Personality traits and learning effectiveness in online vocational education
Insights from international students in high-tech learning contexts in China
DOI:
https://doi.org/10.32674/n2zbza81Keywords:
Personality traits, Online learning self-efficacy, High-tech learning support, Online vocational education, International students, PLS-SEM, ChinaAbstract
This study examines how personality traits influence learning effectiveness in online vocational education among international students in China, with attention to the mediating role of online learning self-efficacy and the moderating role of perceived high-tech learning support. Using a mixed-method design, survey data were collected from 412 international students enrolled in online or blended vocational programs. Measures included personality traits (conscientiousness and openness), online learning self-efficacy, perceived high-tech learning support, and learning effectiveness. A PLS-SEM-inspired path model with bootstrapping tested direct, indirect, and interaction effects, supplemented by 18 semi-structured interviews. Results indicate that personality traits positively predict self-efficacy and learning effectiveness, with self-efficacy partially mediating this relationship. Perceived high-tech learning support strengthens the link between self-efficacy and learning effectiveness. Interview findings underscore the role of simulation-based practice, timely feedback, and multilingual guidance in enhancing confidence and skill development.
References
Abe, J. A. A. (2020). Big five, linguistic styles, and successful online learning. The internet and Higher Education, 45, Article 100724. https://doi.org/10.1016/j.iheduc.2019.100724
Alkış, N., & Temizel, T. T. (2018). The impact of motivation and personality on academic performance in online and blended learning environments. Educational Technology & Society, 21(3), 35–47. https://www.jstor.org/stable/26458505
Anthonysamy, L., Lim, O. W., Singh, P., & Alasa, V. (2026). Exploring digital literacy challenges and strategies among international students studying abroad. Journal of International Students, 16(7), 209–232. https://doi.org/10.32674/fhpg3y40
Aristovnik, A., Keržič, D., Ravšelj, D., Tomaževič, N., & Umek, L. (2020). Impacts of the COVID-19 pandemic on life of higher education students: A global perspective. Sustainability, 12(20), Article 8438. https://doi.org/10.3390/su12208438
Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies and academic achievement in online higher education learning environments: A systematic review. The internet and Higher Education, 27, 1–13. https://doi.org/10.1016/j.iheduc.2015.04.007
Cheung, S. K., Kwok, L. F., Phusavat, K., & Yang, H. H. (2021). Shaping the future learning environments with smart elements: Challenges and opportunities. International Journal of Educational Technology in Higher Education, 18(1), 16. https://doi.org/10.1186/s41239-021-00254-1
Chiu, T. K. F. (2022). Applying the self-determination theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education, 54(Sup1), S14–S30. https://doi.org/10.1080/15391523.2021.1891998
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22. https://doi.org/10.1186/s41239-023-00392-8
Dahalan, F., Alias, N., & Shaharom, M. S. N. (2024). Gamification and game-based learning for vocational education and training: A systematic literature review. Education and Information Technologies, 29(2), 1279–1317. https://doi.org/10.1007/s10639-022-11548-w
Delcker, J., & Ifenthaler, D. (2022). Digital distance learning and the transformation of vocational schools from a qualitative perspective. Frontiers in Education, 7, Article 908046. https://doi.org/10.3389/feduc.2022.908046
Getenet, S., Cantle, R., Redmond, P., & Albion, P. (2024). Students' digital technology attitude, literacy and self-efficacy and their effect on online learning engagement. International Journal of Educational Technology in Higher Education, 21(1), 3. https://doi.org/10.1186/s41239-023-00437-y
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027
Han, X., Zhou, Q., Shi, W., & Yang, S. (2020). Online learning in vocational education of China during COVID-19: Achievements, challenges, and future developments. Journal of Educational Technology Development and Exchange, 13(2), 61–82. https://doi.org/10.18785/jetde.1302.04
Handrianto, C. (2026). Learning motivation and self-efficacy of Indonesian international students in Malaysian universities. Journal of International Students, 16(11), 65–82. https://doi.org/10.32674/9msrh666
Honicke, T., & Broadbent, J. (2016). The influence of academic self-efficacy on academic performance: A systematic review. Educational Research Review, 17, 63–84. https://doi.org/10.1016/j.edurev.2015.11.002
Iqbal, Q., Ahmad, N. H., & Li, Z. (2021). Frugal-based innovation model for sustainable development: Technological and market turbulence. Leadership & Organization Development Journal, 42(3), 396–407. https://doi.org/10.1108/LODJ-06-2020-0256
Liu, Y., Zhan, Q., & Zhao, W. (2023). A systematic review of VR/AR applications in vocational education: Models, affects, and performances. Interactive Learning Environments, 32(10), 6375–6392. https://doi.org/10.1080/10494820.2023.2263043
Mahmood, R., Ahmed, W., Mahmood, S., Suleman, R. M. F., Shabbir, M., & Wang, F. Y. (2026). From challenges to coping strategies. Journal of International Students, 16(3), 35–62. https://doi.org/10.32674/rcg6jt52
Mammadov, S. (2022). Big Five personality traits and academic performance: A meta-analysis. Journal of Personality, 90(2), 222–255. https://doi.org/10.1111/jopy.12663
Martin, F., & Bolliger, D. U. (2018). Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning, 22(1), 205–222. https://doi.org/10.24059/olj.v22i1.1092
Mok, K. H., Xiong, W., Ke, G., & Cheung, J. O. W. (2021). Impact of COVID-19 pandemic on international higher education and student mobility: Student perspectives from mainland China and Hong Kong. International Journal of Educational Research, 105, Article 101718. https://doi.org/10.1016/j.ijer.2020.101718
Ren, W., Zhu, X., & Liang, Z. (2024). How does internet access quality affect learning outcomes? A multiple mediation analysis among international students in China. Journal of International Students, 14(1), 449-468. https://ojed.org/jis/article/view/5881
Rivers, D. J. (2021). The role of personality traits and online academic self-efficacy in acceptance, actual use and achievement in Moodle. Education and Information Technologies, 26(4), 4353–4378. https://doi.org/10.1007/s10639-021-10478-3
Sghir, N., Adadi, A., & Lahmer, M. (2023). Recent advances in predictive learning analytics: A decade systematic review (2012–2022). Education and Information Technologies, 28, 8299–8333. https://doi.org/10.1007/s10639-022-11536-0
Sim, C. L., Li, Z., Chuah, F., Lim, Y. J., & Sin, K. Y. (2022). An empirical investigation of the role of lean six sigma practices on quality performance in medical device manufacturing industry. International Journal of Lean Six Sigma, 13(3), 671–691. https://doi.org/10.1108/IJLSS-06-2020-0089
Tian, M., & Lu, G. (2022). Online learning satisfaction and its associated factors among international students in China. Frontiers in Psychology, 13, 916449. https://doi.org/10.3389/fpsyg.2022.916449
Tian, M., Lu, G., Li, L., & Yin, H. (2021). International undergraduate students in Chinese higher education: An engagement typology and associated factors. Frontiers in Psychology, 12, Article 680392. https://doi.org/10.3389/fpsyg.2021.680392
Wen, W., Hu, D., & Hao, J. (2018). International students' experiences in China: Does the planned reverse mobility work? International Journal of Educational Development, 61, 204–212. https://doi.org/10.1016/j.ijedudev.2017.03.004
Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G.-J., & Paas, F. (2019). Supporting self-regulated learning in online learning environments and MOOCs: A systematic review. International Journal of Human–Computer Interaction, 35(4–5), 356–373. https://doi.org/10.1080/10447318.2018.1543084
Xu, L., Duan, P., Padua, S. A., & Li, C. (2022). The impact of self-regulated learning strategies on academic performance for online learning during COVID-19. Frontiers in Psychology, 13, Article 1047680. https://doi.org/10.3389/fpsyg.2022.1047680
Yokoyama, S. (2019). Academic self-efficacy and academic performance in online learning: A mini review. Frontiers in Psychology, 9, Article 2794. https://doi.org/10.3389/fpsyg.2018.02794
Yu, Z. (2021). The effects of gender, educational level, and personality on online learning outcomes during the COVID-19 pandemic. International Journal of Educational Technology in Higher Education, 18(1), Article 14. https://doi.org/10.1186/s41239-021-00252-3
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0
Zhang, Y. (2025). Assessing and enhancing emotional well-being in online learning for international undergraduates in China. Journal of International Students, 15(4), 209-228. https://doi.org/10.32674/cfbja013
Zhou, L., Xue, S., & Li, R. (2022). Extending the Technology Acceptance Model to explore students’ intention to use an online education platform at a university in China. SAGE Open, 12(1), 21582440221085259. https://doi.org/10.1177/21582440221085259
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