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Article

Influencing Factors of Students’ Learning Gains in Tourism Education: An Empirical Study on 28 Tourism Colleges in China

by
Lina Zhong
1,
Xiaonan Li
2,
Sunny Sun
3,*,
Rob Law
2,
Xiangchi Qi
1 and
Yingchao Dong
1,*
1
Institute for Big Data Research in Tourism, School of Tourism Sciences, Beijing International Studies University, Chaoyang District, Beijing 100024, China
2
Asia-Pacific Academy of Economics and Management, Department of Integrated Resort and Tourism Management, Faculty of Business Administration, University of Macau, Avenida da Universidade, Taipa, Macau 999078, China
3
College of Asia Pacific Studies, Ritsumeikan Asia Pacific University, 1-1 Jumonjibaru, Beppu 874-8577, Japan
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(24), 16601; https://doi.org/10.3390/su142416601
Submission received: 6 November 2022 / Revised: 4 December 2022 / Accepted: 7 December 2022 / Published: 12 December 2022
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Abstract

:
The rapid development of tourism has put forward new requirements for the training of tourism talents. This study conducted a cross-regional questionnaire survey on tourism management undergraduate students from 28 tourism colleges in seven regions of China. PLS method was used to explore the relevant influencing factors of students’ learning gain. The study concluded that: (1) Students’ gain in professional knowledge reserve, learning ability, innovation ability, teamwork ability and social ethics have a significantly positive impact on students’ employment situation. (2) Internship and employment guidance plays an important role in the influence of college investment on students’ learning gain. (3) Student engagement plays an important mediating role in the relationship between teaching factors (such as learning curriculum and teachers’ teaching quality) and students’ learning gain. (4) The college’s learning guidance and advice collection can effectively improve students’ learning gain, but the impact of the college’s environmental facilities on the sense of acquisition of tourism management students is not significant. This paper provides important implications for the improvement of talent mechanism of tourism education.

1. Introduction

Tourism and hospitality companies can only be as successful as the efficiency of their human resources [1]. Employees, especially frontline staff, are tasked with directly communicating with tourists and consumers [2], carrying corporate image [3], and are wellsprings of technological change and organizational progress [4]. Thus, employee quality ultimately decides organizational efficiency. The level of employee professional skills depends on the quality of training provided by the company [5] and the professional knowledge and skills provided during the employee’s college education [6]. Within the tourism and hospitality industry, the rapid development (and its subsequent screeching halt brought about by the COVID-19 pandemic) forwarded new requirements for the training of new talents [7] such as the degree of high-tech skills [8]. Tourism education and training play an important role in providing qualified graduates [9]. However, higher education for tourism majors still face great challenges, and the general problem of low employment rate still exists [10]. Additionally, the low rate of job-matching of tourism management majors is a serious problem in the field of tourism education [11], therein being extremely detrimental to the high-quality development of the tourism industry as it reduces the creation of high-quality employees [12].
Students’ learning gain is an important part of education quality research: it is an important factor used to evaluate students’ learning effect. Recently, studies on students’ learning gain have become increasing in various disciplines, with an increasing number of studies looking into the influencing factors of learning gain. Many factors affect learning gain such as evaluation feedback [13] and teaching techniques [14]. However, there remains to be a definite study on the learning gains of Chinese tourism students. Figuring out these learning gains would improve the training mechanism of Chinese tourism professionals to carry out in-depth investigation and research on students’ learning gain.
Using data from 28 colleges in seven Chinese regions as samples and following the perspective of students, this study focuses on employment guidance and how student participation plays an intermediary role in influencing mechanism, explores the influence factors of tourism colleges undergraduate students’ feeling. Furthermore, improvement strategies are forwarded for benchmarking of the promotion of mechanisms for tourism education personnel training. The following research questions are thus explored: (1) What are the influencing factors of undergraduate students’ learning gain in tourism colleges in China?; (2) Is there a significant relationship between students’ learning gain and their employment?; (3) Under the characteristics of tourism management, which is more practical, what role do student participation and internship and employment guidance play in improving students’ learning gain?

2. Literature Review and Research Hypothesis

2.1. Student Employment Research

Recently, quality of students’ employment is a key topic of higher education teaching research. University is the training base of high quality talents, with education quality being directly related to the quality of students’ employment [15]. To test whether students acquiring the necessary skills and abilities through learning in college directly affects their employment situation, Luo and Zhao revealed that the introduction of big data analysis in school classroom education coupled by the reform of curriculum management significantly improves students’ knowledge level and professional skills [16]. This can make students better follow the needs of the employment market, thus improving their overall employability. Students’ knowledge enrichment, human capital accumulation, social capital accumulation and other learning acquisition while in undergraduate positively affect their employment quality. Additionally, information construction, teaching reform, and employment guidance of colleges and universities also promote high quality employment for students [17]. Hence, it is expected that students’ theoretical knowledge and practical skills acquired in school will impact their employment quality. Hence, the following hypothesis is proposed:
Hypothesis 1 (H1).
Students’ learning gain has a positive effect on students’ employment situation.
Moreover, the employment guidance provided by schools for students, such as internship guidance courses and practice opportunities, affect the employment situation of students. For example, Sibgatova et al. pointed out that the student employment guidance courses of the “school- enterprise” integrated system constructed by schools help improve the employment quality of students [18]. In the Internet era, the setting of career planning courses in colleges helps improve students’ self-knowledge, tap their potential, constantly improve their ability to adapt to society, and promote high-quality employment [19]. Individualized vocational counseling promote students to find ideal jobs and improve the overall employment quality of students [20]. Yu et al. constructed an evaluation system of college students’ employment guidance service and verified the promotion effect of college students’ employment guidance courses and services on student employment quality [21].
However, some prior studies have examined whether college support can affect students’ learning gains through internship and employment guidance, and then improve students’ employment situation. For example, colleges usually understand students’ employment needs and improve internship and employment guidance to promote students’ learning gains and improve their employment situation. Hence, the current study aims to provide enlightenments to the relevant work of colleges. As such, the following research hypotheses are proposed.
Hypothesis 2a (H2a).
Internship and employment guidance has a positive impact on students’ employment situation.
Hypothesis 2b (H2b).
Internship and employment guidance has a positive impact on students’ learning gain.
Hypothesis 3 (H3).
College support has a significant impact on internship and employment guidance.
Hypothesis 4a (H4a).
College support influences students’ learning gain through the intermediary role of internship and employment guidance.
Hypothesis 4b (H4b).
Internship employment guidance influences students’ employment situation through the intermediary effect of students’ learning gain.

2.2. Research on Students’ Learning Gain

Currently, there are various definitions of learning gains in the academic circle. Learning ability, employability and learning gains are usually defined as synonyms or similar words [22]. Academics in the UK focus on student learning gains and defined it as the difference in student performance between two stages of learning as a measure of a school’s efforts to promote student learning [23]. Various scholars have also optimized the definition of learning gain to different degrees. McGrath et al. defined learning gain as a kind of “distance”, emphasizing the comprehensive difference among students’ skills, abilities, content knowledge, and personal development at two time points [23]. Garzón et al. applied learning harvest in AR teaching context and defined it as”the improvement of students’ learning outcomes before and after the application of AR” [14]. In sum, students’ learning gains are the result of comprehensive improvement of theory, emotion, value, ability and behavior. Hence, the current study defines students’ learning gain as the positive experience and evaluation generated by students considering their benefits from education, which is ultimately the result of synchronized various factors.
The measurement of students’ learning harvest is another focus in the field of education [24]. Some influencing factors involve teaching contents and methods, teaching environment and facilities, and students’ participation in interaction [22]. Extant literature on students’ learning gain and the influencing factors of students’ learning gain mainly include two aspects: the college factor and the student’s individual learning factor. The combination of students’ individual learning and external environment impacts students’ output. For example, Davis and Murrell emphasized the influence of student’s own academic knowledge and practical experience on students’ learning gain [25], while Mahan mainly focused on the relationship between campus relationship, school environment, and learning gain [26]. Thus, the current study chose college investment factor and individual learning factor for subsequent research and analysis.

2.2.1. College Investment Factor

Educational quality is mainly based on the college environment and various educational resources. For college students, environmental facilities, college support, and other factors may affect students’ learning gain, with the number of teachers and the size of the school significantly affecting students’ reading ability [27]. School facilities can further influence students’ learning outcomes and promote further education [28,29]. Campus environment facilities such as libraries, school textbooks, and the availability of laboratory facilities also have a significantly positive impact on students’ academic achievement [30]. Moreover, technology facilities have become pivotal factors. For instance, Achor and Ityobee conducted a mixed method survey of both teachers and students, showing that the completeness of communication technology facilities in schools significantly affect teachers’ teaching quality and students’ learning outcomes [31]. Hence, the intensity of college support and college environmental facilities may, expectedly, affect students’ sense of acquisition. Therefore, the following research hypotheses are proposed.
Hypothesis 5 (H5).
College support has a positive impact on students’ learning gain.
Hypothesis 6 (H6).
College environmental facilities have a positive impact on students’ learning gain.

2.2.2. Students’ Individual Learning Factor

The theory of “student engagement” holds that the education quality of a university mainly depends on whether or not it promotes students to engage into various activities better [32]. Various studies have outlined the important role of student engagement in students’ learning gain. This effective combination of extra-curricular teaching activities and in-class teaching knowledge significantly stimulate students’ willingness to participate in learning and improve their learning harvest [33]. By introducing games into the classroom, students’ engagement in the classroom is greatly improved, therein greatly promoting students’ absorption and understanding of the knowledge they have learned [34]. For instance, Manwaring et al. divided student engagement into two dimensions: emotional engagement and cognitive engagement. Results show that both of these two dimensions have positive impacts on students’ learning effect [35]. Hence, it is expected that student participation plays an important role in improving students’ leaning gains.
Teaching quality is the foundation of higher education; indicators such as teachers’ teaching quality and students’ curriculum design play important roles in stimulating students’ individual participation and improving students’ learning gain. The use of a single textbook and rigid teaching methods in the classroom greatly reduces students’ understanding of the textbook, subsequently reducing students’ learning gains [36]. Behle and Maher constructed the “Teaching Excellence” index system using British higher education as the research object. They found that teaching quality indicators such as curriculum design, course evaluation, and feedback are important in the overall index structure, significantly impacting students’ learning harvest [37]. In German higher education, studies on the improvement of teaching quality significantly promotes students’ learning gains [38]. Teachers’ grade, teaching experience, and teaching skills reflect teachers’ teaching quality [39]. More experienced teachers are good at evaluating students’ learning through more comprehensive indicators other than academic performance, thus promoting students’ learning gains [40]. Hence, it is expected that both teachers’ teaching quality and school curriculum design will affect students’ learning gain through student engagement. Thus, the present study paper will analyze the teaching factors into the individual students’ learning system. As such, the following research hypotheses are proposed. Figure 1 shows the proposed model on student’s learning gain.
Hypothesis 7 (H7).
Student engagement has a positive impact on students’ learning gain.
Hypothesis 8a (H8a).
Curriculum design has a positive impact on students’ learning gain.
Hypothesis 8b (H8b).
Curriculum design has a positive impact on student engagement.
Hypothesis 9a (H9a).
Teachers’ teaching quality has a positive impact on students’ learning gain.
Hypothesis 9b (H9b).
Teachers’ teaching quality has a positive impact on student engagement.
Hypothesis 10a (H10a)
Curriculum design affects students’ learning gain through the mediating effect of student engagement.
Hypothesis 10b (H10b).
Teachers’ teaching quality influences students’ learning gain through the mediating effect of student engagement.

3. Methodology

3.1. Questionnaire Design

According to the relevant research on students’ learning gains and education quality, this study referred to the research results of Yi [41], Gao [42], and Liu [43], and combined with expert consultation to design a questionnaire survey. The questionnaire survey consists of two parts: the first part investigates the factors relating to students’ learning gain. Latent variables were adopted to measure the following aspects: “students’ learning gain”, “students’ employment situation”, “students’ engagement”, “internship and employment guidance”, “college environmental facilities”, “college support”, “curriculum design”, and “teachers’ teaching quality”. A 5-point Likert scale was used to measure students’ perception of each influencing factor, with numbers 1 to 5 representing strongly disagree, disagree, neutral, agree and strongly agree, respectively. The second part contains the demographic characteristics of respondents (e.g., gender).

3.2. Data Collection

The survey was distributed to Chinese undergraduate students majoring in tourism management. To make the sample more representative and universal, the present study divided the country into seven regions: east, south, north, central, southwest, northwest and northeast China. Four colleges with offering degrees in tourism management were randomly selected in each region, thus selecting 28 colleges in total. Questionnaires were distributed to undergraduate students of selected 28 colleges from 4 November to 24 December 2019. In total, 867 valid questionnaires were collected, while 27 invalid ones were removed, ultimately garnering 840 valid questionnaires reflecting an effectiveness rate of 96.89%. In the sample, 653 students were female (accounting for 77.7% of the respondents) and 187 students were male (accounting for 22.3% of the respondents), therein coinciding with the reality of the gender ratio of tourism management students.

3.3. Research Methods

Structural equation modeling (SEM) was adopted to explore the internal mechanism, that is, influencing factors of students’ learning gain in tourism education, because SEM establishes, estimates, and tests causality models which contain both observable explicit variables and potentially unobstructed latent variables [44]. SEM has also been used in prior studies to clearly analyze the effects of single indicators on the overall population and the relationships among single indicators [45]. Partial least square (PLS), Smart PLS 3.0 and SPSS 25.0 were used for data analysis. PLS has no strict requirements on the normal distribution of data, and it is also applicable in the case of small samples [46]. It is mostly used in prediction and theoretical exploratory research; and has been widely used in the field of tourism and hotel management [47]. SPSS 25.0 software was first adopted to calculate Cronbach’s α coefficient and basic statistics, delete unqualified items, and eliminate invalid questionnaires. Next, Smart PLS 3.0 software was used to test the reliability and validity of samples and the structural model with a variety of judgment indicators.

4. Results

4.1. Reliability and Validity

Standardized load estimate, Cronbach’s α coefficient, Average variance extracted (AVE) and Combined reliability (CR) were mainly used by the reliability and validity tests as the evaluation indicators and modifiers of the latent variables. The revised parameter estimation results are shown in Table 1. Factor loads of each latent variable range from 0.726 to 0.910, all greater than 0.7; CR range from 0.866 to 0.923, all greater than 0.7; AVE range from 0.617 to 0.787, all greater than 0.5; Cronbach’s α coefficient range from 0.774 to 0.895. All of them are greater than 0.7, indicating the reliability symbol standard of each latent variable. The items of the same variable have good convergence validity and the questionnaire has good internal consistency.
The discriminant validity analysis of the scale was mainly detected by comparing the square root AVE value of the latent variable with the correlation coefficient between each latent variable (Table 2). The second column on the left of the table shows the AVE value of each latent variable, the bold value is the square root of AVE, and the other values are the correlation coefficients of each latent variable. Here, the AVE square root value of each potential variable is greater than the correlation coefficient of each potential variable, indicating that each potential variable has good discriminant validity. Moreover, the complete collinearity test was also used to test common method variation and the variance inflation factor (VIF value) was found to be between 1.467 and 2.927, and all VIF values were less than 5, thus indicating that common method variation had no serious impact on data validity.

4.2. Research Hypothesis Testing

PLS and Bootstrapping methods were used herein to test the structural model, Bootstrapping = 5000 times, and significance p < 0.05 was taken as the standard. The path relationship of structural model (Figure 2) and hypothesis test results (Table 3) are as follows:
Table 4 shows the results of mediation effects testing. First, focusing on the factors influencing students’ employment status, both students’ learning gains (β = 0.501, p < 0.001) and internship and employment guidance (β = 0.277, p < 0.001) from college education have significant positive impacts on students’ employment situation. Hence H1 and H2a are supported, indicating that the more students’ learning gains, more internship and employment guidance are provided. Also, more students become appreciated by employers, ultimately making it easier for them to find the jobs that they are satisfied with and allowing them to enjoy a better employment match with their majors. On the school’s internship and employment guidance, on the one hand, the internship and employment guidance (β = 0.214, p < 0.001) have a significantly positive effect on students’ learning gains. Hence, H2b is also supported, meaning that students can improve their professional knowledge reserves and enhance their learning, innovation, synergy skills and social ethics through internships and career guidance activities. On the other hand, internship and employment guidance is also significantly and positively influenced by college support (β = 0.750, p < 0.001). Hence H3 is supported, reflecting that student feedback, curriculum support, and other similar behaviors are important sources of information and guarantees for schools to provide proper internship and employment guidance.
Second, the focus is on the factors influencing students’ learning gains. For institutional factors, college support (β = 0.215, p < 0.001) has a significant positive effect on students’ learning gains, and H5 is supported, reflecting that the more support is given to students by their schools, more students can acquire professional knowledge, learning ability, innovation, teamwork, and social ethics from their university. However, results also show that the environmental facilities of the school (β = 0.051, p > 0.05) do not have a significant positive effect on students’ learning gains. Hence, H6 is not supported. The reason may be that the school’s facility environment is considered the basic guarantee for students’ successful learning, while institutional support behaviors are core factors affecting students’ learning gains (in terms of college factors). For personal factors, including students’ engagement (β = 0.149, p < 0.001), curriculum settings (β = 0.172, p < 0.001), and teachers’ teaching level (β = 0.176, p < 0.001) have significant positive impacts on students’ learning gains. Hence, H7, H8a, and H9a are supported. In other words, the stronger students’ participate in learning, the more students can gain from learning. In addition, the more advanced and reasonable school’s curriculum can be developed, students will be more engaged in learning and can gain more from their university learning. Notably, school curriculum settings (β = 0.326, p < 0.001) and teachers’ teaching level (β = 0.497, p < 0.001) also have significantly positive effects on students’ engagement, thus supporting hypotheses H8b and H9b. This suggests that curriculum and teachers’ level are important factors affecting students’ personal engagement.
Bias-corrected non-parametric Bootstrap method was used to test the mediation effect. The sample extraction of Bootstrap was set to 5000 and was carried out under a 95% confidence interval. The results of the mediating effect test showed that internship and employment guidance and student engagement had important mediating effects in promoting student learning gains and contributed to improved quality of student employment. Specifically, college support indirectly affects students’ learning gain through internship and employment guidance (β = 0.161, p < 0.001). Internship and employment guidance can indirectly affect students’ employment situation through students’ learning gain (β = 0.107, p < 0.001), thus verifying hypotheses H4a and H4b. Results also showed that both curriculum design (β = 0.049, p < 0.01) and teachers’ teaching quality (β = 0.074, p < 0.01) indirectly affect students’ learning gain through student engagement, thus also supporting hypotheses H10a and H10b.

5. Conclusions

5.1. Main Results

Following a questionnaire survey of undergraduate students majoring in tourism management from 28 tourism colleges in China, this study explores the influence mechanism of students’ learning gain in tourism colleges from two aspects of college investment and individual learning of students, and focuses on the intermediary role of internship and employment guidance and student engagement in the mechanism.
The main conclusions are as follows:
(1)
Students’ gain in professional knowledge reserve, learning ability, innovation ability, teamwork ability and social ethics through college study can have a significant positive impact on students’ employment situation.
(2)
Internship and employment guidance plays an important role in the influence of college investment on students’ learning gain. The degree of college support affects students’ learning gain through internship and employment guidance, and the direct influence of internship and employment guidance on students’ employment situation is also very significant.
(3)
Student engagement plays an important mediating role between teaching factors and students’ learning gain. Curriculum design and teachers’ teaching quality affect students’ learning gain through student engagement.
(4)
The college learning guidance and advice collection can effectively improve students’ sense of acquisition, but the impact of college environment facilities such as catering and accommodation on students’ learning gain is not significant.

5.2. Implication

Taking students’ learning gain as the research theme, this study conducted an investigation and research on undergraduates majoring in tourism management from 28 colleges in seven regions of China from two aspects of college investment and individual learning of students. The main theoretical contributions of the present study are indicated as follows:
First, the present study introduces the pedagogical theory of student’s learning gain theory into the study of tourism education, verifies that the two influential dimensions of student learning gain are also applicable to tourism majors, expands the study sample of tourism majors’ learning gain, and pioneers a large-scale cross-regional survey of tourism education research conducted nationwide. Specifically, the present study finds a significant relationship between tourism education students’ learning gains and their employment status, providing favorable support for the importance and necessity of focusing on student learning gains research.
Second, the present study can, to a certain extent, complement and revise the theories related to students’ learning gain, which is the second innovation of the present study. It is found that environmental facilities have no significant effect on the learning gains of undergraduate tourism management students, which is inconsistent with the results of previous studies; and provide a new direction for future research, which helps to explore the most essential factors affecting students’ gains.
Finally, based on the actual characteristics of tourism majors, the present study introduces internship and employment guidance as an important mediating factor in the college investment impact pathway of student learning gains and verifies the important mediating role of internship and employment guidance, providing an innovative perspective for tourism education-related research. The aforementioned findings effectively complement relevant research in the field of education, especially in the field of tourism education. It can be seen that research on students’ learning gains differs in terms of impact mechanisms due to the characteristics of different disciplines and cannot be generalized across disciplines and majors.
The current study provides some important elucidations for the construction of talent cultivation mechanism for undergraduate students in tourism colleges:
(1)
Students’ learning gain is the subjective perception and evaluation of their undergraduate learning effect, and students’ employment is an important indicator of teaching quality. The study verifies that students’ learning gain significantly influences students’ employment situation, illustrating that colleges must not only focus on employment rate. They must also pay much regard in improving their students’ sense of gain and their comprehensive quality, such as their professional knowledge accumulation, learning ability, innovation ability, team cooperation ability, and social morality. Paying attention to the comprehensive development and cultivation of tourism talents effectively promotes and improves the employment situation of students majoring in tourism.
(2)
Tourism is a highly practical discipline with an equally practical industry. Hence, undergraduate training in tourism colleges should focus on the internship and employment guidance according to the normative suggestions of what should be included in tourism education. Tourism colleges should provide more employment recommendations and better internship site conditions for students, allowing them fully understand the requirements of employers through practical courses, and providing professional employment guidance and career planning training.
(3)
Student engagement plays an important role in teaching tourism at the undergraduate level. Results clearly conclude that student engagement plays an important mediating role between teaching factors and students’ learning gain. Therefore, colleges should improve the mobilization of students’ participation in curriculum design and teachers’ teaching quality evaluation, including guiding students to think independently, encouraging class participation, and creating avenues for interaction with teachers, therein fully mobilizing students’ enthusiasm for classroom participation and activating the tourism teaching class.
(4)
Schools should pay attention to students’ learning guidance and collecting feedback on students’ opinions. Listening to students’ concerns is one of the important factors affecting students’ learning gain. Hence students’ learning guidance and timely feedback on their collected work should be fully managed to improve education quality’s enthusiasm and initiative, therein also ensuring the fairness of the work to provide professional guidance for each student and accordingly improve teaching mechanism.

5.3. Limitations and Future Research Directions

The limitations of this study are as follows: firstly, this study conducted a cross-regional study on students from 28 tourism colleges in seven regions across the country. However, it remains undiscussed whether there are significant differences among students from different regions, different colleges, and different genders, which should be examined by future research. Secondly, the research on the influencing factors of students’ learning gain in this paper only focuses on the aspects of college investment and individual learning of students, and the influence of individual learning of students is more prominent than the influence of teaching factors and student participation. Therefore, influencing factors must be expanded into further dimensions, such as students’ learning duration, students’ family background, and the financial investment of colleges.

Author Contributions

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

Funding

This research was funded by Beijing Social Science Foundation Major Project, grant number 21JCA042; and Ethnic research project of the National Committee of the People’s Republic of China, grant number 2020-GMD-089.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data will be available upon request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Unguren, E.; Huseyinli, T. The moderating effect of student club membership on the relationship between career intention in the tourism sector and post-graduate employability anxiety. J. Hosp. Leis. Sport Tour. Educ. 2020, 27, 100265. [Google Scholar] [CrossRef]
  2. Stamolampros, P.; Korfiatis, N.; Chalvatzis, K.; Buhalis, D. Job satisfaction and employee turnover determinants in high contact services: Insights from Employees’ Online reviews. Tour. Manag. 2019, 75, 130–147. [Google Scholar] [CrossRef]
  3. Čuić Tanković, A. Teaching Case Study: Building the Corporate Identity—The Case of Anniks Hotel. Tour. Hosp. Manag. 2020, 26, 455–462. [Google Scholar] [CrossRef]
  4. Kandampully, J.; Zhang, T.C.; Jaakkola, E. Customer experience management in hospitality: A literature synthesis, new understanding and research agenda. Int. J. Contemp. Hosp. Manag. 2018, 30, 21–56. [Google Scholar] [CrossRef]
  5. Ibrahim, R.; Boerhannoeddin, A.; Bakare, K.K. The effect of soft skills and training methodology on employee performance. Eur. J. Train. Dev. 2017, 41, 388–406. [Google Scholar] [CrossRef]
  6. Fajaryati, N.; Akhyar, M. The employability skills needed to face the demands of work in the future: Systematic literature reviews. Open Eng. 2020, 10, 595–603. [Google Scholar] [CrossRef]
  7. Kim, H.J.; Jeong, M. Research on hospitality and tourism education: Now and future. Tour. Manag. Perspect. 2018, 25, 119–122. [Google Scholar] [CrossRef]
  8. Qiu, H.; Li, Q.; Li, C. How technology facilitates tourism education in COVID-19: Case study of Nankai University. J. Hosp. Leis. Sport Tour. Educ. 2021, 29, 100288. [Google Scholar] [CrossRef]
  9. Hjalager, A.M.; Andersen, S. Tourism employment: Contingent work or professional career? Empl. Relat. 2001, 23, 115–129. [Google Scholar] [CrossRef]
  10. Walsh, K.; Chang, S.; Tse, E.C.-Y. Understanding students’ intentions to join the hospitality industry: The role of emotional intelligence, service orientation, and industry satisfaction. Cornell Hosp. Q. 2015, 56, 369–382. [Google Scholar] [CrossRef]
  11. Akeh, L.B. Emerging Dynamics in Tourism Industry Workforce Mobility in Southern Cross River State, Nigeria. Mediterr. J. Soc. Sci. 2021, 12, 56. [Google Scholar] [CrossRef]
  12. Guan, X.; Gong, J.; Huan, T.-C. Trick or treat! How to reduce co-destruction behavior in tourism workplace based on conservation of resources theory? J. Hosp. Tour. Manag. 2022, 52, 42–49. [Google Scholar] [CrossRef]
  13. Molin, F.; Haelermans, C.; Cabus, S.; Groot, W. Do feedback strategies improve students’ learning gain?-Results of a randomized experiment using polling technology in physics classrooms. Comput. Educ. 2021, 175, 104339. [Google Scholar] [CrossRef]
  14. Garzón, J.; Acevedo, J. Meta-analysis of the impact of Augmented Reality on students’ learning gains. Educ. Res. Rev. 2019, 27, 244–260. [Google Scholar] [CrossRef]
  15. Kroupova, K.; Havranek, T.; Irsova, Z. Student Employment and Education: A Meta-Analysis; IES Working Paper; Charles University in Prague, Institute of Economic Studies (IES): Prague, Czech Republic, 2021. [Google Scholar]
  16. Luo, Q.; Zhao, Y. Impact of Education with Chinese Feature Reform of Management Courses Based on Big Data Analysis on Employment Quality of College Students. In Proceedings of the 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy, Shanghai, China, 6–8 November 2020; pp. 295–300. [Google Scholar]
  17. Deng, Q. Study on Improvement in Employment Quality of College Students. In Proceedings of the 2020 6th International Conference on Social Science and Higher Education (ICSSHE 2020), Xiamen, China, 27–29 November 2020; pp. 904–912. [Google Scholar]
  18. Sibgatova, K.I.; Gilmanshina, S.I.; Khalikova, F.D.; Gilmanshin, I.R.; Akchurina, I.R.; Shchaveleva, N.G.; Fassakhova, G.R. Peculiarities of Pupils and Vocational College Students’ Career Guidance Modeling in the Integrated System “School-College-Enterprise”. Asian Soc. Sci. 2015, 11, 386–391. [Google Scholar] [CrossRef]
  19. Meng, Z. Exploration and Practice of Coaching Technology in College Career Guidance Under the Internet+ Era. In Proceedings of the 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy, Shanghai, China, 6–8 November 2020; pp. 573–580. [Google Scholar]
  20. Darwin, M.R.; Farozin, M.; Retnawati, H. What Career Guidance and Counseling Services Are Needed by Senior High School Students? J. Pendidik. Indones. 2020, 9, 608–617. [Google Scholar] [CrossRef]
  21. Yu, Y.; Tang, R.; Li, Y.; Zhang, Z.; Li, J. Construction of evaluation system for college students’ career guidance service based on multi attribute group decision making. Acta Tech. 2016, 62, 1049–1060. [Google Scholar]
  22. Evans, C.; Kandiko Howson, C.; Forsythe, A. Making sense of learning gain in higher education. High. Educ. Pedagog. 2018, 3, 1–45. [Google Scholar] [CrossRef] [Green Version]
  23. McGrath, C.H.; Guerin, B.; Harte, E.; Frearson, M.; Manville, C. Learning Gain in Higher Education; RAND Corporation: Santa Monica, CA, USA, 2015. [Google Scholar]
  24. Leitner, P.; Ebner, M.; Ebner, M. Learning Analytics Challenges to Overcome in Higher Education Institutions. In Utilizing Learning Analytics to Support Study Success; Springer: Cham, Switzerland, 2019; pp. 91–104. [Google Scholar]
  25. Davis, T.M.; Murrell, P.H. A structural model of perceived academic, personal, and vocational gains related to college student responsibility. Res. High. Educ. 1993, 34, 267–289. [Google Scholar] [CrossRef]
  26. Mahan, D.M. The Four-Year Experience of First-Generation Students at a Small Independent University: Engagement, Student Learning, and Satisfaction; University of Louisville: Louisville, KY, USA, 2010. [Google Scholar]
  27. van Hek, M.; Kraaykamp, G.; Pelzer, B. Do schools affect girls’ and boys’ reading performance differently? A multilevel study on the gendered effects of school resources and school practices. Sch. Eff. Sch. Improv. 2018, 29, 1–21. [Google Scholar] [CrossRef] [Green Version]
  28. David, E.C.; Emunemu, B. Availability of physical and instructional facilities and its impact on student learning outcome in public secondary schools in Ogun state, Nirgeria. Afr. J. Educ. Manag. 2018, 19, 87–100. [Google Scholar]
  29. Lubis, A.S. The Influence of School Facilities and Educational Cost on Students Decision Making to Continue Education to Senior High School Al Ulum Terpadu with the School Location as Moderating Variable. In Proceedings of the International Conference of ASEAN Perspective and Policy (ICAP), Medan, Indonesia, 3 May 2018; pp. 140–144. [Google Scholar]
  30. Owoeye, J.S.; Olatunde Yara, P. School facilities and academic achievement of secondary school agricultural science in Ekiti State, Nigeria. Asian Soc. Sci. 2011, 7, 64–74. [Google Scholar] [CrossRef]
  31. Achor, E.E.; Ityobee, S. Assessment of availability of ICT facilities for Basic Science Teaching and Learning in Upper Basic schools in Benue State. J. Res. Curric. Teach. 2020, 12, 1–10. [Google Scholar]
  32. Astin, A.W. Student involvement: A developmental theory for higher education. J. Coll. Stud. Pers. 1984, 25, 297–308. [Google Scholar]
  33. Membrillo-Hernández, J.; Muñoz-Soto, R.B.; Rodríguez-Sánchez, Á.C.; Díaz-Quiñonez, J.A.; Villegas, P.V.; Castillo-Reyna, J.; Ramírez-Medrano, A. Student Engagement Outside the Classroom: Analysis of a Challenge-Based Learning Strategy in Biotechnology Engineering. In Proceedings of the 2019 IEEE Global Engineering Education Conference (EDUCON), Dubai, United Arab Emirates, 9–11 April 2019; pp. 617–621. [Google Scholar]
  34. Khan, A.; Ahmad, F.H.; Malik, M.M. Use of digital game based learning and gamification in secondary school science: The effect on student engagement, learning and gender difference. Educ. Inf. Technol. 2017, 22, 2767–2804. [Google Scholar] [CrossRef]
  35. Manwaring, K.C.; Larsen, R.; Graham, C.R.; Henrie, C.R.; Halverson, L.R. Investigating student engagement in blended learning settings using experience sampling and structural equation modeling. Internet High. Educ. 2017, 35, 21–33. [Google Scholar] [CrossRef]
  36. Puspitarini, Y.D.; Hanif, M. Using Learning Media to Increase Learning Motivation in Elementary School. Anatol. J. Educ. 2019, 4, 53–60. [Google Scholar] [CrossRef]
  37. Behle, H.; Maher, S. Measuring “Teaching Excellence” and “Learning Gain” in the United Kingdom. Beiträge Hochschulforschung 2018, 40, 48–67. [Google Scholar]
  38. Praetorius, A.K.; Klieme, E.; Herbert, B.; Pinger, P. Generic dimensions of teaching quality: The German framework of three basic dimensions. ZDM 2018, 50, 407–426. [Google Scholar] [CrossRef]
  39. Canales, A.; Maldonado, L. Teacher quality and student achievement in Chile: Linking teachers’ contribution and observable characteristics. Int. J. Educ. Dev. 2018, 60, 33–50. [Google Scholar] [CrossRef]
  40. Kini, T.; Podolsky, A. Does Teaching Experience Increase Teacher Effectiveness? A Review of the Research. Learning Policy Institute. 2016. Available online: https://files.eric.ed.gov/fulltext/ED606426.pdf (accessed on 4 December 2022).
  41. Fang, Y. Teaching quality evaluation of tourism management course based on multi-level fuzzy comprehensive evaluation. J. Yellow River Conserv. Tech. Inst. 2017, 29, 85–87. [Google Scholar]
  42. Weiquan, G. Practice Teaching Quality Evaluation System Construction for Tourism Management Major in the Applied Undergraduate Universities in Liaoning. J. Heilongjiang Univ. Technol. Compr. Ed. 2016, 16, 22–25. [Google Scholar]
  43. Xinyi, L. An Empirical Study on Teaching Quality Evaluation for Tourism Management Major Based on Expectancy-Disconfirmation Theory from the Perspective of Student’s Perception. J. Jinzhong Univ. 2017, 34, 80–86. [Google Scholar]
  44. Ullman, J.B.; Bentler, P.M. Structural Equation Modeling. In Handbook of Psychology, 2nd ed.; John Wiley & Sons: Hoboken, NJ, USA, 2012; pp. 661–690. [Google Scholar]
  45. Mueller, R.O.; Hancock, G.R. Structural Equation Modeling. In The Reviewer’s Guide to Quantitative Methods in the Social Sciences; Routledge: Oxford, UK, 2018; pp. 445–456. [Google Scholar]
  46. Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 2019, 31, 2–24. [Google Scholar] [CrossRef]
  47. Hair, J.F.; Sarstedt, M.; Ringle, C.M.; Mena, J.A. An assessment of the use of partial least squares structural equation modeling in marketing research. J. Acad. Mark. Sci. 2012, 40, 414–433. [Google Scholar] [CrossRef]
Sustainability 14 16601 g001 550
Figure 1. Proposed model on Student’s Learning Gain.
Figure 1. Proposed model on Student’s Learning Gain.
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Figure 2. SEM model of the learning gain of students. *** p < 0.001, and the parameter is estimated to be significant at 99.9% confidence level. ** p < 0.01, and the parameter is estimated to be significant at 99% confidence level.
Figure 2. SEM model of the learning gain of students. *** p < 0.001, and the parameter is estimated to be significant at 99.9% confidence level. ** p < 0.01, and the parameter is estimated to be significant at 99% confidence level.
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Table
Table 1. Reliability and validity test of the scale.
Table 1. Reliability and validity test of the scale.
Latent VariablesMeasurement ItemsStandardized EstimateCronbach’s αCRAVE
Students’ employment situationES1 I can be appreciated by my employer0.8920.8640.9170.787
ES2 I can find a job that I am satisfied with0.905
ES3 My job position will relate to my major position0.863
Students’ learning gainSG1 Through college study, my professional knowledge can support my work 0.7910.8950.9230.705
SG2 Through college study, I improved my learning ability0.884
SG3 Through college study, I improved my innovation ability0.847
SG4 Through college study, I improved my teamwork ability0.854
SG5 Through college study, I improved my social morality0.817
Student engagementSE1 I have the courage to ask profound questions in class0.7640.7740.8680.687
SE2 Through discussion, teachers and students establish mutually stimulating partnerships0.859
SE3 Teachers pay attention to communication and interaction with students0.861
Internship and employment guidanceEG1 The college has channels to let me know the employer’s requirements for students0.8980.8410.9040.759
EG2 The internship site conditions can meet my internship needs0.833
EG3 The college recommended me for employment0.880
College supportCS1 The college often collects my opinions on the quality of education0.8720.8600.9150.781
CS2 I can get enough guidance from the college in my studies0.910
CS3 Students from different family backgrounds can receive positive guidance from the college0.869
College environmental FacilitiesCF1 Sports and exercise facilities can meet my exercise needs0.8360.7930.8660.617
CF2 The library and other learning facilities can effectively support my study0.801
CF3 The hygiene of the school meals is to my satisfaction0.776
CF4 Student dormitories are comfortable0.726
Curriculum
design		CD1 The teaching courses can meet my need for professional knowledge0.8840.8520.9100.772
CD2 The teaching content is advanced0.879
CD3 The curriculum is conducive to promoting my identity of tourism major0.873
Teachers’ teaching qualityTQ1 The teachers of specialized courses have the necessary professional knowledge0.8990.8520.9100.772
TQ2 Teachers of specialized courses have practical working experience in their major0.889
TQ3 As a result of the high standard of a certain teacher, I increased my study effort0.847
Table
Table 2. The results of discriminant validity test.
Table 2. The results of discriminant validity test.
AVECDSEESSGEGTQCSCF
CD0.7720.879
SE0.7050.6240.829
ES0.7870.5540.5750.887
SG0.7050.6540.6790.6940.839
EG0.7590.6080.6260.6260.6970.871
TQ0.7720.5980.6920.4860.6290.5050.878
CS0.7810.5970.6490.5870.7020.750.5430.884
CF0.6170.5020.5470.5080.5770.6780.4420.6280.786
Table
Table 3. Hypothesis testing results.
Table 3. Hypothesis testing results.
Model AssumptionsPath Coefficient (β)t ValueResults
H1SG→ES0.501 ***13.315SUPPORTED
H2aEG→ES0.277 ***7.085SUPPORTED
H2bEG→SG0.214 ***4.694SUPPORTED
H3CS→EG0.750 ***32.398SUPPORTED
H5CS→SG0.215 ***5.655SUPPORTED
H6CF→SG0.0511.324UNSUPPORTED
H7SE→SG0.149 ***3.524SUPPORTED
H8aCD→SG0.172 ***4.585SUPPORTED
H8bCD→SE0.326 ***9.016SUPPORTED
H9aTQ→SG0.176 ***4.92SUPPORTED
H9bTQ→SE0.497 ***14.335SUPPORTED
*** p < 0.001, and the parameter is estimated to be significant at 99.9% confidence level.
Table
Table 4. Results of mediation effects testing.
Table 4. Results of mediation effects testing.
Model AssumptionsPath Coefficientt ValueResults
H4aCS→EG→SG0.161 ***4.691SUPPORTED
H4bEG→SG→ES0.107 ***4.466SUPPORTED
H10aCD→SE→SG0.049 **3.231SUPPORTED
H10bTQ→SE→SG0.074 **3.429SUPPORTED
*** p < 0.001, and the parameter is estimated to be significant at 99.9% confidence level. ** p < 0.01, and the parameter is estimated to be significant at 99% confidence level.
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Zhong, L.; Li, X.; Sun, S.; Law, R.; Qi, X.; Dong, Y. Influencing Factors of Students’ Learning Gains in Tourism Education: An Empirical Study on 28 Tourism Colleges in China. Sustainability 2022, 14, 16601. https://doi.org/10.3390/su142416601

AMA Style

Zhong L, Li X, Sun S, Law R, Qi X, Dong Y. Influencing Factors of Students’ Learning Gains in Tourism Education: An Empirical Study on 28 Tourism Colleges in China. Sustainability. 2022; 14(24):16601. https://doi.org/10.3390/su142416601

Chicago/Turabian Style

Zhong, Lina, Xiaonan Li, Sunny Sun, Rob Law, Xiangchi Qi, and Yingchao Dong. 2022. "Influencing Factors of Students’ Learning Gains in Tourism Education: An Empirical Study on 28 Tourism Colleges in China" Sustainability 14, no. 24: 16601. https://doi.org/10.3390/su142416601

APA Style

Zhong, L., Li, X., Sun, S., Law, R., Qi, X., & Dong, Y. (2022). Influencing Factors of Students’ Learning Gains in Tourism Education: An Empirical Study on 28 Tourism Colleges in China. Sustainability, 14(24), 16601. https://doi.org/10.3390/su142416601

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