University, Fuzhou 350001, China
2
School of Finance, Fujian Jiangxia University, Fujian Jiangxia University, Fuzhou 350001, China
3
School of Electronic Information Science, Fujian Jiangxia University, Fujian Jiangxia University, Fuzhou 350001, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(3), 2177; https://doi.org/10.3390/su15032177
Submission received: 14 November 2022 / Revised: 14 January 2023 / Accepted: 16 January 2023 / Published: 24 January 2023
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Abstract

:
(1) Background: Online education has developed into a new form of education. However, the relationship between online education and subjective well-being has seldom been extensively studied in the literature. Thus, this study provides quantitative evidence regarding the effect of online education on subjective well-being. (2) Objective: The objective of this study was to reveal the net effect of online education on subjective well-being and explore the mediating roles of social class mobility and social tolerance between online education and subjective well-being. (3) Methods: Based on the 2019 China Comprehensive Social Survey data, the “counterfactual framework” was constructed using the propensity score matching method, and 1029 matched samples were analyzed. (4) Results: Online education is significantly positively correlated with subjective well-being (average treatment effect on the treated, ATT = 0.189, p < 0.01). Social class mobility and social tolerance serially mediate the relationship of online education and subjective well-being (the intermediary role of social class mobility is 0.0163; the mediating role of social tolerance is 0.0064). (5) Conclusion: This study confirms the positive predictive effect of online education on subjective well-being and affirms the multiple mediating roles of social class mobility and social tolerance between online education and subjective well-being.

1. Introduction

Online education (OE) is a new form of distance education in the Internet era [1]. Bringing online education into full play is an important part of China’s education development during the 14th Five-Year Plan period. Since the outbreak of COVID-19, online education has been widely used and has played an important role in promoting education development. According to China’s 48th Statistical Report on Internet Development in China, as of June 2021, the number of online education users in China reached 325 million, accounting for 32.1% of all Internet users. The booming development of online education is the result of a combination of objective factors, such as information technology innovation, business value promotion, and the concept of lifelong education [2]. It is also because online education can generate a wide range of social benefits. Of all topics, “online education lights up people’s happiness” is becoming an interesting one. This poses new research questions: is online education making people happy simply a kind of popular psychological satisfaction in the Internet era, or is it an objective social fact? No in-depth research on this topic has been conducted yet.
It is important to study the relationship between online education and the subjective well-being (SWB) of Chinese residents. It is necessary to clarify the function of online education in subjective well-being studies and respond to the value of China’s policy of vigorously developing online education. Based on this, this paper addresses two issues: (1) the overall impact of online education on Chinese residents’ subjective well-being, and (2) the mediating paths through which online education affects Chinese residents’ subjective well-being.

2. Theoretical Background and Hypotheses

2.1. Online Education and Subjective Well-Being

The exploration of well-being can be traced back to the ancient Greek period when Socrates expressed the proposition of “how should a man live”, which led scholars to explore the concept of happiness. The early understanding of well-being was mainly based on literary definitions, such as Aristotle’s “perfectionism” and Jeremy Bentham’s “happiness”. Since the 1950s, scholars have begun defining and quantifying subjective well-being from fields, such as economics, psychology, and sociology. According to Jevons, subjective well-being in economics refers to the optimal choice of a person under constraints [3]. Psychologist Costa believes that subjective well-being focuses on the perception of psychological states and should include external criteria, emotional experience, and individual self-evaluation [4]. Diener believes that subjective well-being is an important measure of national and social livelihood development, and is an emotional experience consisting of emotions and cognition. This means that based on their life situation and the criteria set by them, people have a comprehensive evaluation and then possess subjective pleasurable emotions [5]. With the continuous exploration of the connotation of subjective well-being, studies on its influencing factors have emerged, including both individual characteristics, such as individual traits, marriage, employment, and Internet use [6,7,8], and macro characteristics such as unemployment rate, social security, and government spending [9,10,11]. Among them, the relationship between education, as an important factor in enhancing the quality of individuals and promoting social development, and subjective well-being has received increasing attention from the academic community.
Online education is defined as a learning experience that uses online or remote devices (e.g., cell phones, laptops) and access to a synchronous or asynchronous environment [12,13]. The online education process allows learners to access online instructional materials, submit assignments or reports remotely, and take assessment tests during or after class [14]. Online education as a new paradigm of teaching and learning can be more innovative and even more flexible by reflecting a more student-centered educational philosophy in the teaching and learning process [15,16]. The COVID-19 pandemic has had a lasting impact on educational development [17], and online education played a crucial role during this pandemic by ensuring that students were able to complete their learning tasks during school closures [18]. Numerous studies related to online education have identified this pandemic as a great opportunity and challenge for the development of online education [17,18,19]. Even in the post-pandemic period, online education remains a key factor in achieving sustainable education because of its flexible learning process, rich technological support, and interdisciplinary pedagogy [20].
Although online education has strong Internet industry attributes, it is still education in essence and therefore has the potential to influence subjective well-being. However, few studies have focused on analyzing the effects of online education on subjective well-being, but studies on education can provide insights into the well-being effects of online education. A large body of literature indicates that education can significantly contribute to subjective well-being. The first perspective argues from a philosophical psychological perspective, following a subjective-to-subjective logic, that education can change individuals’ cognitive abilities and provide them with more emotional support in work and life scenarios, thus enhancing their subjective well-being [21,22]. The second perspective is based on human capital theory and follows an objective-to-subjective logic. This reveals that education, as an important human capital, is directly proportional to economic benefits and can promote the development of economic resources, such as individuals’ income and career development, thus in turn enhancing individuals’ perceptions of subjective well-being [23]. Since the reform and opening up, although the return to education varied across different regions in China, individual economic returns generally present a characteristic change in growth as the years of schooling increase [24]. Yet, this positive correlation is not robust. Some experts argue that educational attainment has a negative effect on well-being. Because people with a higher level of education also have higher self-expectations, these people’s happiness decreases to a greater extent in the event of unemployment, economic recession, and other factors [25]. Recent studies conducted on Chinese youth have also determined the occurrence of this phenomenon: as the educational level increases, the positive prediction of happiness decreases [26]. Several international surveys also revealed unhappiness in higher-education groups. For example, in 2018, a survey published in Nature reported that graduate students are six times more likely to be anxious and depressed than the general public [27].
The complexity of existing studies suggests that analyzing the relationship between online education and the subjective well-being of Chinese residents using national survey data is necessary. In addition, the existing research suffers from two shortcomings: first, theoretical research lags behind the development of the educational paradigm in reality. Although there are numerous research results on education and happiness, the research conducted on the relationship between online education and happiness is insufficient. Second, there is a lack of exploration in the process mechanism of online education affecting subjective well-being. This study aims to respond to the abovementioned research deficiencies and expand the perception of the relationship between online education and subjective well-being. Online education is a kind of human capital investment in the digital era. According to human capital theory, as individuals invest more (time and money) in education, they can obtain economic compensation for knowledge accumulation, and economic compensation will positively affect their well-being [28]. This analysis is consistent with the traditional Chinese concept of “education makes people happier”. In conclusion, this study stated that online education has a positive effect on the subjective well-being of Chinese residents, and proposed the first hypothesis:
H1. 
Online education has a significant effect on the subjective well-being of Chinese residents.

2.2. Online Education, Social Class Mobility, and Subjective Well-Being of Chinese Residents

It is not enough to identify the impact of online education on Chinese residents’ subjective well-being. It is necessary to further explore its intrinsic influence mechanisms. Social class mobility (SCM) is an important factor influencing subjective well-being. Social class mobility is an important criterion to test the fairness and democracy of a country, and it is a dynamic balance formed in the process of dynamic changes in social class and social status. [29]. In China, social class is divided into ten major classes based on occupation differentiation and the occupational status of three kinds of resources (organizational, economic, and cultural) [30]. Additionally, social class mobility is the vertical or horizontal mobility presented between these ten social classes. This study focused on vertical social class mobility, which refers to the mobility of a person from the lower to upper classes or from upper to lower classes in terms of status and occupation. If a society has more opportunities for upward rather than downward social mobility during a certain period, it means that the society is progressing; otherwise, the society is regressing. As an important feature of modern society, upward social class mobility significantly positively affects residents’ well-being [31].
The research shows that education is an important ladder for upward social class mobility of Chinese residents. Higher education means a greater likelihood of upward social class mobility. There are two explanatory mechanisms for this. First, cultural capital has a positive effect on social class mobility. Education is an important cultural capital. Bourdieu argues that “in the fluid world of status culture, the stock of cultural capital of individuals is only partially determined by their childhood experiences and family background” [32]. Education allows groups with disadvantaged family structures to increase their wealth and social status through self-generating factors, such as knowledge accumulation, rather than through predisposing factors such as social ties or family origin. Thus, education is the most effective path to achieve upward social class mobility for individuals from a lower social class. This view has been confirmed by a series of empirical studies [33,34]. Second, this facilitation is also influenced by the belief in education in traditional Chinese culture. The traditional Chinese culture has traditionally emphasized education to change one’s destiny, especially the establishment of the imperial examination system, which makes education more prominent as a “door knocker” for ordinary people to achieve upward social class mobility. Even at present, the belief in education that “knowledge changes fate” is still embedded in the deepest layer of the Chinese national psychological structure and is the most typical perception of the value of education among Chinese residents [35].
Since the reform and opening up, with the deepening of the concept of sustainable development of education and the transformation of the educational ecosystem, the role of education in promoting social class mobility in China is no longer limited to school education. For one thing, online education can largely compensate for the limitations of brick-and-mortar schooling and provide new educational opportunities, and the educated can achieve more stable learning outcomes [36]; therefore, online education has the potential to reshape the educational landscape and promote educational equity [37]. Moreover, online education helps educated people prepare for their future careers and promotes the development of individual social network relationships [38]. These advantages make online education an important driver of upward social mobility. In summary, social class mobility can effectively promote the subjective well-being of Chinese residents. At the same time, online education is effective in promoting social class mobility; therefore, the second hypothesis was proposed.
H2. 
Social class mobility is a mediator between online education and Chinese residents’ subjective well-being. Online education can enhance Chinese residents’ subjective well-being by promoting social class mobility.

2.3. Online Education, Social Tolerance and Subjective Well-Being

Tolerance is not only a high-frequency word used in modern society but also a multidisciplinary concept involving political science, ethics, and philosophy. According to Galeotti’s definition, tolerance is a social virtue and a political tenet. Its significance is that it enables individuals and social groups with different views and behavioral tendencies to live together peacefully in the same society [39]. From this concept, it is clear that social tolerance is a degree of recognition and acceptance of differences and a willingness to grant equal rights [40]. Since the beginning of human society, social tolerance has been a key factor in building a moral society. Especially in modern society, social tolerance can be ranked alongside other social capital elements in the traditional sense, such as interpersonal trust, and is an important social capital indispensable to residents’ subjective well-being [41,42]. Without tolerance, society will fall into various “cultural, social, and political conflicts. The efficiency of socioeconomic development will be slowed down. The living environment will deteriorate. The political operation will plunge into inefficiency and paralysis. All will ultimately harm the well-being of every member of society.” [43].
Online education can promote the formation and enhancement of social tolerance, because the essence of tolerance is a cultural concept, one which cannot be spread without education. Culturally diverse instructional content in an online educational environment is easily integrated into the educational process [44], which helps educated individuals challenge certain cultural perceptions and enhance their enthusiasm for learning and communication skills [45]. Individuals can experience a culture of tolerance in the learning process, change traditional values, and achieve respect for pluralism and the equal status of different subjects, thus making society as a whole more tolerant. Additionally, positive social tolerance can provide the basic prerequisites for the free development of individuals and provide room for creative activities. At the same time, it can also promote the construction of a democratic and harmonious society, and thus contribute to the increase in residents’ well-being [46]. To summarize, online education can effectively promote social tolerance, thus further enhancing residents’ happiness. Accordingly, the third hypothesis was proposed:
H3. 
Social tolerance is the mediator between online education and subjective well-being. Online education can enhance subjective well-being by promoting social tolerance.

3. Methods

3.1. Participants

This study used 2019 survey data of the Chinese General Social Survey (CGSS), which included 31 provinces in China. According to the abovementioned research objectives, the survey results were sifted. The missing and invalid data were deleted, and 2581 cases were finally obtained. These included 1215 men (47.1%) and 1366 women (52.95%). The average age was 41.8 years (standard deviation, SD = 13.16, range = 18–69). A total of 1832 people (71%) received a high school diploma or below, and 749 people (29%) received a college education or higher. The number of people in agricultural populations was 1560 (60.4%), and the number in nonagricultural populations was 1021 (39.6%). The average logarithm of personal income was 4.36 (SD = 0.58, range = 0.48–6.18).

3.2. Procedure

CGSS is a national, comprehensive, and continuous survey project. The survey was initiated by the Chinese Academy of Social Sciences (CASS) and launched by the National Survey Research Center at Renmin University of China (NSRC). It meets the requirements of the ethical review committees of the Chinese Academy of Social Sciences. CGSS adopts probability sampling in household surveys. During the survey, investigators entered some preselected villages or communities, randomly selected some families, and interviewed one or more of the family members. CGSS is a public nonprofit project. Within two years following the end of the survey, the project team will open the original data to the entire society without any charges.

3.3. Subjective Well-Being

The CGSS 2019 uses a unidimensional scale to measure the subjective well-being of Chinese residents, a measure that has been adopted by several large-scale social surveys such as the Program for International Student Assessment, the Chinese Household Tracking Survey, and the World Values Survey [47,48,49]. The unidimensional Likert scale can accurately measure subjective well-being in a large-sample sampling survey [50]. The question in the questionnaire was “On the whole, I am a happy person”. The corresponding options were: 1 = strongly agree, 2 = agree, 3 = disagree, 4 = strongly disagree, and 8 = not sure. The study recoded them as 1 = very unhappy, 2 = unhappy, 3 = not sure, 4 = happy, and 5 = very happy.

3.4. Online Education

We used the frequency of attending learning education online to measure online education behavior. In the questionnaire, the options corresponding to the frequency of online participation in learning education were 1 = almost every day, 2 = multiple times a week, 3 = at least once a week, 4 = at least once a month, 5 = several times a year, and 0 = never. To meet the needs of the study, 1 and 2 were recoded as 1 for the high-frequency group of online learning, and 3, 4, 5, and 0 were recoded as 0 for the low-frequency group of online learning.

3.5. Social Class Mobility

The first mediating variable presented in this study was social class mobility, which was measured using respondents’ subjective perceptions of social class mobility. When people evaluate their status, they tend to make “selective” comparisons of society to their environment [51]. Given this, this study combined the questions in the CGSS 2019 questionnaire with the multiple-choice question: “Which level do you think your socioeconomic status will be in the next 5 years in your local area?”. The answers to this item were used to determine whether the individual’s class will move upward in the next 5 years. In the questionnaire, 1 = upper, 2 = upper middle, 3 = middle, 4 = lower middle, and 5 = lower. The values 1, 2, 3, 4, and 5 were recoded as 5, 4, 3, 2, and 1. Larger values indicated better social class mobility in the future.

3.6. Social Tolerance

The second mediating variable presented in this study was social tolerance. For the measurement of social tolerance, one question item of the CGSS questionnaire (respondents’ subjective evaluation of the present level of social tolerance) was selected as an indicator for the study. This question item was a comprehensive judgment based on the tolerance level of six groups of people: premarital cohabitants, homosexuals, beggars, ex-prisoners, people with different religious beliefs, and acquired immune deficiency syndrome (AIDS) patients. The tolerance of these behaviors does not mean that the respondent is a practitioner of these behaviors or that they encourage these ideas and behaviors, but only that they believe that these ideas or behaviors have the right to exist. In the questionnaire, 1 indicates very intolerant, 10 indicates very tolerant, and the 10-point scale was transformed into a 5-point scale, where 1 indicates very intolerant, 2 indicates relatively intolerant, 3 indicates moderately tolerant, 4 indicates relatively tolerant, and 5 indicates very tolerant.

3.7. Control Variables

The control variables selected for the study included two parts: the first was demographic factors, including gender, age, education level, political affiliation, household registration, and income level. The second was social security and social capital. Social security can enhance subjective well-being by providing individuals with risk precautions [52]. It is measured by pension and medical security according to the basic national conditions in China. Interpersonal trust is an important social capital that can enhance subjective well-being by providing social support to individuals [53]. In addition, the study showed that pension security, medical security, and interpersonal trust are also important factors influencing social class mobility and social tolerance, which is an important reason for including them as control variables [54,55]. Therefore, to ensure reliable model estimation results, the above variables were introduced into the model as control variables. The descriptive statistical analysis of all variables is shown in Table 1.

3.8. Data Analysis

3.8.1. Propensity Score Matching (PSM) Method

In the regression model based on the survey data, only the correlation between independent and dependent variables can be analyzed, but effective causal identification is not possible, i.e., the subjective well-being of the respondents may be influenced by factors other than online education; therefore, the problem of endogeneity arises. Therefore, the propensity score matching method was used to analyze the net effect of online education on subjective well-being. This method uses the estimation of the treatment effect in the counterfactual simulation state and can effectively eliminate the selectivity bias caused by confounding factors [56]. The basic idea of the PSM method is to estimate the propensity score through Logistic or Probit models, and to select an individual from the control group (CG) who is similar to the sample of the intervention group (IG) in every characteristic for matching. After matching, the samples of the intervention and control groups no longer presented a systematic bias, i.e., the difference between the intervention and control groups in the dependent variable of subjective well-being at this time was only related to the independent variable, i.e., online education. In this paper, those respondents who participated in online education more than “more than once a week” were set as the high-frequency group, i.e., the intervention group, and those who participated in online education less than or equal to “at least once a week” were set as the low-frequency group, i.e., the control group. A logistic model was used to estimate the propensity score. To improve the stability of the results, nearest neighbors matching (NNM), radius matching (RM), and kernel matching (KM) were used for the analysis.

3.8.2. Mediating Effect Test

The mediating effect test was conducted according to the classical stepwise regression method, and the significance of the mediating effect was tested using a nonparametric bootstrap. In this paper, social class mobility and social tolerance were used as mediating variables to examine the influencing mechanism of online education on subjective well-being, and the mediating effect model was constructed as follows.
S W B = α 0 + c O E + α 1 c o n t r o l + ε 1
S C M = β 0 + β 1 O E + β 2 c o n t r o l + ε 2
S T = γ 0 + γ 1 O E + γ 2 c o n t r o l + ε 3
S W B = η 0 + c ′ O E + η 1 S C M + η 2 S T + η 3 c o n t r o l + ε 4
SWB represents subjective well-being, and OE represents online education. SCM and ST are social class mobility and social tolerance, and control represents the control variables. α0, β0, γ0, and η0 are intercept terms; α1, β2, γ2, and η3 are influence coefficients; ε1, ε2, ε3, and ε4 are residual errors. Model (1) examined the effect of online education on subjective well-being, and c is total influence coefficient. Model (2) examined the effect of online education on social class mobility, and β1 is the influence coefficient. Model (3) examined the effect of online education on social tolerance, and γ1 is the influence coefficient. Model (4) examined the effect of online education on subjective well-being while controlling social class mobility and social tolerance. c ′ is the direct influence coefficient. η1 and η2 are the influence coefficients of SCM and ST on SWB respectively. The independent mediated effects are “online education → social class mobility → subjective well-being” and “online education → social tolerance → subjective well-being”. The mediating effect values are β 1 η 1 and γ 1 η 2 . The sum of these two is the total mediated effect. The total effect c of online education on subjective well-being is the sum of the direct c ′ and mediated effects, which is c = c ′ + β 1 η 1 + γ 1 η 2 .
In this study, the stability of the results of the mediation analysis was tested by the mediating effects analysis for the full sample (N = 2581) and the sample with one-to-one nearest neighbors matching (caliper = 0.05) (N = 1029).

4. Results

4.1. The Overall Contribution of Online Education on Chinese Residents’ Subjective Well-Being

The correlation between online education and subjective well-being was analyzed using Order Probit regression and ordinary least squares (OLS) regression models, and the results are presented in Table 2. The results of both Order Probit and OLS regression analyses show that online education has a significant positive contribution to subjective well-being at the 1% significance level, indicating that the two present a very significant positive correlation. By comparing the AIC and BIC of the two models, it was observed that the values of Order Probit were smaller than those of the OLS regression model, so model 1 was superior. From model 1, it can be observed that among the control variables, gender has a significant negative effect on subjective well-being, and both age and interpersonal trust have a significant positive effect on subjective well-being.
The relationship between OE and Chinese residents’ SWB was analyzed using PSM. The OE encoded by two categories (0 = low-frequency group; 1 = high-frequency group) was the variable. Gender, age, education level, religious belief, and political identity were covariates. SWB was the output variable. The tendency score was calculated using the logistic model. The results of the logistic model are presented in model 3 in Table 3. In the logistic model, pseudo R2 = 0.1928, −2 log likelihood = 2887.1736. This means the model fitting effect was relatively good. Model 3 indexed that independent variables were significantly related to OE in different degrees, except gender, income, and healthcare security.
Columns 3 to 5 in Table 3 are the balance test results of NNM. Before matching, there were significant covariate differences between CG and IG (|t| = 0.21–22.18), such as age, education level, political identity, and registered residence. After matching, no covariates presented have significant differences between CG and IG (|t| = 0.05–1.25). The absolute value of standardized deviation of most variables was less than 5% except the deviation of education level (5.5%). The results show that the NNM process meets the balance hypothesis, which better balances the heterogeneity between CG and IG.
Figure 1 shows that the standardized deviation of CG and IG before and after the NNM. It shows that the standardized deviation of CG and IG becomes significantly smaller after NNM. The results of RM and KM are similar to those of NNM, which indexes that these three matching methods can all meet and support the hypothesis, and the selection of matching variables and methods is appropriate enough.
The impact of OE on Chinese residents’ SWB was analyzed by using NNM, RM, and KM separately (Table 4). Among them, CG was the low-frequency group, and IG was the high—frequency group. The average treatment effect on the treated (ATT) was a core index to test the effect of PSM. In this study, ATT was equal to the SWB of the high-frequency group minus the SWB of the low- frequency group.
In order to improve the matching accuracy of NNM based on one-to-one matching, a smaller matching tolerance (caliper = 0.05) was used in this research. The ATT was 0.189 (t = 2.52 > 2.33, p < 0.01) after NNM. It was significant at the level of 1%. The ATT was 0.183 (t = 3.16 > 2.33, p < 0.01) after RM. Additionally, the ATT was 0.184 (t = 3.17 > 2.33, p < 0.01) after KM. This shows that the results of the three matching methods all have a high robustness value, because the ATT of the three methods is similar, and the significance levels are the same. ATT > 0 after matching means that the SWB of IG was higher than that of CG. OE and SWB showed a significant relationship after effectively controlling the influence of covariates, such as age, education level, and political identity on SWB. It can be concluded that the difference in SWB between IG and CG was caused by the frequency difference of OE. Increasing the frequency of OE can significantly improve Chinese residents’ SWB, and hypothesis 1 is true.

4.2. The Analysis of the Mediating Roles

In order to analyze the mediating roles of social class mobility and tolerance between OE and Chinese residents’ SWB, bootstrap analysis was performed by using the process program. The sampling times were 5000, and the confidence interval was 95%. The NNM and whole samples were used separately to analyze the multiple mediating roles, respectively, and the results are presented in Table 5.
From the percentage of mediating effects presented in Table 5, it can be observed that the post-matching sample tests better, which indicates that the post-matching sample handles the endogeneity problem better and effectively reduces the influence of selectivity bias on the model, thus making the model results more accurate. Therefore, only the modeling results of the post-matching sample were analyzed in this paper. The constructed expressions of the mediating effect model are presented in Expressions (5)–(7)
S C M = 2.2831 + 0.0877 × O E − 0.1652 × g e n d e r + 0.1701 × e d u − 0.2228 × r e s i d e n c e − 0.0111 × a g e + 0.1904 × t r u s t
S T = 2.1514 + 0.0365 × O E + 0.1099 × g e n d e r + 0.0055 × a g e + 0.3503 × t r u s t
S W B = 1.8336 + 0.0462 × O E + 0.1863 × S C M + 0.176 × S T − 0.1509 × g e n d e r + 0.1063 × t r u s t
where, SCM and ST are social class mobility and social tolerance, SWB represents subjective well-being, OE represents online education, and edu represents education level.
The results of the analysis of the matched sample presented in Table 5 also show that mediating effect 1 of online education to subjective well-being via social class mobility and mediating effect 2 of online education to subjective well-being via social tolerance are both significant, and together they explain 32.90% of the total effect. Mediating effect 1 is 0.0163 (95% CI = [0.0078−0.0266]), which accounts for 23.62% of the total effect, and mediating effect 2 is 0.0064 (95% CI = [0.0012−0.0136]), which accounts for 9.28% of the total effect. Therefore, it can be determined that social class mobility and social tolerance can partially mediate the effect between online education and subjective well-being.
A visualization of the mediating effects was illustrated in conjunction with the mediating effects regression model (Figure 2). The mechanism of the parallel intermediary effect of social class mobility and social tolerance can be explained as follows: on the one hand, the frequency increase in OE can effectively promote social class mobility (beta = 0.0877, p < 0.01), and social class mobility helps to improve Chinese residents’ SWB (beta = 0.1863, p < 0.01). The first intermediary path is established in this way. On the other hand, the frequency increase in OE can effectively promote social tolerance (beta = 0.0365, p < 0.05), and social tolerance helps to improve Chinese residents’ SWB (beta = 0.1760, p < 0.01). The second intermediary path is established in this way. It can be concluded that hypotheses 2 and 3 are both valid at the same time based on the analysis results. The above regression coefficients all pass the significance test at the 5% level (or 1%), but the coefficient values are small, indicating that both mediating variables are partial mediating variables, i.e., the independent variables act on the dependent variable through the mediating variables on the one hand and act directly on the dependent variable on the other hand. The explanatory rate of the mediating effect is mainly seen through the percentage of the two mediating effects in Table 5. In the matched sample, mediating effect 1 accounts for 23.62% and mediating effect 2 accounts for 9.28%, and the coefficients of the mediating effects are both significant, indicating that the two variables of social class mobility and social tolerance have significant mediating effects between subjective well-being and online education.

5. Discussion

This study aimed to examine the relationship between online education and subjective well-being, using the propensity value matching approach to determine whether social class mobility and social tolerance play multiple mediating roles in online education and subjective well-being. The study drew inferences based on prior results on the relationship between education and well-being: positive online education participation behavior enhances the subjective well-being of Chinese residents. The results show that there is a significant positive relationship between online education and subjective well-being and that online education significantly and positively predicts subjective well-being. The results of this study are consistent with those of Mcintosh and Tett, and support the basic idea that education leads to happiness, i.e., education promotes and enhances subjective well-being when individuals gain positive subjective and objective experiences (improved employment skills, improved income, psychological health, and social adjustment, etc.) that are consistent with investment in education [57,58]. Although higher levels of education may reduce well-being to some extent for a specific group, positive online education participation behaviors result in benefits that are consistent with their educational investment for a larger group of recipients, such as better access to quality educational resources and effective compensation for knowledge and education. These results not only theoretically expand the range and perspective of research on subjective well-being but also contribute to the sustainable development of education.
A higher frequency of online education participation is associated with higher self-rated social class mobility scores, a result that is consistent with the previous work and validates the results of previous studies [59]. Since the reform and opening up, the return to education in Chinese society has been among the highest in the world [60]. For Chinese residents, education is the only way for intellectuals to fulfill their ambitions and honor their ancestors [61]. Especially after COVID-19, thanks to the convenience and flexibility of online education, it has promoted the sustainable development of education, which can empower more Chinese residents with the hope and expectation of upgrading their social class and inspire them to have a better vision of a happy life based on the effective expansion of individual access to knowledge. Furthermore, this study reaffirmed the human capital theory that individuals can obtain economic compensation for their development through education investment, and these economic compensations will promote and enhance happiness. Additionally, this empirical study proved that online education can create a different path for students to manage their knowledge, and they can develop different skills and obtain different employment opportunities due to the online experience [62].
According to the results of this study, the higher the self-rated score of social class mobility of individuals, the higher their perceived happiness, which is consistent with the results of Helliwell and other scientists [63]. Additionally, this result is consistent with the fact that social class identity predicts higher levels of subjective well-being, regardless of whether the identity is derived from subjective or objective social class [64]. Since social class mobility is reflected by the changes occurring in individual social status, the more opportunities for upward social class mobility in a society, the more dynamic the social development. According to Maslow’s hierarchy of needs theory, the pursuit of happiness is the ultimate goal of human beings. Additionally, the pursuit of happiness by individuals is highly correlated with their social class. People with a high social class can obtain more social resources, meet more spiritual and material needs of the individual, and their self-worth can be realized more easily, so they are happier. This suggests that to improve the subjective well-being of Chinese residents, we need to build a better and more extensive educational platform for social class mobility through online education.
The results of the mediating role analysis suggest that online education can also enhance subjective well-being by promoting social tolerance. Social tolerance is an important factor in achieving democratic consolidation and stability and contributes to the enhancement of subjective well-being [65]. The study showed that in response to the moral and ethical challenges of the Internet era, online education enables people to be more rationally aware of the way of thinking they should possess and work together to shape a more tolerant social environment. For contemporary China, which stands at the crossroads of ethics and morality, enhancing social tolerance, optimizing the social environment, and building a more harmonious social atmosphere is one of the feasible paths to enhance residents’ subjective well-being. Through this path, online education can significantly enhance people’s subjective well-being. This study broadened the explanatory space of the relationship between online education and subjective well-being, suggesting that social tolerance is an important reason why online education enhances subjective well-being.

5.1. Implications

The results of this study provide academic and practical implications for online education. The academic contributions include three aspects: first, it clarifies the relationship between online education and Chinese residents’ subjective well-being, which broadens the research range and perspective of subjective well-being. Although both online and conventional education contribute to subjective well-being, the impact of the COVID-19 pandemic on conventional education has forced society to pay more attention to online education and the impact it brings. This perspective provides a new explanatory framework for the study of happiness, and is also instructive for the continuous improvement of happiness. Second, it specifies the mediating role of social class mobility and social tolerance and explores the possible realization paths of the impact of online education on enhancing subjective well-being, which is complementary to the current research on the eudemonics effects of education. Third, based on national survey data obtained from China, the net effect of online education on subjective well-being is analyzed using the propensity value matching method, which makes the study’s results more reliable and more consistent with the actual situation.
From the perspective of education development, online education can respond to possible public emergencies. The role played by online education in promoting the sustainable development of education should be fully recognized. Promoting sustainable education worldwide is one of the goals of the UN agenda 2030 for education. Online education can provide more abundant resources and more convenient ways for people’s lifelong learning, and open up a broader virtual learning space for the sustainable development of education.
Currently, online education has become a new form of education in the history of human civilization. The booming development of online education is irreversible. Teachers need to take the initiative to embrace this new educational revolution, actively learn digital knowledge, and transform their teaching methods so that education recipients can enjoy a more efficient and better learning experience. Schools should actively promote the development of online education disciplines, establish a theoretical system of online education, and deeply explore development laws and basic methods of online education, so as to make positive theoretical responses to the development of education and the needs of the times. The Chinese government needs to vigorously develop online education based on the new requirements for sustainable development of education, actively establish a panorama of online education, and promote the swiftness of information flow of learning resources, comprehensiveness of online education content, personalization of learning paths, and diversification of education supply. This is as important as the economistic orientation of eudemonics for enhancing the subjective well-being of Chinese residents. In addition, this study determined that online education can also enhance people’s subjective well-being by promoting social class mobility and social tolerance. This provides an important theoretical reference for building a learning society more firmly and effectively. At the same time, this study can also make people aware of the importance and realistic value of online education and help more people choose diverse learning patterns based on their individual needs.

5.2. Limitations and Future Research Directions

There are still some limitations evident in this research. Firstly, online education’s influence on subjective well-being only starts from social class mobility and social tolerance, and the results show that the regression coefficients of these two research variables are small, indicating that there may be other mediating or moderating variables. In the future, it is necessary to further explore other possible paths of online education’s influence on subjective well-being in detail based on relevant theories, and constantly enrich the ways to improve residents’ SWB. Secondly, this study only analyzed OE as a whole, because the one-dimensional aggregation analysis was adopted from the original database. However, the content of OE is diverse, and its happiness effect is likely to be heterogeneous. Therefore, it is necessary to broaden the research and investigate the differential impact of different OE contents on SWB, and to attempt to discover what kind of teaching content of OE can profoundly affect the Chinese residents’ SWB.

6. Conclusions

The purpose of this study was to determine the impact of online education on the subjective well-being of Chinese residents and to analyze the specific mediating paths. This study determined that online education can directly and positively predict subjective well-being and indirectly influence subjective well-being through the multiple mediating effects of social class mobility and social tolerance. In the post-pandemic era, online education has become an important way to develop education in China. The results of this study could facilitate the precise development of online education in China. The impact of online education on subjective well-being is inseparable from social class mobility. Therefore, the development of online education should accurately grasp the educational needs of individuals; provide more effective educational resources in a targeted manner; meet the needs of learners with different backgrounds, preferences, and levels; and build a quality educational platform for social class mobility. At the same time, the social role of online education should be fully recognized, and people should be guided to establish an open mindset and work together to shape a more tolerant social environment.

Author Contributions

Conceptualization, S.L.; investigation, S.L.; formal analysis, S.L.; writing—original draft preparation, S.L.; visualization, S.L.; methodology, Y.C.; supervision, Y.C.; validation, Y.C.; data curation, Y.C.; project administration, H.Z.; writing—review and editing, H. Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Humanities and Social Sciences project of the Ministry of Education of China, grant number 21JDSZK006, the Natural Science Foundation of Fujian Province of China, grant number 2021J011224, and the Foundation of Department of science and technology of Fujian Province of China, grant number 2021H6003.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Acknowledgments

This work was supported by the Smart Home Information Collection and Processing on Internet of Things Laboratory of Digital Fujian.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 02177 g001 550
Figure 1. NNM-standardized deviation diagram of each variable.
Figure 1. NNM-standardized deviation diagram of each variable.
Sustainability 15 02177 g001
Sustainability 15 02177 g002 550
Figure 2. The intermediary relationship between OE and SWB and non-standardized regression coefficient. **p < 0.05, *** p < 0.01.
Figure 2. The intermediary relationship between OE and SWB and non-standardized regression coefficient. **p < 0.05, *** p < 0.01.
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Table
Table 1. Descriptive statistical analysis of variables.
Table 1. Descriptive statistical analysis of variables.
VariablesDefining and Assigning Values to VariablesMeanSD
Dependent variablesSubjective well-beingSelf-evaluation of subjective well-being (1 = very unhappy, 2 = unhappy, 3 = not sure, 4 = happy, 5 = very happy)4.041.03
Independent variablesOnline educationFrequency of participation in online education (0 = low-frequency group, 1 = high-frequency group)0.370.48
Mediating variablesSocial class mobilitySubjective perception (1 = lower, 2 = lower middle, 3 = middle, 4 = upper middle, 5 = upper)2.981.05
Social toleranceTolerance level (1 = very intolerant, 2 = relatively intolerant, 3 = moderately tolerant, 4 = relatively tolerant, 5 = very tolerant)3.640.82
Control variablesDemographic factorsGenderGender (1 = female, 2 = male)1.470.50
AgeAge (actual age = 2019 − year of birth)41.813.16
Education levelEducation level (0 = high school and below, 1 = college and above)0.290.45
Religious beliefReligious faith (0 = no, 1 = yes)0.140.34
Political identityPolitical status (0 = non-Communist Party member, 1 = Communist Party member)0.140.35
Registered residenceHousehold registration (0 = agricultural hukou, 1 = non-agricultural hukou)0.400.49
Individual income in 20182018 annual individual income converted to logarithm as variable4.360.58
Social security and social capitalPension securityPension security (0 = no, 1 = yes)0.590.49
Healthcare securityHealthcare security (0 = no, 1 = yes)0.860.34
Interpersonal trustInterpersonal trust (1 = very distrustful, 2 = relatively distrustful, 3 = moderately trustful, 4 = relatively trustful, 5 = very trustful)3.440.89
Table
Table 2. Regression analysis results of OE on Chinese residents’ SWB.
Table 2. Regression analysis results of OE on Chinese residents’ SWB.
VariablesModel 1 (Order—Probit)Model 2 (OLS)
Gender−0.1212 *** (0.0455)−0.1079 *** (0.0408)
Age0.0048 ** (0.0022)0.0031 (0.0208)
Education−0.0552 (0.0616)−0.011 (0.0551)
Religious belief0.0363 (0.0647)−0.0004 (0.0578)
Political identity0.0834 (0.0705)0.1057 * (0.0627)
Registered residence−0.0309 (0.0514)0.0033 (0.0461)
Income (take logarithm)0.056 (0.0413)0.0637 * (0.0371)
Pension security−0.0704 (0.0542)−0.0541 (0.0487)
Healthcare security0.0984 (0.0673)0.1273 ** (0.0606)
Interpersonal trust0.2406 *** (0.0252)0.2158 *** (0.0224)
OE0.0704 *** (0.0156)0.0621 *** (0.0139)
N25812581
Pseudo R20.0242
−2log likelihood5476.0489
F 13.79
adj R2 0.0517
AIC55067352.4
BIC5593.97422.7
Note: * p < 0.1, ** p < 0.05, *** p < 0.01; standard error in brackets.
Table
Table 3. Logistic regression analysis of predictive propensity value and balance test results of NNM.
Table 3. Logistic regression analysis of predictive propensity value and balance test results of NNM.
VariablesModel 3t-Value before NNMt-Value after NNMbias after NNM (%)
Gender−0.1480 (0.0940)−0.640.080.3
Age−0.0617 *** (0.0045)−18.82 ***0.893.5
Education level1.1779 *** (0.1232)22.18 ***1.165.5
Religious belief0.2933 ** (0.1305)5.80.160.7
Political identity1.1875 *** (0.1504)8.64 ***−0.05−0.2
Registered residence0.2009 * (0.1066)0.88 ***0.170.7
Income (take logarithm)0.0844 (0.0866)5.82 ***−1.01−4.2
Pension security0.4019 *** (0.1108)0.640.321.3
Healthcare security −0.0543 (0.1370)0.21−1.25−4.8
Interpersonal trust0.1313 *** (0.0511)4.72 ***−0.45−1.7
Number2581
Pseudo R20.1928
−2 log likelihood2887.1736
Note: * p < 0.1, ** p < 0.05, *** p < 0.01; standard error in brackets.
Table
Table 4. Results of PSM analysis.
Table 4. Results of PSM analysis.
Matching MethodM (IC)M(CG)ATTs. e.t-Value
NNMBefore matching4.15403.93490.219 ***0.0405.43
After matching4.15413.96510.189 ***0.0752.52
RMBefore matching4.15403.93490.219 ***0.0405.43
After matching4.15413.97110.183 ***0.0583.16
KMBefore matching4.15403.93490.219 ***0.0435.43
After matching4.15413.97030.184 ***0.0583.17
Note: *** p < 0.01; NNM, caliper = 0.05; RM, caliper = 0.05.
Table
Table 5. Results of multiple mediating role test (bootstrap= 5000).
Table 5. Results of multiple mediating role test (bootstrap= 5000).
Influence RoleAfter Matching Before Matching
effect
(se/bootse)		95% CI④effect
(se/bootse)		95% CI④
Total effect
OE→SWB0.0690
(0.0196)		[0.0305, 0.1076]0.0621 (0.0139)[0.0349−0.0893]
Direct effect
OE→SWB0.0462
(0.0193)		[0.0083, 0.0842]0.0480 (0.0136)[0.0214−0.0747]
mediating role 1
OE→SCM①→SWB0.0163
(0.0048)		[0.0078, 0.0266]0.0112 (0.0029)[0.0057−0.0171]
Proportion of mediating role 1⑤23.62% 18.04%
mediating role 2
OE→ST②→SWB0.0064
(0.0032)		[0.0012, 0.0136]0.0029 (0.0016)[0.0002−0.0064]
Proportion of mediating role 2⑤9.28% 4.67%
Proportion of total mediating role 32.90% 22.71%
N③10292581
Note: Total effect, direct, and intermediary effect test all use 5000 bootstrap repeated sampling to obtain 95% confidence intervals. ① SCM = social class mobility; ② ST = social tolerance; ③ N = number; ④ CI = confidence intervals; ⑤ proportion of mediating role = mediating role/total effect.
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Liu, S.; Cao, Y.; Zhang, H. Online Education and Subjective Well-Being in China: Multiple Mediating Roles of Social Class Mobility and Social Tolerance. Sustainability 2023, 15, 2177. https://doi.org/10.3390/su15032177

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Liu S, Cao Y, Zhang H. Online Education and Subjective Well-Being in China: Multiple Mediating Roles of Social Class Mobility and Social Tolerance. Sustainability. 2023; 15(3):2177. https://doi.org/10.3390/su15032177

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Liu, Shuang, Yan Cao, and Hao Zhang. 2023. "Online Education and Subjective Well-Being in China: Multiple Mediating Roles of Social Class Mobility and Social Tolerance" Sustainability 15, no. 3: 2177. https://doi.org/10.3390/su15032177

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