Abstract
Rapid technological change creates a persistent alignment problem for technical and vocational education and training (TVET): curricula may be formally linked to an occupation while students still experience weak correspondence between classroom learning and contemporary work. This study examines whether perceived curriculum–job alignment is associated with career commitment among students and recent graduates of Chinese higher vocational New Energy Vehicle (NEV) programs, and whether learning experience mediates that association. A cross-sectional survey produced 354 valid responses from students and recent graduates across multiple higher vocational institutions in one Chinese province. Curriculum–job alignment, learning experience, and career commitment were assessed on five-point scales. Learning experience was modeled as a second-order construct comprising teacher support, authentic resources, practice-oriented assessment, capability activation, and goal clarity. Confirmatory factor analysis and structural equation modeling were conducted, with indirect effects estimated through 5,000 bootstrap samples. The final model showed good fit (χ²/df = 2.12, CFI = .962, TLI = .952, RMSEA = .058, SRMR = .038). Curriculum–job alignment was positively associated with learning experience (β = .41, p < .001) and retained a direct association with career commitment (β = .22, p < .001). Learning experience was positively associated with career commitment (β = .46, p < .001), and the indirect effect was significant (β = .19, 95% CI [.14, .25]). The model explained 61% of the variance in career commitment. The findings indicate that alignment becomes educationally consequential when students experience it through credible teaching, current equipment, authentic assessment, active competence development, and visible career pathways. For fast-changing vocational fields, curriculum reform should therefore connect dynamic task mapping with deliberately designed learning experiences and staged workplace exposure.
References
Biggs, J. (1996). Enhancing teaching through constructive alignment. Higher Education, 32(3), 347–364. https://doi.org/10.1007/BF00138871
Billett, S. (2009). Realising the educational worth of integrating work experiences in higher education. Studies in Higher Education, 34(7), 827–843. https://doi.org/10.1080/03075070802706561
Blau, G. J. (1985). The measurement and prediction of career commitment. Journal of Occupational Psychology, 58(4), 277–288. https://doi.org/10.1111/j.2044-8325.1985.tb00201.x
Ferns, S. J., Zegwaard, K. E., Pretti, T. J., & Rowe, A. D. (2025). Defining and designing work-integrated learning curriculum. Higher Education Research & Development, 44(2), 371–385. https://doi.org/10.1080/07294360.2024.2399072
Gulikers, J. T. M., Bastiaens, T. J., & Kirschner, P. A. (2004). A five-dimensional framework for authentic assessment. Educational Technology Research and Development, 52(3), 67–86. https://doi.org/10.1007/BF02504676
Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Jackson, D. (2017). Developing pre-professional identity in undergraduates through work-integrated learning. Higher Education, 74(5), 833–853. https://doi.org/10.1007/s10734-016-0080-2
Jackson, D., & Dean, B. A. (2023). The contribution of different types of work-integrated learning to graduate employability. Higher Education Research & Development, 42(1), 93–110. https://doi.org/10.1080/07294360.2022.2048638
Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79–122. https://doi.org/10.1006/jvbe.1994.1027
Lizzio, A., Wilson, K., & Simons, R. (2002). University students’ perceptions of the learning environment and academic outcomes: Implications for theory and practice. Studies in Higher Education, 27(1), 27–52. https://doi.org/10.1080/03075070120099359
Ministry of Education of the People’s Republic of China. (2022, April 21). Vocational Education Law of the People’s Republic of China (2022 revision). https://www.moe.gov.cn/jyb_sjzl/sjzl_zcfg/zcfg_jyfl/202204/t20220421_620064.html
Ministry of Education of the People’s Republic of China. (2023, July 17). Notice on accelerating key tasks in the reform of the modern vocational education system. https://www.moe.gov.cn/srcsite/A07/zcs_zhgg/202307/t20230717_1069319.html
OECD. (2023). Building future-ready vocational education and training systems. OECD Publishing. https://doi.org/10.1787/28551a79-en
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879
Putnick, D. L., & Bornstein, M. H. (2016). Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. Developmental Review, 41, 71–90. https://doi.org/10.1016/j.dr.2016.06.004
Ramsden, P. (1991). A performance indicator of teaching quality in higher education: The Course Experience Questionnaire. Studies in Higher Education, 16(2), 129–150. https://doi.org/10.1080/03075079112331382944
UNESCO. (2022). Transforming technical and vocational education and training for successful and just transitions: UNESCO strategy 2022–2029. https://unesdoc.unesco.org/ark:/48223/pf0000383360

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