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Article

Association between Self-Efficacy and Learning Conformity among Chinese University Students: Differences by Gender

by
Bin Xiao
and
Guandong Song
*
School of Humanities and Law, Northeastern University, Shenyang 110167, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(14), 8725; https://doi.org/10.3390/su14148725
Submission received: 23 May 2022 / Revised: 14 July 2022 / Accepted: 15 July 2022 / Published: 17 July 2022
(This article belongs to the Special Issue Advances in Psychophysiological Evaluation: Towards a Better Understanding of the Stress Response and Sustainable Lifestyle)
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Abstract

:
Background: Although past research highlights the impact of self-efficacy on university students’ learning motivation, examining potential links with specific types of learning conformity is limited. The current study examined associations between Chinese university students’ perceived self-efficacy and learning conformity across different types of learning motivation.Methods: A total of 339 Chinese university students were surveyed using the General Self-Efficacy Scale and the Learning Conformity Scale. Multiple regression models were constructed to focus on the mechanisms of general self-efficacy on learning conformity. Results: (1) There are three types of learning conformity: learning abidance, learning obedience, and learning compliance. (2) General self-efficacy has a negative effect on learning obedience. In contrast, it positively affects learning abidance and learning compliance. (3) The general self-efficacy of girls is lower than that of boys. Still, girls are more likely to be motivated to learn compliance than boys. Conclusions: The study reveals that it is essential to stimulate students’ motivation to learn abidance to love learning from the inside out; to improve girls’ self-efficacy; to raise students’ awareness of self-respect and self-development; and to encourage self-approval in public institutions.

1. Introduction

Conformity is a widespread psychosocial phenomenon. The earliest research on conformity can be traced back to 1759, when Adam Smith understood conformity as herding, also known as herd behavior, in his book The Theory of Moral Sentiments [1]. In recent years, conformity has been the subject of many classic studies in higher education [2]. Of particular concern is research regarding conformity, which is also critical to university students’ learning [3]. Several studies show that the study of learning conformity focuses on exploring the internal psychological laws of university students’ learning [4]. Learning conformity reflects the differences in learning motivation of different student groups and has important practical significance for motivating students to learn and generate learning gains [5]. There is some controversy regarding the concept of conformity, however. Based on behaviorist psychology, some scholars define conformity as individuals being influenced by social pressure to produce behaviors consistent with that of others [6]. Based on information processing psychology, some scholars define conformity as the motivated choice of behavior of individuals influenced by social information [7]. Some scholars divide conformity into compliance and acceptance [8], and others split conformity into compliance, obedience, and acceptance [9]. Surprisingly, current research is vague about the definition of learning conformity. Therefore, this paper defines learning conformity by drawing on the definition of conformity from information-processing psychology. Learning conformity refers to the motivated selective behavior of university students influenced by social information. Concerning the importance of motivation, researchers have noted that people with high levels of motivation are more likely to succeed in harsh learning situations or high-stress social situations, and are also more likely to achieve their dreams [10,11,12].
To investigate the current status of learning conformity among Chinese university students, we designed the Learning Conformity Scale (see the research methods section for basic information about the study design and population). During the scale design process, we found that there were indeed significant motivational differences in university students’ learning behaviors. Some students believe that learning is to achieve ideals and values, some believe learning is only a task at this stage, and some believe learning is a lifelong hobby. The individual cognitive and behavioral contradictions behind such differences in learning motivation are considered. However, there are also gender differences in subjects’ interpretations and feedback about learning motivation. Thus, we argue that university students’ motivations to learn conformist behaviors depends on individual differences and are influenced by multiple factors.
Based on the scale study and previous studies in the literature, the present study sought to explore the classification of motivational differences in learning conformity and the reasons that may influence differences in learning conformity. To elaborate on the significance and value of this study in more detail, we explored the causal relationships and mechanisms of learning conformity among university students in the following aspects: learning motivation, self-efficacy, and gender differences.

1.1. Learning Conformity Research

The definition of learning conformity noted above refers to motivating behaviors influenced by social information. However, scholars’ current research regarding learning conformity has focused on two main areas: social learning and individual learning. Within the social learning framework, some scholars point out that conformity promotes the development of a culture based on the social learning perspective, and that learning conformity behaviors evolve when the only problem faced by individuals is the cooperation dilemma [13]. Within the framework of individual learning, Levett-Jones et al. examined nursing students’ conformity behaviors in clinical placements, noting that nursing students need to adapt to the normative behaviors of the professional team, comply with clinical requirements, and become confident practitioners [14]. Fukushima and Sharp et al. argued that compared to American college students, Japanese college students were less likely to be transgressors [15]. This means that they are more likely to develop conformity behaviors. The above studies affirm the critical value of learning conformity research, but few studies delineate the psychological motivation for learning conformity.
Based on this, we found that learning conformity can be categorized into learning abidance, obedience, and compliance, drawing on scholarly classifications of conformity. In particular, learning abidance refers to the process of students’ internalized interest in and enjoyment of learning, where cognition and behavior align. Learning obedience is the process students learn to avoid being punished by authority, where cognition is inconsistent with behavior [16]. Learning compliance is how students learn to make their family and friends happy; again, cognition and behavior are inconsistent [17].

1.2. Learning Motivation Research

Motivation is the direct cause of student learning; motivation directly causes or sustains student learning behavior [18]. University students’ learning motivations are affected by personal cognition. Since the 1980s, scholars have studied the relationship between general self-efficacy and learning motivation [19]. These studies show that general self-efficacy not only affects an individual’s intrinsic motivation and behavior, but also affects an individual’s psychological endurance, academic achievement, growth, and development in a positive way. Generally, students with higher self-efficacy tend to have a more substantial academic level, learning ability, and apparent learning motivation [20].
Some scholars have also gradually begun to observe the relationship between academic motivation and academic conformity. Specifically, students who have a passion for academics and pursue them tend to produce conformity behaviors to cutting-edge educational issues more readily than students without academic pursuits [21].

1.3. Self-Efficacy Research

Although learning conformity is consistently associated with the self-awareness of university students, school policy pressure, social pressure, self-development, and academic success, research linking subcontracts of learning conformity and a specific perception of self-efficacy is more limited [22,23,24]. There is no research regarding the relationship between general self-efficacy and learning conformity in the current literature. This study begins to fills that gap.
American psychologist Bandura first proposed the concept of self-efficacy [25]. Self-efficacy is an individual’s subjective judgment and evaluation about whether they can achieve a specific accomplishment or complete a particular job [26]. It has been noted that self-efficacy is not simply a verbal expression of university students based on their self-perception and self-competence; it is produced via cognitive processing, and individuals with different self-efficacies produce different motivated behaviors for learning [27]. General self-efficacy, as the inner cognition of individual subjective achievement and competence, may affect the motivation choice of university students to learn conformity [28]. Some studies note that higher self-efficacy has an increased degree of influence on individual health conformity [29,30].

1.4. Gender Differences Research

There is also evidence of the importance of gender difference [31,32]. Gender issues are a long-standing issue in the training of college students. Numerous studies in the West show that gender differences are evident in academic performance, motivation, and motivation, with males often having an advantage [33]. Guided by situational expectation values and mindset theory, Lee and Yu et al. examined undergraduate physics courses and found that girls are more likely to be affected by fixed mindsets, which damage their self-efficacy and self-confidence in physics learning [34]. Kalender and Marshman et al. noted that girls have lower self-efficacy and motivation in physics learning than in high school students [35]. However, some studies do not support this finding. Dökme et al. examined female science and engineering students’ basis for learning in STEM fields. The purpose of female science education students in STEM fields can be considered long-term sustainable and pervasive impact, as they are potential future educators [36]. Patall and Steingut examined differences in academic achievement and motivation in science studies between girls and boys in high school; no significant differences were found in biology courses [37].
Using a sample of Chinese university students, we aim to replicate and extend past research linking self-efficacy to learning conformity. This study adds to the literature by raising the limited body of research regarding the social information that influences university students’ learning motivation (i.e., self-efficacy information) as they relate to learning conformity, as well as examining the classification of motivations for learning conformity. Based on the above analysis, current studies emphasize the influence of self-efficacy on learning motivation; however, few focus on the effect of general self-efficacy on learning conformity. At the same time, it can be seen that gender differences in different learning situations are distinct; however, studies of learning conformity rarely involve gender differences. This study considers exploring the differences in learning conformity and general self-efficacy among university students of different genders significant.
In summary, this study has three primary objectives: 1. To investigate the motivation classification of university students’ learning conformity behavior. 2. To explore the relationship between self-efficacy and learning conformity. 3. To ascertain the gender difference. In addition, this study has two research hypotheses. First, self-efficacy positively affects learning conformity, but affects different types of learning conformity separately. Second, gender makes a difference in both self-efficacy and learning conformity.

2. Materials and Methods

2.1. Participants and Procedure

The sample size for the university student learning conformity study was first determined based on a priori power analysis, setting α fixed at the conventional level of 0.05 and powers of 0.80 [27], as well as assuming a small effect size (r = 0.20) [27]. The power analysis indicated that the minimum sample size required for this study was n = 194.
Study participants are undergraduate students enrolled in Chinese universities recruited through convenience sampling. Students from five universities in Shenyang, northeastern China, were randomly selected from November 2021 to January 2022. Participants came from Northeastern University, Liaoning University, Agricultural University, Polytechnic University, and Architectural University. In our survey, participants could withdraw at any point without being required to complete the questionnaire.
The researchers invited Chinese university students to participate in the ‘Conformity’ study via an email containing a link to the online survey. Recipients were asked to share the online invitation with their classmates by posting the ‘Conformity’ study link on their social networking platforms. Each student provided informed consent by clicking on the tab: “Yes, I agree to participate in the ‘Conformity’ study”. The final group of 390 undergraduates was recruited at random again. Fifty-one responses were deemed insufficient, with at least 20% of the items left unanswered. Therefore, only 339 subjects were eligible for the final analysis, resulting in an 86.9% valid response rate. Throughout, all participants’ information was guaranteed to remain anonymous and confidential. The Ethics Committee of the Northeastern University of China approved this study.
Participant data for 339 university students (19.35 ± 2.62 years old, from freshman to senior year, 39% male and 51% female) were analyzed. Several indicators were also examined: 168 students attended double-degree universities; 171 students attended traditional institutions; 51% of students were from urban areas; and 49% of students were from rural regions. The gender classification in the sample is well balanced and represents the robustness of the data. Disciplines include 98 humanities and social sciences students, 111 students in engineering and technology, and 130 students in agricultural subjects. The sample analysis of students’ family situations shows a balanced distribution of parents’ income, which is in line with general societal income. The distribution of parents’ education levels also reflects the education level of different societal groups and indicates the reasonableness of data selection.

2.2. Measures

The questionnaire used in this paper contains the following three main parts.
(1) Personal basic information questionnaire.
The respondents’ basic personal, professional, and family information are considered. This part of the questionnaire covers personal information about gender, grade, place of origin, school level and major, as well as basic family information including father’s education level, mother’s education level, and total monthly family income.
(2) General Self-Efficacy Scale (GSES).
The General Self-Efficacy Scale (GSES) was revised and tested by Schwarzer [38]. The scale was scored on a 4-point Likert scale, with one being wholly incorrect and four being entirely correct; the sum of all the items scored was the General Self-Efficacy Score, with the total score ranging from 10 to 40. The reliability and validity results of the scale applied to this paper showed that the Cronbach alpha coefficient was 0.982, the KMO value was 0.978, and the Bartlett test p-value was 0.000, indicating that the scale has good reliability and validity. International contexts have extensively used the GSES scale. The literature indicates that the Chinese version of the GSES is also reliable and valid when administered to Chinese populations [39].
(3) Motivation for Learning Conformity Scale.
In designing the survey items, reference was made to research conformity classification in the Scientific Research Conformity Scale (SRC) proposed by Song et al. [40], as well as the “conformity scale of students using Facebook” in the Facebook Conformity Scale (FCS) offered by Sun et al. [2]. A 5-point Likert scale, including the ranking of “strongly adherent, moderately adherent, average, somewhat non-adherent, and strongly non-adherent”, was designed. The higher the score, the greater the tendency to comply with the behavior. The items were categorized as learning abidance (7), learning obedience (6), and learning compliance (5), for a total of 18 items. Sample items from the Learning Conformity Scale constructs are shown in Table 1.
The exploratory factor analysis (EFA) resulted in a KMO statistic of 0.93, and Bartlett’s spherical test (p < 0. 001) also possessed statistical significance (supplementary Table S1). Using Kaiser’s research, a KMO value greater than 0.80 is more consistent with factor analysis [41]; the KMO of the learning conformity scale fits this criterion perfectly. The final three factors were extracted: learning abidance, containing five items with factor loadings between 0.89 and 0.91, and an explanatory variable of 37.5%; learning obedience, containing five items with factor loadings between 0.88 and 0.89, and an explanatory variable of 28.2%; and learning compliance, containing four items with factor loadings between 0.81 and 0.91, and an explanatory variable of 21.3%. Cronbach’s alpha value of 0.93 across the instrument was more significant than the 0.70 recommended by Ledyard [42].
This was followed by confirmatory factor analysis (CFA). The overall model fit test revealed that χ 2 (74) = 0.963 and p = 0.57 were statistically significant. The model fit indicators also yielded CFI = 1, GFI = 0.97, RMSEA = 0.00, and RMR = 0.05, all of which met the criterion of a good fit [43]. Table 2 shows that the three factors’ composite reliability (CR) ranged from 0.93 to 0.97, and AVE ranged from 0.78 to 0.88. This indicates that the learning conformity scale has good reliability and validity [44]. As for discriminant validity, learning abidance was 0.921, learning obedience was 0.938, and learning compliance was 0.886, indicating that the scale has relatively good discriminant validity (Table 3).

2.3. Data Analysis

First, we performed a descriptive analysis and definition of the variables used in the OLS model as a basis for determining the model data.
Second, we used OLS regression analysis with SPSS 23.0 software, designed by the American International Business Machines Corporation (IBM) headquartered in Armonk, NY, USA. The dependent variables were learning abidance, learning obedience, and learning compliance; the independent variable was self-efficacy. Control variables included place of origin, parental income, and parental education. Statistical significance was determined by p < 0.05. Using OLS regression, the relationship between the dependent and independent variables can be represented, and significance can be determined.
Next, gender differences were explored in this study; the method adopted was ANOVA analysis, and the software was SPSS 23.0. The technique focuses on the variability of X for Y, where X is fixed category data and Y is quantitative data. In this paper, X is male and female, and Y is learning conformity and self-efficacy. In the detailed analysis, p-values were analyzed first, and if p < 0.05, the groups showed differences; specific differences were then compared to the mean. Data analysis and processing steps are illustrated in Figure 1 to address these questions.

3. Results

3.1. Variable Setting

This section sets and describes the variables used in the OLS regression; the primary setting criteria are shown in Table 4. First, the dependent variables in the multiple regression model were learning abidance, obedience, and compliance. The values taken are the average scores of the scale questions. Second, an independent self-efficacy variable takes the value of the total score on the scale combined. Third, the values of the remaining variables, such as place of origin, grade, and subject, are dummy variables expressed as continuous scores. The table also reflects all variables’ mean and standard deviation.

3.2. Regression Analyses

First, self-efficacy was used as an independent variable for OLS regression analysis, and was studied using the robust standard error regression method. As seen in Table 5, the model r-squared value of 0.642 implies that these indicators can explain 64.24% of the change in learning abidance. When the F-test was performed, the model passed the F-test (F = 165.010, p = 0.000 < 0.05). Self-efficacy had a significant positive effect on learning abidance (t = 18.347, p = 0.000 < 0.01). Place of origin had a significant negative effect on learning abidance (t = −12.579, p = 0.000 < 0.01). Parental monthly income significantly positively affects learning abidance (t = 2.119, p = 0.034 < 0.05), as do father’s education (t = 2.921, p = 0.003 < 0.01) and mother’s education (t = 3.690, p = 0.000 < 0.01).
Second, OLS regression analysis was conducted with self-efficacy as the independent variable, and learning obedience as the dependent variable. As seen in Table 6, the model R-squared value is 0.657, implying that these indicators can explain 65.71% of the change in learning obedience. When the F-test was performed, the model passed the F-test (F = 104.601, p = 0.000 < 0.05). Self-efficacy had a significant negative effect on learning obedience (t = −14.963, p = 0.000 < 0.01). Place of origin significantly positively affects learning obedience (t = 21.704, p = 0.000 < 0.01), as do parental monthly income (t = 3.149, p = 0.002 < 0.01) and mother’s education (t = 2.444, p = 0.015 < 0.05).
Third, OLS regression analysis was conducted with self-efficacy as the independent variable and learning compliance as the dependent variable. As shown in Table 7, the model R-squared value is 0.692, implying that these indicators can explain 69.16% of learning compliance change. When the F-test was performed, the model passed the F-test (F = 74.277, p = 0.000 < 0.05). Self-efficacy had a significant positive relationship with learning compliance (t = 12.084, p = 0.000 < 0.01). Place of origin significantly negatively affects learning compliance (t = −13.972, p = 0.000 < 0.01), as do parental monthly income (t = −6.054, p = 0.000 < 0.01), father’s education (t = −4.047, p = 0.000 < 0.01), and mother’s education (t = −2.747, p = 0.006 < 0.01).

3.3. Gender Differences

The results of the ANOVA are provided in Table 8. According to the requirements of the ANOVA, it is clear that gender differences are significant only in self-efficacy (F = 4.367, p = 0.000 < 0.01) and learning compliance (F = 1.073, p = 0.000 < 0.01). No differences were reflected in learning abidance and obedience (p > 0.05). Once significance was determined, the differences in variance were reflected by the comparison of means. Gender for learning compliance showed that the mean for male students (6.2) was lower than the mean for female students (15.87). Figure 2 and Figure 3 show visual plots of gender differences in self-efficacy and learning compliance.

4. Discussion

4.1. Classification of Learning Conformity

Regarding the first research question, confirmatory factor analysis (CFA) revealed that the learning conformity scale can be divided into learning abidance, learning obedience, and learning compliance. At the same time, the Learning Conformity Scale has good reliability and discriminant validity. Although previous studies investigated the classification of conformity, there is still a lack of specific exploration of the types of learning conformity [45,46].
Some scholars classify conformity as rational or irrational, while others classify conformity as acceptance, compliance, and obedience [8,9,47]. Unlike previous studies, this study categorizes learning conformity as learning abidance, learning obedience, and learning compliance, based on exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). This division is more meaningful not only at the data level, but also in confirming that learning conformity behavior is formed by the influence of different learning motivations. It supports the link between learning conformity and learning motivation research at the theoretical level.

4.2. The Association between Self-Efficacy and Learning Conformity

Regarding the second research question, self-efficacy positively affects learning conformity. This was inconsistent with expectations, as self-efficacy affects different types of learning conformity in various orientations. However, previous studies investigated the effect of general self-efficacy on student motivation and concluded that general self-efficacy significantly affects learning motivation [24,48,49]. Unlike previous studies, this paper concludes that general self-efficacy has a negative effect on learning obedience and a positive impact on learning abidance and learning compliance. The authority of the teacher is related to students’ motivation and self-efficacy—the more authoritative the teacher, the lower the students’ self-efficacy and motivation to learn [50]. As individual self-efficacy represents students’ self-confidence and self-awareness in schooling, external rewards and punishments in school tend not to affect the self-awareness of students with higher general self-efficacy [51]; therefore, general self-efficacy will negatively affect learning obedience. This is consistent with our hypothesis, from which we also hypothesize that individual self-efficacy differences manifest differently in learning behaviors; this indirectly confirms the importance of self-efficacy in terms of personal growth and motivation to learn [28,52,53].

4.3. Differences by Gender

The third research question concerns differences in self-efficacy and learning conformity by gender. Interestingly, the general self-efficacy of female college students was lower than that of male students. Still, female students were more likely to be motivated to learn to comply [35]. University students are at an essential stage of self-awareness, individual development, and thinking metamorphosis; their cognition of the subject, psychological development, and cognitive tendencies are more malleable [37]. If they are faced with insufficient family support or negative educational regulation, low self-efficacy phenomena, such as low self-esteem and self-denial, may occur [54]. The results of this study side-step the glass ceiling phenomenon, in which families and society expect different things from male and female students in university due to the traditional Chinese cultural stereotype that male students are better at studies and careers [55]. It is widely believed that female students are not worthy of study or cannot achieve higher levels of success after graduation. This traditional social perception can lead to negative cues for girls about their academic and professional careers, resulting in lower self-efficacy. Some studies note that young girls are more likely to fall into traditional gender roles due to less social experience [56]. However, the analysis in this paper shows that there is no gender difference in girls’ motivation to pursue a love of learning or to achieve personal goals after receiving a university education. This also reflects that education will allow girls to advance in their cognitive and emotional pursuits.
Even though many girls still choose to study hard to meet the expectations of their families or friends, it is a fact that getting a university education will weaken gender inequality [57].
On the one hand, families should pay attention to leadership and the cultivation of self-confidence in the process of encouraging and educating their children, and should also pay more attention to valuable guidance, and the individual academic needs and social expectations of girls to avoid a greater gender bias in their theoretical support for their children. Further, because girls are instilled with more family awareness through traditional Chinese family parenting, they are more likely to be influenced by their parents or relatives to produce cognitive differences [58]. On the other hand, we advocate targeted family education for college students. We should actively promote proper guidance for parents regarding college students’ academic performance, academic development, and academic planning [59] to enhance their children’s self-efficacy and correct academic motivation. Moreover, university students’ learning motivation and academic development plans are not static, but will show dynamic development with changes in an educational environment, family support, and individual cognition [60]. University teachers should also provide targeted guidance to convey social and career aspirations more clearly to students to develop correct academic perceptions and motivation.

4.4. Practical Application

Knowing that differences in self-efficacy affect learning motivation, this study has important implications for cognitive differences in learning motivation across university students, as well as practical applications for self-efficacy to motivate students to comply with learning. Furthermore, future researchers may focus not only on self-efficacy as an important factor in students’ internal cognition, but also on the influence of external factors on students’ motivation to learn to comply. From this point of view, the development of student conformity behavior is influenced by a combination of intrinsic and extrinsic factors. This study will advance the practical implications of “behavioral and cognitive” research on student learning.

5. Conclusions

These findings add to the growing body of evidence showing the importance of positive self-efficacy and its association with student learning motivation. Additionally, differences in the school environment will affect the learning conformity behaviors of individuals differently, which will in turn lead to individual differences in learning motivation. Therefore, in the face of the immediate learning demands of university students, universities, society, and families should pay attention to positively guiding the learning natures of university students. This can be accomplished by enhancing the exploration of the nature of “student-centered” education, stimulating students’ motivation to learn from the inside out and love learning, and realizing the academic mission of university students empowered by society and the times. Further, the management mode of university education in public colleges and universities should pay more attention to students’ psychological development, basic learning aspirations, and self-growth aspirations. This will enhance the rise of self-respect, self-development, and self-approval consciousness of students in public colleges and universities by increasing their level of schooling.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su14148725/s1, Table S1: Linear combination coefficients and weighting results.

Author Contributions

B.X.: conceptualized data analysis, conducted the literature review, and helped draft the article. G.S.: provided thesis ideas and prepared the essay. All authors have read and agreed to the published version of the manuscript.

Funding

This project was supported by the National Social Science Foundation (Project ID: 15BSH093).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki. The project was approved by the Ph.D. Program Committee of Northeastern University, and approved by the Institutional Ethics Committee of Northeastern University (Project Identification Code: 10/11/2021).

Informed Consent Statement

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

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors without undue reservation.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 14 08725 g001 550
Figure 1. Data analysis steps in this study.
Figure 1. Data analysis steps in this study.
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Figure 2. Gender differences in self-efficacy.
Figure 2. Gender differences in self-efficacy.
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Figure 3. Gender differences in learning compliance.
Figure 3. Gender differences in learning compliance.
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Table
Table 1. Description and sample items of the Learning Conformity Scale.
Table 1. Description and sample items of the Learning Conformity Scale.
Scale ItemsDescription and Sample Items
Learning abidanceI am passionate about learning and enjoy learning from the inside out.
Learning obedienceStudying is a way to improve my GPA and gain access to graduate school or higher education.
Learning complianceI get good academic performance so that the family can be honored.
Table
Table 2. Convergent validity test of the Learning Conformity Scale (CFA).
Table 2. Convergent validity test of the Learning Conformity Scale (CFA).
DimensionAbbreviation of ItemUnStd. Coef.S.E.z-ValuepStd.
Coef.		CRAVE
Learning AbidanceA1: Passion for learning1 0.9280.9650.848
A2: Interest in learning0.9770.03230.083**0.917
A3: Enjoys learning0.9880.03329.95**0.914
A4: Endorsement learning0.9950.03230.915**0.923
A5: Learning is an ability0.9790.03230.598**0.922
Learning
Obedience		B1: Study for scholarships1 0.9410.9730.879
B2: Study for further education1.0240.02935.539**0.942
B3: Study for fame1.0120.02935.15**0.94
B4: Study to avoid punishment0.9880.02933.905**0.931
B5: Study for a job1.0290.0334.236**0.933
Learning ComplianceC1: Study for family happiness1 0.8930.9360.785
C2: Study to avoid disappointing the family0.9380.03923.773**0.883
C3: Study to make parents and friends enjoyable1.0150.04125.009**0.905
C4: Study can contribute to family prosperity0.9490.04222.593**0.863
** p < 0.01.
Table
Table 3. Discriminant validity test of the learning conformity scale.
Table 3. Discriminant validity test of the learning conformity scale.
DimensionsDiscriminant Validity
Learning AbidanceLearning ObedienceLearning Compliance
Learning Abidance0.921
Learning Obedience0.4210.938
Learning Compliance0.2380.3280.887
Table
Table 4. Variable Definition and Description.
Table 4. Variable Definition and Description.
Variable CategoryVariable NameVariable DefinitionMeanStd. Error
Dependent variableLearning AbidanceAverage scores for items in learning abidance3.3751.485
Learning ComplianceAverage scores for items in learning compliance2.8071.406
Learning ObedienceAverage scores for items in learning obedience3.2791.450
Independent variableSelf-efficacyTotal self-efficacy score (0–40)32.7810.57
Place of originUrban = 1, Rural = 0,1.4180.494
GradeFreshman = 1, Sophomore = 2, Junior = 3, Senior = 42.2641.176
SubjectsHumanities and social = 1, Science and technology = 2, Agricultural sciences = 31.8701.347
Parental monthly
income		≤1000 RMB = 1, 1001–2000 RMB = 2, 2001–5000 RMB = 3, 5001–7000 RMB = 4, ≥7001 RMB = 53.3172.011
Father’s educationIlliterate = 1, Primary = 2, Junior High School = 3,
Middle or High School = 4, Undergraduate = 5		3.1501.405
Mother’s educationIlliterate = 1, Primary = 2, Junior High School = 3,
Middle or High School = 4, Undergraduate = 5		3.0161.518
Table
Table 5. OLS regression results of self-efficacy on learning abidance (n = 339).
Table 5. OLS regression results of self-efficacy on learning abidance (n = 339).
Coef.Std. Err.tp95% CIR²F-Test
Constant17.4431.31113.3080.000 **14.874~20.0120.642F (7331) = 165.010, p = 0.000
Self-efficacy−0.2750.01518.3470.000 **0.304~0.246
Place of origin−6.0200.479−12.5790.000 **−6.958~−5.082
Grade−0.2530.222−1.1420.254−0.688~0.182
Subjects0.2310.3170.7280.467−0.391~0.853
Parental monthly income0.5040.2382.1190.034 *0.038~0.969
Father’s education0.8850.3032.9210.003 **0.291~1.478
Mother’s education1.1710.3173.6900.000 **0.549~1.793
* p < 0.05, ** p < 0.01; D-W: 0.569.
Table
Table 6. OLS regression results of self-efficacy on learning obedience (n = 339).
Table 6. OLS regression results of self-efficacy on learning obedience (n = 339).
Coef.Std. Err.tp95% CIR²F-Test
Constant−18.9371.521−12.4520.000 **−21.917~−15.9560.657F (7331) = 104.601, p = 0.000
Self-efficacy0.2980.020−14.9630.000 **−0.259~−0.337
Place of origin11.4710.52921.7040.000 **10.435~12.507
Grade0.2490.2341.0620.288−0.211~0.708
Subjects−0.1500.341−0.4410.659−0.818~0.518
Parental monthly income0.8510.2703.1490.002 **0.321~1.381
Father’s education−0.5480.322−1.7010.089−1.180~0.084
Mother’s education0.9230.3782.4440.015 *0.183~1.664
* p < 0.05, ** p < 0.01; D-W: 0.902.
Table
Table 7. OLS regression results of self-efficacy on learning compliance (n = 339).
Table 7. OLS regression results of self-efficacy on learning compliance (n = 339).
Coef.Std. Err.tp95% CIR²F-Test
Constant17.8181.13115.7560.000 **15.602~20.0350.692F (7331) = 74.277, p = 0.000
Self-efficacy0.2570.02112.0840.000 **0.215~0.299
Place of origin−5.5860.400−13.9720.000 **−6.370~−4.803
Grade−0.0460.137−0.3320.740−0.315~0.224
Subjects−0.2660.189−1.4040.160−0.636~0.105
Parental monthly income−0.7740.128−6.0540.000 **−1.024~−0.523
Father’s education−0.7320.181−4.0470.000 **−1.087~−0.378
Mother’s education−0.5060.184−2.7470.006 **−0.867~−0.145
** p < 0.01; D-W: 1.015.
Table
Table 8. Results of gender ANOVA.
Table 8. Results of gender ANOVA.
Gender (Mean ± SEM)Fp
Male (n = 133)Female (n = 206)
Self-efficacy35.49 ± 0.30120.82 ± 0.5064.3670.000 **
Learning abidance11.40 ± 0.77510.97 ± 0.7241.2670.606
Learning obedience13.47 ± 0.73513.13 ± 0.5651.0930.715
Learning compliance6.203 ± 0.2415.87 ± 0.1871.0730.000 **
** p < 0.01.
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Xiao, B.; Song, G. Association between Self-Efficacy and Learning Conformity among Chinese University Students: Differences by Gender. Sustainability 2022, 14, 8725. https://doi.org/10.3390/su14148725

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Xiao B, Song G. Association between Self-Efficacy and Learning Conformity among Chinese University Students: Differences by Gender. Sustainability. 2022; 14(14):8725. https://doi.org/10.3390/su14148725

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Xiao, Bin, and Guandong Song. 2022. "Association between Self-Efficacy and Learning Conformity among Chinese University Students: Differences by Gender" Sustainability 14, no. 14: 8725. https://doi.org/10.3390/su14148725

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