As an important part of higher education, higher vocational education plays an indispensable role in promoting the socioeconomic development of a country. However, one significant problem facing higher vocational education is the low learning outcomes among its students (J. Ma, 2014). Due to a relatively weak knowledge foundation and low self-discipline, many higher vocational students have failed to obtain a degree or vocational qualifications within the prescribed period of study or to find jobs that require higher comprehensive qualities (Huang & Zheng, 2019). Therefore, improving the learning effectiveness of higher vocational students and ensuring the education quality of higher vocational personnel are urgent priorities for global higher education institutions.

 

There is consensus among researchers that education quality is best measured utilizing the perspective of students’ learning (Jiang et al., 2023). Kuh (2001) proposed the concept of learning engagement, which mainly refers to the time and energy that students invest in effective learning activities. As a student-centered educational idea that considers students’ learning experience as well as their growth and development, learning engagement has become an important indicator in the evaluation of education quality. Therefore, this study adopted learning engagement as its theoretical basis to explore the quality of higher vocational education in China.

 

Career-planning clarity is based on the process through which individuals establish career-development goals with regard to internal and external factors, formulate plans, and take action accordingly (Barker, 1998). A typical feature of individuals with clear career plans is being action-oriented, which means individuals driven by their career goals will actively participate in activities consistent with their career planning. Higher vocational education is closely linked with socioeconomic development, which highlights the improvement of its graduates’ professional capabilities, employment skills, and entrepreneurship, all of which are dependent on students’ knowledge and ability regarding career planning (C. Ma et al., 2020; Weng, 2010). Therefore, an investigation of learning engagement from the perspective of career-planning clarity addresses the inherent characteristics of higher vocational education and is of great theoretical and practical significance.

 

The process through which career-planning clarity directly and indirectly affects learning engagement has been found to be moderated by several variables, including learning motivation and the campus environment. Modern educational psychologists have postulated that learning motivation involves a combination of an individual’s psychological drives based on their evaluation of learning behaviors, including nonintellectual factors such as their learning interest and learning attitude (Chen & Zhang, 2008). Learning motivation regulates learning activities through initiation, orientation, guidance, maintenance, regulation, reinforcement, and sublimation (Liu et al., 2013). According to social cognitive career theory, individuals with clearer career planning show more positive expectations regarding their career goals and higher career self-efficacy, which often strengthens their learning motivation (Lee et al., 2015). Meanwhile, career construction theory stipulates that an individual’s career development is determined not only by internal factors but also by external factors related to their surroundings (Savickas, 2005), among which the campus environment is crucial (Han & Rojewski, 2015). The campus environment refers to the external environment and conditions through which schools and departments allocate relevant resources to provide support and assistance for individual students’ study on campus (Sun & Liu, 2018). The student comprehensive influence model also regards the support of the campus environment as an important factor affecting the development of students, which will directly or indirectly play a role in their growth and development (Pascarella, 1985).

 

Despite the interrelated importance of these factors, few studies have examined them holistically, and there have been even fewer relevant investigations of vocational college students. While previous studies have found associations between the campus environment and career-planning clarity (Ismail et al., 2023), learning engagement (Kuh, 2009a; Wang & Wang, 2017), and learning motivation (Yu, 2019), the roles that learning motivation and the campus environment play in the relationship between career-planning clarity and learning engagement remain unclear. To address this research gap, this study examined the impact of career-planning clarity, learning motivation, and campus environment on learning engagement to determine effective ways to improve the learning participation of higher vocational students and the quality of higher vocational education.

Clear career planning that reflects individual self-discipline, initiative, and focus is a positive personal attribute (Strauss et al., 2012). The clearer the career plan, the stronger is the awareness that an individual has developed about their future career, which then motivates their active participation in learning. Through case interviews and surveys, Li and Gao (2017) found that the career-planning clarity and adaptability of higher vocational students at the start of their college years have a significant impact on their academic and future development in society. In addition, Kenny et al. (2006) found that students’ career-planning clarity is related to their participation in learning, which means that those who develop clearer career plans and higher expectation of themselves put more effort into studying. Therefore, we proposed the following hypothesis:

Hypothesis 1: There will be a significant positive relationship between the career-planning clarity and learning engagement of higher vocational students.

There is consensus in the literature that greater learning motivation will provide consistent guidance through a student’s learning process. For example, Kim and Doo (2022) surveyed 206 junior college students in South Korea and found that learning motivation and career decision making had a positive impact on their learning engagement. In their investigation of 1,011 community college students in Taipei, China, Kuo et al. (2021) found a significant and positive correlation between students’ learning motivation and learning performance. In addition, several researchers have found positive correlations between students’ learning motivation and engagement (Bates & Khasawneh, 2007; Wolters, 1999, 2003). Meanwhile, clarity in individual career planning may enhance learning motivation by providing a clear sense of purpose and direction. Andre et al. (2019) argued that career planning can enhance individual learning motivation and facilitate individual learning and development, since exploring career interests and academic plans can not only expand job options but also further shape self-understanding. Yang (2015) surveyed 224 higher vocational college students in China and found a significant and positive correlation between their career planning and learning motivation. In summary, it can be inferred that career-planning clarity will facilitate learning motivation, which, in turn, will enhance learning engagement. Therefore, we proposed the following hypothesis:

Hypothesis 2: Higher vocational students’ learning motivation will moderate the relationship between their career-planning clarity and learning engagement.

Researchers have shown that the positive influence of the campus environment can amplify the relationship between career-planning clarity and learning motivation by providing students with the necessary support, guidance, and encouragement to actively engage in the career planning process and pursue their professional ambitions with greater enthusiasm. For example, Ismail et al. (2023) and O’Keefe et al. (2011) both found that students’ awareness of career planning is, to some extent, influenced by their learning environments, indicating that a more supportive learning environment facilitates a stronger awareness of career planning. The availability of support systems, career-counseling services, and networking opportunities may enhance students’ awareness of career planning and their confidence in pursuing their chosen career paths. Therefore, we proposed the following hypothesis:

Hypothesis 3a: The campus environment will play a moderating role in the positive relationship between the career-planning clarity and learning motivation of higher vocational students, such that a more (vs. less) supportive campus environment will strengthen the positive effect of students’ career-planning clarity on learning motivation.

 

Researchers have also found that a supportive campus environment strengthens the positive effects of career-planning clarity on learning engagement by creating an atmosphere conducive to academic success and personal growth. For example, Kuh (2009b) showed that students’ learning engagement is related to various aspects of campus life, while Bridges et al. (2005) found that a supportive campus atmosphere encourages students to actively participate in various educational activities. Davis and Murrell (1993) conducted a mass survey indicating that the supportive and convenient environment provided by colleges is an important factor in promoting students’ development. In a survey of 8,951 students from 10 higher vocational colleges in China, Wang and Wang (2017) found that the support of colleges significantly and positively influences students’ learning engagement, indicating that an unsupportive environment may hinder students’ ability to fully engage with their learning and pursue their career aspirations. Therefore, we proposed the following hypothesis:

Hypothesis 3b: The campus environment will play a moderating role in the positive relationship between the career-planning clarity and learning engagement of higher vocational students, such that a more (vs. less) supportive campus environment will strengthen the positive effect of career-planning clarity on learning engagement.

 

A supportive campus environment also strengthens the relationship between learning motivation and learning engagement by creating an ecosystem that empowers students to pursue their goals. Yu (2019) surveyed 174 higher vocational college students in China and found a positive correlation between campus support and students’ learning initiative. Tinto (1975) found that whether students from different backgrounds have successfully integrated into the campus environment affects their decision to stay or leave; that is, the lower the integration students achieve, the more likely they are to drop out. Lundberg et al. (2018) surveyed 10,071 community college students in the United States and found that the relationship between teachers and students in the school environment can affect students’ learning motivation. These studies show that the campus environment plays a pivotal role in shaping students’ overall learning experiences, attitudes, and behaviors. In an environment characterized by support and encouragement, students tend to embrace a culture of academic excellence and collaborative learning, motivating them to actively participate in their educational endeavors. Therefore, we proposed the following hypothesis:

Hypothesis 3c: The campus environment will play a moderating role in the relationship between the learning motivation and learning engagement of higher vocational students, such that a more (vs. less) supportive campus environment will strengthen the positive effect of students’ learning motivation on learning engagement.

 

Figure 1 shows our proposed moderated mediation model.

 

We conducted a survey of 1,316 students attending higher vocational colleges in Guangdong Province, China, in March 2023, utilizing convenience sampling and the Questionnaire Star platform. Guangdong Province is at the forefront of China’s reform and opening up and has had the largest scale of higher vocational education for many years, which has played an important role in the socioeconomic development of the area. We distributed links to the survey through the WeChat and QQ groups of these higher vocational colleges over a 2-week period. Participants joined on a voluntary basis and were assured that their responses would be kept confidential. The ethics committee at Jinan University approved the study. We instructed participants to select the response that best reflected their thoughts, feelings, or behaviors. After we collected the completed forms, we excluded 100 due to invalid data such as missing or abnormal values, ultimately obtaining 1,216 valid samples for analysis (response rate = 92.40%). Among the respondents, there were 217 men (17.85%) and 999 women (82.15%). Participants were aged between 17 and 21 years (M = 18.82, SD = 0.78).

We measured career-planning clarity using a 20-item career planning scale designed by Dai (2006), which is divided into five subdimensions: self-cognition, career cognition, career orientation, planning formulation, and emotional attitude. Sample items are “You are clear about the career you most enjoy pursuing” and “You are very confident about the development of your career.” Items are rated on a 4-point Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree), with higher scores indicating clearer career planning. Cronbach’s alpha of this scale was .96, indicating sufficient internal consistency. Confirmatory factor analysis showed that no factor loading was less than .40, and the model fit the data well, χ²/df = 8.93, root-mean-square error of approximation (RMSEA) = .081, comparative fit index (CFI) = .94, incremental fit index (IFI) = .94, goodness-of-fit index (GFI) = .88, parsimonious goodness-of-fit index (PGFI) = .66, normalized fixed parsimonious index (PNFI) = .77.

 

We measured learning engagement using a 17-item scale developed by Shi et al. (2007), which has three dimensions: active cooperative learning (e.g., “You take the initiative to ask questions or participate in discussions in class”), student–faculty interaction (e.g., “You discuss your career plans and ideas with your teachers”), and enriching educational experience (e.g., “You have participated in or plan to participate in social practice or investigation before graduation”). Items 1 to 9 are rated on a 4-point Likert scale (1 = never, 4 = very often), while Items 10 to 17 are rated on a 4-point Likert scale with different anchors (1 = not yet decided, 4 = already done). Cronbach’s alpha for this study was .92, indicating high internal consistency. Confirmatory factor analysis showed that no factor loading was less than .40, and the model fit the data well, χ²/df = 8.54, RMSEA = .079, CFI = .94, IFI = .94, GFI = .90, PGFI = .67, PNFI = .77.

 

To measure learning motivation, we used a four-item scale developed by Shi et al. (2007). Sample items are “You feel happy when you concentrate on your study” and “When encountering difficulties in your studies, you always try your best to find ways to overcome them.” Items are rated on a 4-point Likert scale (1 = strongly disagree, 4 = strongly agree), with higher scores indicating stronger learning motivation. Cronbach’s alpha of this study was .87. Confirmatory factor analysis showed that no factor loading was less than .40, and the model fit the data well, χ²/df = 7.37, RMSEA = .072, CFI = .99, IFI = .99, GFI = .99, PGFI = .33, PNFI = .33.

 

We measured campus environment using an 11-item scale developed by Shi et al. (2007), which encompasses three dimensions assessed using distinct rating scales. Items 1 to 4 are related to interpersonal environment and are rated on a 7-point Likert scale (1 = completely disagree, 7 = completely agree). A sample item is “Overall, you have a good relationship with your teachers.” Items 5 to 9 are related to cultural environment and are rated on a 4-point Likert scale (1 = strongly disagree, 4 = strongly agree). A sample item is “Students are encouraged to participate in various campus cultural activities in school.” Items 10 and 11 are related to the physical environment and are rated on a 6-point Likert scale (1 = strongly disagree, 6 = strongly agree). A sample item is “You are satisfied with the learning hardware (classrooms, libraries, laboratories, internet, etc.).” Cronbach’s alpha values were .92 for Items 1 to 4, .93 for Items 5 to 9, and .94 for Items 10 to 11, indicating good internal consistency. Confirmatory factor analysis showed that no factor loading was less than .40, and the fitness indicators showed that the model fit the data well, χ²/df = 3.87, RMSEA = .049, CFI = .99, IFI = .99, GFI = .98, PGFI = .58, PNFI = .70.

We used SPSS 27.0 for data processing. First, we conducted a Pearson correlation analysis to judge the correlations between variables. Then, we used Model 4 of the PROCESS macro for SPSS to estimate the simple slope of the adjustment effect.

 

 

 

We used Harman’s single-factor test to test for common method bias. The results show that the eigenvalues of eight factors were greater than 1 without rotation. The first factor accounted for 37.70% of the variance, which is lower than the critical standard of 40%, indicating that the data were not significantly affected by common method bias.

Table 1 contains the descriptive statistics and correlations of the research variables. The pairwise correlations among career-planning clarity, learning engagement, learning motivation, and campus environment were all significant and positive (p < .01). Meanwhile, the product-moment correlation results show that gender had a significant correlation with the research variables; hence, we included gender in the model as a control variable in the subsequent analysis of mediating and moderating effects.

We used Model 4 of the PROCESS macro to test the mediating effect of learning motivation in the relationship between the career-planning clarity and learning engagement of higher vocational students. As shown in Table 2, the results for Model 1 reveal that career clarity significantly and positively predicted learning engagement, β = .625, p < .001; 95% CI [0.580, 0.670]. The results for Models 2 and 3 reveal that career-planning clarity significantly predicted learning motivation, β = .554, p < .001, 95% CI [0.510, 0.599], which then translated into learning engagement, β = .443, p < .001, 95% CI [0.392, 0.494]. Results of bias-corrected percentile bootstrapping indicate that the confidence interval of neither the direct effect of career-planning clarity on learning engagement nor the mediating effect of learning motivation contained 0, which implies a partial mediation effect of learning motivation on career-planning clarity and learning engagement. The mediating effect was .25 and the indirect effect accounted for 39.20% of the total.

We used Model 59 of the PROCESS macro to test the moderating effect of campus environment in the study. The results of Table 4 show that in Model 1 the interaction between career-planning clarity and campus environment positively predicted learning motivation, β = .087, p < .001, 95% CI [0.047, 0.126]. In Model 2 the interaction between career-planning clarity and campus environment positively predicted learning engagement, β = .069, p < .01, 95% CI [0.022, 0.115], and that between learning motivation and campus environment positively predicted learning engagement, β = .060, p < .05, 95% CI [0.012, 0.109]. These results show that our moderated mediation model fit the empirical data and explained the relationships between career-planning clarity, learning motivation, the campus environment, and learning engagement among higher vocational students.

Table 5 shows the mediating effect values under the adjustment of campus environment at different levels. The confidence intervals did not contain 0, indicating significance. Comparative analysis of mediation effects with adjustment showed the difference values at different levels, and the confidence intervals did not contain 0. These results indicate that the mediating effect of career-planning clarity on students’ learning engagement through learning motivation was significantly enhanced under the moderating effect of campus environment.

To better explain the moderated mediation model, we divided campus environment into high (M + 1 SD) and low (M – 1 SD) levels and conducted a simple slope test to examine its impact on the relationship between career-planning clarity and learning motivation as well as that between learning motivation and learning engagement. The detailed moderation effects are shown in Figures 2, 3, and 4. As can be seen in Figure 2, when students felt a high level of support from the campus environment there was a significant positive predictive effect of career-planning clarity on learning motivation, β = .487, t = 18.649, p < .001, 95% CI [0.436, 0.538]; however, when the campus environment was less supportive the positive predictive effect was reduced, β = .357, t = 11.207, p < .001, 95% CI [0.295, 0.420].

 

As can be seen in Figure 3, when the campus environment was more supportive we found a significant positive predictive effect of learning motivation on learning engagement, β = .440, t = 12.915, p < .001, 95% CI [0.373, 0.507]; however, when the campus environment was less supportive the positive predictive effect was reduced, β = .349, t = 11.225, p < .001, 95% CI [0.288, 0.410].

 

As can be seen in Figure 4, when the campus environment was more supportive we found a significant and positive effect of career-planning clarity on learning engagement, β = .369, t = 11.799, p < .001, 95% CI [0.307, 0.430]; however, when the campus environment was less supportive the positive predictive effect was reduced, β = .266, t = 8.315, p < .001, 95% CI [0.203, 0.329].

 

This study investigated the relationship between career-planning clarity and the learning engagement of higher vocational education students, using the campus environment and learning motivation as mediating and moderating variables, respectively. The results showed that (a) career-planning clarity had a significant positive effect on learning engagement; (b) learning motivation mediated the positive relationship between career-planning clarity and learning engagement; and (c) the campus environment played a moderating role in the relationships between career-planning clarity and learning motivation, career-planning clarity and learning engagement, and learning motivation and learning engagement among higher vocational students.

In line with the findings of Kenny et al. (2006), we found that the career-planning clarity of higher vocational students directly and positively predicted their learning engagement, supporting Hypothesis 1. This significant and direct effect indicates that the higher the degree of career-planning clarity of higher vocational students, the more likely they are to be proactive in the learning process.

We also found that higher vocational students’ career-planning clarity indirectly predicted their learning engagement through the mediator of learning motivation, supporting Hypothesis 2. These findings corroborate previous research (Andre et al., 2019; Yang, 2015) and align with social cognitive career theory, which emphasizes that clearer career planning may enhance learning motivation. Our findings also extend the current literature by highlighting the significant role of learning motivation as a partial mediator in the relationship between career-planning clarity and learning engagement.

Last, we found that when the campus environment was supportive, higher vocational students developed clearer career plans and established higher learning goals and life pursuits, thus constantly strengthening their learning engagement, which is in line with previous research findings and supported Hypotheses 3a, 3b, and 3c. The more supportive the campus environment is for students’ learning, the more academic gains students can make (Bai & Zhou, 2018). This finding is also consistent with the theoretical viewpoint of the student comprehensive influence model, in which the campus environment is proposed to play a foundational role in students’ development (Pascarella, 1985).

This study has important practical implications for educators and administrators in higher vocational education institutions. We recommend that educators utilize the significant and positive effect of career-planning clarity on learning engagement to help students develop clearer career goals. In addition, the identification of learning motivation as an intermediary factor indicates the importance of fostering motivation among students through career counseling and academic support services. We recommend that vocational colleges ensure necessary investment in the campus’s physical environment, teaching facilities, and support provided to students in areas such as skills training, employment guidance, and financial assistance. Last, we recommend that vocational colleges aim to enhance communication and cooperation among teachers, students, and peers to build a harmonious and friendly interpersonal campus environment.

There are some limitations to this study. First, our research sample was sourced only from Guangdong Province in China. Future researchers could expand the scope of study to other countries and cultural settings. Second, we used convenience sampling and surveyed only vocational college students, which limits the generalizability of our results. Random sampling could be implemented and the population surveyed could be expanded in future research. Last, this study collected data over a single academic year, making it difficult to establish the causality and temporal precedence among the variables. More implications could be obtained in the future through using longitudinal data.

Andre, L., Peetsma, T. T. D., van Vianen, A. E. M., Jansen in de Wal, J., Petrović, D. S., & Bunjevac, T. (2019). Motivated by future and challenges: A cross-cultural study on adolescents’ investment in learning and career planning. Journal of Vocational Behavior, 110, 168–185.

 

Bai, H., & Zhou, Z. Y. (2018). How college education affects undergraduates’ learning harvest: Based on CCSEQ empirical survey data [In Chinese]. Journal of Education, 14(3), 81–88.

 

Barker, J. (1998). Career planning: A developmental approach. Birth Defects Research Part B: Developmental & Reproductive Toxicology, 71(4), 193–280.

 

Bates, R., & Khasawneh, S. (2007). Self-efficacy and college students’ perceptions and use of online learning systems. Computers in Human Behavior, 23(1), 175–191.

 

Bridges, B., Cambridge, B., Kuh, G. D., & Leegwater, L. H. (2005). Student engagement at minority serving institutions: Emerging lessons from the BEAMS project. New Directions for Institutional Research, 125, 25–43.

 

Chen, S. X., & Zhang, X. H. (2008). Discussion on strengthening the effectiveness of ideological and political education from the current situation of college students’ learning motivation [In Chinese]. Journal of North University of China (Social Science Edition), S1, 8–11.

 

Davis, T. M., & Murrell, P. H. (1993). A structural model of perceived academic, personal, and vocational gains related to college student responsibility. Research in Higher Education, 34(3), 267–289.

 

Han, H., & Rojewski, J. W. (2015). Gender-specific models of work-bound Korean adolescents’ social supports and career adaptability on subsequent job satisfaction. Journal of Career Development, 42(2), 149–164.

 

Huang, H. Y., & Zheng, Y. J. (2019). How can American community colleges maintain a steady increase in the number of students? [In Chinese]. Chinese Vocational and Technical Education, 30, 35–41 + 50.

 

Ismail, I., Idris, M., & Asri, A. (2023). The impact of placement, career development and motivation on employee performance in the environmental service life and forestry industry of Polewali Mandar Regency [In Indonesian]. Cash Flow Jurnal Manajemen, 2(1), 134–144.

 

Jiang, L. Y., Li, Q. Q., & Xin, T. (2023). Focusing on the high-quality development of education: Status quo and prospect of regional education quality evaluation [In Chinese]. Journal of the Chinese Society of Education, 2, 12–16.

 

Kenny, M. E., Blustein, D. L., Haase, R. F., Jackson, J., & Perry, J. C. (2006). Setting the stage: Career development and the student engagement process. Journal of Counseling Psychology, 53(2), 272–279.

 

Kim, J., & Doo, M. Y. (2022). The effects of motivation, career decision-making self-efficacy, and self-regulation on learning engagement of junior college students. Journal of College Student Development, 63(4), 432–448.

 

Kuh, G. D. (2001). The National Survey of Student Engagement: Conceptual framework and overview of psychometric properties. Indiana University Center for Postsecondary Research & Planning.

 

Kuh, G. D. (2009a). The National Survey of Student Engagement: Conceptual and empirical foundations. New Directions for Institutional Research, 2009(141), 5–20.

 

Kuh, G. D. (2009b). What student affairs professionals need to know about student engagement. Journal of College Student Development, 50(6), 683–706.

 

Kuo, Y.-K., Wang, J.-H., Kuo, T.-H., & Ho, L.-A. (2021). Leisure satisfaction influences learning performance among community college students. SAGE Open, 11(4), Article 21582440211067238.

 

Lee, H.-S., Flores, L. Y., Navarro, R. L., & Kanagui-Muñoz, M. (2015). A longitudinal test of social cognitive career theory’s academic persistence model among Latino/a and White men and women engineering students. Journal of Vocational Behavior, 88, 95–103.

 

Li, Y., & Gao, C. D. (2017). Research on countermeasures of college freshmen education based on career development theory [In Chinese]. Higher Education Press Research.

 

Liu, Y., Gao, Y., Sun, D. M., Chen, J. S., Tian, Y. J., & Hu, X. Y. (2013). Study on the influencing factors and mechanism of college students’ learning motivation [In Chinese]. Studies in Ideological Education, 224(7), 69–72 + 111.

 

Lundberg, C. A., Kim, Y. K., Andrade, L. M., & Bahner, D. T. (2018). High expectations, strong support: Faculty behaviors predicting Latina/o community college student learning. Journal of College Student Development, 59(1), 55–70.

 

Ma, C., Zhou W. B., & Zhao, S. F. (2020). The impact of family support on employee innovation—The moderating role of career-planning clarity and mediating role of work engagement [In Chinese]. Soft Science, 34(1), 103–109.

 

Ma, J. (2014). A study on the model of higher education quality evaluation in the United States: An analysis based on the Community College Student Engagement Survey [In Chinese]. Foreign Education Research, 41(2), 103–112.

 

O’Keefe, M., Laven, G., & Burgess, T. (2011). Student non‐completion of an undergraduate degree: Wrong program selection or part of a career plan? Higher Education Research and Development, 30(2), 165–177.

 

Pascarella, E. T. (1985). College environmental influences on learning and cognitive development: A critical review and synthesis. Higher Education: Handbook of Theory and Research, 1(1), 1–61.

 

Savickas, M. L. (2005). The theory and practice of career construction. In R. W. Lent & S. D. Brown (Eds.), Career development and counseling: Putting theory and research to work (pp. 42–70). John Wiley & Sons.

 

Strauss, K., Griffin, M. A., & Parker, S. K. (2012). Future work selves: How salient hoped-for identities motivate proactive career behaviors. Journal of Applied Psychology, 97(3), 580–598.

 

Sun, D. M., & Liu, Y. Y. (2018). Research on the influence mechanism of education quality in research-oriented universities: Based on the analysis of CCSS survey data of L University [In Chinese]. Fudan Education Forum, 16(6), 56–64.

 

Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125.

 

Wang, Y. S., & Wang X. (2017). An empirical study on students’ learning engagement and its influencing factors in higher vocational colleges [In Chinese]. Educational Research, 38(1), 77–84.

 

Weng, Q. X. (2010). The effect of career self-management on career decision-making effectiveness [In Chinese]. Management Review, 1, 82–93.

https://doi.org/10.14120/j.cnki.cn11-5057/f.2010.01.001.

 

Wolters, C. A. (1999). The relation between high school students’ motivational regulation and their use of learning strategies, effort, and classroom performance. Learning and Individual Differences, 11(3), 281–299.

 

Wolters, C. A. (2003). Regulation of motivation: Evaluating an under-emphasized aspect of self-regulated learning. Educational Psychologist, 38(4), 189–205.

 

Yang, X. Z. (2015). The intervention of vocational college students’ career planning and its impact on learning motivation (Master’s thesis) [In Chinese]. Northwest Normal University.

 

Yu, Z. X. (2019). Research on the relationship between teacher support and students’ learning engagement in higher vocational colleges: An analysis of the mediating effect based on learning self-efficacy [In Chinese]. Vocational and Technical Education, 40(17), 65–70.