Abstract
Since the outbreak of the pandemic, a large number of university students have been required to adapt to e-learning. E-learning engagement, a key indicator of academic success, is thus a great concern of educational partitioners and researchers. Although previous studies have identified various determinants of e-learning engagement, there is a paucity of research that examines the associations between university students' personalities and their engagement in e-learning. Moreover, the mechanisms underlying the personality-engagement relationship are still poorly understood. The present study used the five-factor model of personality as the main theoretical framework to explore how students with different personality traits engaged in e-learning. Additionally, it examined whether achievement emotions and adaptability mediated the personality-engagement relationship in the e-learning context. A sample of 1004 students enrolled at Guizhou University participated in an online survey to collect data for the study. Employing structural equation modeling, the findings unveiled several significant results: (1) extroversion, agreeableness, openness to new experiences, and conscientiousness exhibited positive associations with e-learning engagement, (2) neuroticism demonstrated a negative relationship with e-learning engagement, (3) the mediating effect of enjoyment as an achievement emotion was observed between personality traits (excluding neuroticism) and e-learning engagement, (4) adaptability played a mediating role in the relationship between personality traits (excluding conscientiousness and neuroticism) and e-learning engagement, and (5) the negative achievement emotion of anxiety did not operate as a mediator between personality traits and e-learning engagement. This study enriches the understanding of the relationship between personality and engagement in the emerging field of e-learning. Moreover, it offers a fresh perspective on how to investigate mechanisms underlying the relationship between personality and engagement in the e-learning context. The findings could provide a basis for instructors who wish to deploy emotional and adaptability interventions to increase university students’ engagement in e-learning.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
References
Adeshola, I., & Agoyi, M. (2022). Examining factors influencing e-learning engagement among university students during covid-19 pandemic: A mediating role of “learning persistence”. Interactive Learning Environments, 1–28. https://doi.org/10.1080/10494820.2022.2029493
Alon, L., Sung, S., Cho, J., & Kizilcec, R. F. (2023). From emergency to sustainable e-learning: Changes and disparities in undergraduate course grades and experiences in the context of COVID-19. Computers & Education, 203, 104870. https://doi.org/10.1016/j.compedu.2023.104870
Al-Sharafi, M. A., Al-Emran, M., Iranmanesh, M., Al-Qaysi, N., Iahad, N. A., & Arpaci, I. (2022). Understanding the impact of knowledge management factors on the sustainable use of AI-based chatbots for educational purposes using a hybrid SEM-ANN approach. Interactive Learning Environments, 1–21. https://doi.org/10.1080/10494820.2022.2075014
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103, 411–423. https://doi.org/10.1037/0033-2909.103.3.411
Archambault, L., Leary, H., & Rice, K. (2022). Pillars of online pedagogy: A framework for teaching in e-learning environments. Educational Psychologist, 57(3), 178–191. https://doi.org/10.1080/00461520.2022.2051513
Audet, É. C., Levine, S. L., Metin, E., Koestner, S., & Barcan, S. (2021). Zooming their way through university: Which Big 5 traits facilitated students’ adjustment to online courses during the COVID-19 pandemic. Personality and Individual Differences, 180, 110969. https://doi.org/10.1016/j.paid.2021.110969
Baruth, O., & Cohen, A. (2023). Personality and satisfaction with online courses: The relation between the Big Five personality traits and satisfaction with e-learning activities. Education and Information Technologies, 28(1), 879–904. https://doi.org/10.1007/s10639-022-11199-x
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–606. https://doi.org/10.1037/0033-2909.88.3.588
Besser, A., Flett, G. L., & Zeigler-Hill, V. (2022). Adaptability to a sudden transition to e-learning during the COVID-19 pandemic: Understanding the challenges for students. Scholarship of Teaching and Learning in Psychology, 8(2), 85–105. https://doi.org/10.1037/stl0000198
Bhagat, K. K., Wu, L. Y., & Chang, C.-Y. (2019). The impact of personality on students’ perceptions towards online learning. Australasian Journal of Educational Technology, 35(4), 4. https://doi.org/10.14742/ajet.4162
Bosselut, G., Castro, O., Chevalier, S., & Fouquereau, E. (2020). Does perceived cohesion mediate the student personality–engagement relationship in the university setting? Journal of Educational Psychology, 112(8), 1692–1700. https://doi.org/10.1037/edu0000442
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Carver, C. S., & Scheier, M. F. (2002). Control processes and self-organization as complementary principles underlying behavior. Personality and Social Psychology Review, 6(4), 304–315. https://doi.org/10.1207/S15327957PSPR0604_05
Chen, P., Bao, C., & Gao, Q. (2021). Proactive personality and academic engagement: The mediating effects of teacher-student relationships and academic self-efficacy. Frontiers in Psychology, 12, 652994. https://doi.org/10.3389/fpsyg.2021.652994
Chen, X., He, J., Swanson, E., Cai, Z., & Fan, X. (2022). Big five personality traits and second language learning: A meta-analysis of 40 years’ research. Educational Psychology Review, 34(2), 851–887. https://doi.org/10.1007/s10648-021-09641-6
Closson, L. M., & Boutilier, R. R. (2017). Perfectionism, academic engagement, and procrastination among undergraduates: The moderating role of honors student status. Learning and Individual Differences, 57, 157–162. https://doi.org/10.1016/j.lindif.2017.04.010
Collie, R. J., & Martin, A. J. (2017). Students’ adaptability in mathematics: Examining self-reports and teachers’ reports and links with engagement and achievement outcomes. Contemporary Educational Psychology, 49, 355–366. https://doi.org/10.1016/j.cedpsych.2017.04.001
Collie, R. J., Holliman, A. J., & Martin, A. J. (2017). Adaptability, engagement and academic achievement at university. Educational Psychology, 37(5), 632–647. https://doi.org/10.1080/01443410.2016.1231296
Collie, R. J., Granziera, H., & Martin, A. J. (2018). Teachers’ perceived autonomy support and adaptability: An investigation employing the job demands-resources model as relevant to workplace exhaustion, disengagement, and commitment. Teaching and Teacher Education, 74, 125–136. https://doi.org/10.1016/j.tate.2018.04.015
Creswell, J. W., & Clark, V. L. P. (2017). Designing and Conducting Mixed Methods Research. SAGE Publications.
de la Fuente, J., Paoloni, P., Kauffman, D., Yilmaz Soylu, M., Sander, P., & Zapata, L. (2020). Big Five, self-regulation, and coping strategies as predictors of achievement emotions in undergraduate students. International Journal of Environmental Research and Public Health, 17(10), 10. https://doi.org/10.3390/ijerph17103602
Deng, W., Lei, W., Guo, X., Li, X., Ge, W., & Hu, W. (2022). Effects of regulatory focus on e-learning engagement of high school students: The mediating role of self-efficacy and academic emotions. Journal of Computer Assisted Learning, 38(3), 707–718. https://doi.org/10.1111/jcal.12642
Dewaele, J. M., & Li, C. (2021). Teacher enthusiasm and students’ social-behavioral learning engagement: The mediating role of student enjoyment and boredom in Chinese EFL classes. Language Teaching Research, 25(6), 922–945. https://doi.org/10.1177/13621688211014538
Earl, S. R., Taylor, I. M., Meijen, C., & Passfield, L. (2023). Trajectories in cognitive engagement, fatigue, and school achievement: The role of young adolescents’ psychological need satisfaction. Learning and Individual Differences, 101, 102248. https://doi.org/10.1016/j.lindif.2022.102248
Ewing, L. A., & Cooper, H. B. (2021). Technology-enabled remote learning during Covid-19: Perspectives of Australian teachers, students and parents. Technology, Pedagogy and Education, 30(1), 41–57. https://doi.org/10.1080/1475939X.2020.1868562
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059
Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. American Psychologist, 56, 218–226. https://doi.org/10.1037/0003-066X.56.3.218
Goldberg, L. R. (1993). The structure of phenotypic personality traits. American Psychologist, 48(1), 26–34. https://doi.org/10.1037/0003-066X.48.1.26
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis: A global perspective. Pearson Education.
Holliman, A. J., Martin, A. J., & Collie, R. J. (2018). Adaptability, engagement, and degree completion: A longitudinal investigation of university students. Educational Psychology, 38(6), 785–799. https://doi.org/10.1080/01443410.2018.1426835
Hong, J., Cao, W., Liu, X., Tai, K., & Zhao, L. (2021). Personality traits predict the effects of Internet and academic self-efficacy on practical performance anxiety in e-learning under the COVID-19 lockdown. Journal of Research on Technology in Education, 55(3), 426–440. https://doi.org/10.1080/15391523.2021.1967818
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Iterbeke, K., & De Witte, K. (2021). Helpful or harmful? The role of personality traits in student experiences of the COVID-19 crisis and school closure. Personality and Social Psychology Bulletin, 48(11), 1614–1632. https://doi.org/10.1177/01461672211050515
Izhar, L. I., Babiker, A., Rizki, E. E., Lu, C.-K., & Abdul Rahman, M. (2022). Emotion self-regulation in neurotic students: A pilot mindfulness-based intervention to assess its effectiveness through brain signals and behavioral data. Sensors, 22(7), 7. https://doi.org/10.3390/s22072703
John, O. P., & Srivastava, S. (1999). The big five trait taxonomy: history, measurement, and theoretical perspectives. In Handbook of Personality: Theory and Research (pp. 102–138). Guilford Press
Jung, Y., & Lee, J. (2018). Learning engagement and persistence in Massive Open Online Courses (MOOCS). Computers & Education, 122, 9–22. https://doi.org/10.1016/j.compedu.2018.02.013
Keller, H., & Karau, S. J. (2013). The importance of personality in students’ perceptions of the e-learning experience. Computers in Human Behavior, 29(6), 2494–2500. https://doi.org/10.1016/j.chb.2013.06.007
Komarraju, M., & Karau, S. J. (2005). The relationship between the big five personality traits and academic motivation. Personality and Individual Differences, 39(3), 557–567. https://doi.org/10.1016/j.paid.2005.02.013
Lee, P., & Wu, T. (2022). Mining relations between personality traits and learning styles. Information Processing & Management, 59(5), 103045. https://doi.org/10.1016/j.ipm.2022.103045
Li, F., Jin, T., Edirisingha, P., & Zhang, X. (2021). School-aged students’ sustainable e-learning engagement during COVID-19: Community of inquiry in a Chinese secondary education context. Sustainability, 13(18), 18. https://doi.org/10.3390/su131810147
Liu, H., Yao, M., Li, J., & Li, R. (2021a). Multiple mediators in the relationship between perceived teacher autonomy support and student engagement in math and literacy learning. Educational Psychology, 41(2), 116–136. https://doi.org/10.1080/01443410.2020.1837346
Liu, X., Gong, S., Zhang, H., Yu, Q., & Zhou, Z. (2021b). Perceived teacher support and creative self-efficacy: The mediating roles of autonomous motivation and achievement emotions in Chinese junior high school students. Thinking Skills and Creativity, 39, 100752. https://doi.org/10.1016/j.tsc.2020.100752
Liu, Y., Zhang, M., Qi, D., & Zhang, Y. (2022). Understanding the role of learner engagement in determining MOOCs satisfaction: A self-determination theory perspective. Interactive Learning Environments, 1–15. https://doi.org/10.1080/10494820.2022.2028853
Martin, F., & Borup, J. (2022). Online learner engagement: Conceptual definitions, research themes, and supportive practices. Educational Psychologist, 57(3), 162–177. https://doi.org/10.1080/00461520.2022.2089147
Martin, A. J., Nejad, H., Colmar, S., & Liem, G. A. D. (2012). Adaptability: Conceptual and empirical perspectives on responses to change, novelty and uncertainty. Journal of Psychologists and Counsellors in Schools, 22(1), 58–81. https://doi.org/10.1017/jgc.2012.8
Martin, A. J., Nejad, H. G., Colmar, S., & Liem, G. A. D. (2013). Adaptability: How students’ responses to uncertainty and novelty predict their academic and non-academic outcomes. Journal of Educational Psychology, 105(3), 728–746. https://doi.org/10.1037/a0032794
Martin, A. J., Yu, K., Ginns, P., & Papworth, B. (2017). Young people’s academic buoyancy and adaptability: A cross-cultural comparison of China with North America and the United Kingdom. Educational Psychology, 37(8), 930–946. https://doi.org/10.1080/01443410.2016.1202904
Martos Martínez, Á., Pérez-Fuentes, M. D. C., Molero Jurado, M. D. M., Simón Márquez, M. D. M., Barragán Martín, A. B., & Gázquez Linares, J. J. (2021). Empathy, affect and personality as predictors of engagement in nursing professionals. International Journal of Environmental Research and Public Health, 18(8), 8. https://doi.org/10.3390/ijerph18084110
McCrae, R. R., & Costa, P. T. (1987). Validation of the five-factor model of personality across instruments and observers. Journal of Personality and Social Psychology, 52(1), 81–90. https://doi.org/10.1037/0022-3514.52.1.81
McCrae, R. R., & John, O. P. (1992). An introduction to the five-factor model and its applications. Journal of Personality, 60(2), 175–215. https://doi.org/10.1111/j.1467-6494.1992.tb00970.x
McCrae, R. R., & Costa, P. T., Jr. (1999). A Five-Factor theory of personality. In L. A. Pervin & O. P. John (Eds.), Handbook of personality: Theory and research (pp. 139–153). Guilford Press.
Meyer, D. K., & Turner, J. C. (2002). Using instructional discourse analysis to study the scaffolding of student self-regulation. Educational Psychologist, 37(1), 17–25. https://doi.org/10.1207/S15326985EP3701_3
Mihai, M., Albert, C. N., Mihai, V. C., & Dumitras, D. E. (2022). Emotional and social engagement in the English language classroom for higher education students in the COVID-19 online context. Sustainability, 14(8), 8. https://doi.org/10.3390/su14084527
Mikulincer, M., Shaver, P. R., & Pereg, D. (2003). Attachment theory and affect regulation: The dynamics, development, and cognitive consequences of attachment-related strategies. Motivation and Emotion, 27(2), 77–102.
Moreira, P. A. S., Inman, R. A., Cloninger, K., & Cloninger, C. R. (2021). Student engagement with school and personality: A biopsychosocial and person-centred approach. British Journal of Educational Psychology, 91(2), 12388. https://doi.org/10.1111/bjep.12388
Nungu, L., Mukama, E., & Nsabayezu, E. (2023). Online collaborative learning and cognitive presence in mathematics and science education. Case study of university of Rwanda, college of education. Education and Information Technologies, 28, 10865–10884. https://doi.org/10.1007/s10639-023-11607-w
Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9
Pekrun, R. (2017). Emotion and achievement during adolescence. Child Development Perspectives, 11(3), 215–221. https://doi.org/10.1111/cdep.12237
Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37(2), 91–105. https://doi.org/10.1207/S15326985EP3702_4
Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36–48. https://doi.org/10.1016/j.cedpsych.2010.10.002
Peng, X., Chen, H., Wang, L., Tian, F., & Wang, H. (2020). Talking head-based L2 pronunciation training: Impact on achievement emotions, cognitive load, and their relationships with learning performance. International Journal of Human-Computer Interaction, 36(16), 1487–1502. https://doi.org/10.1080/10447318.2020.1752476
Piaget, J. (1952). The Origins of Intelligence in Children. International Universities Press.
Poon, W. C., Kunchamboo, V., & Koay, K. Y. (2022). E-Learning enagagement and effectiveness during the COVID-19 Pandemic: The interaction model. International Journal of Human–Computer Interaction, 1–15. https://doi.org/10.1080/10447318.2022.2119659
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
Quigley, M., Bradley, A., Playfoot, D., & Harrad, R. (2022). Personality traits and stress perception as predictors of students’ online engagement during the COVID-19 pandemic. Personality and Individual Differences, 194, 111645. https://doi.org/10.1016/j.paid.2022.111645
Qureshi, A., Wall, H., Humphries, J., & BahramiBalani, A. (2016). Can personality traits modulate student engagement with learning and their attitude to employability? Learning and Individual Differences, 51, 349–358. https://doi.org/10.1016/j.lindif.2016.08.026
Reeve, J., & Tseng, C. M. (2011). Agency as a fourth aspect of students’ engagement during learning activities. Contemporary Educational Psychology, 36(4), 257–267. https://doi.org/10.1016/j.cedpsych.2011.05.002
Rozin, P. (1999). The process of moralization. Psychological Science, 10(3), 218–221. https://doi.org/10.1111/1467-9280.00139
Sadoughi, M., & Hejazi, S. Y. (2021). Teacher support and academic engagement among EFL learners: The role of positive academic emotions. Studies in Educational Evaluation, 70, 101060. https://doi.org/10.1016/j.stueduc.2021.101060
Sak, M. (2021). Understanding the role of personality in explaining L2 learners’ DMC disposition. Foreign Language Annals, 54(2), 429–451. https://doi.org/10.1111/flan.12524
Sander, P., & de la Fuente, J. (2022). Modelling students’ academic confidence, personality and academic emotions. Current Psychology, 41(7), 4329–4340. https://doi.org/10.1007/s12144-020-00957-0
Santo, L. D., Peña-Jimenez, M., Canzan, F., Saiani, L., & Battistelli, A. (2022). The emotional side of the e-learning among nursing students: The role of the affective correlates on e-learning satisfaction. Nurse Education Today, 110, 105268. https://doi.org/10.1016/j.nedt.2022.105268
Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Burroughs, H., & Jinks, C. (2018). Saturation in qualitative research: Exploring its conceptualization and operationalization. Quality & Quantity, 52(4), 1893–1907. https://doi.org/10.1007/s11135-017-0574-8
Slof, B., van Leeuwen, A., Janssen, J., & Kirschner, P. A. (2021). Mine, ours, and yours: Whose engagement and prior knowledge affects individual achievement from online collaborative learning? Journal of Computer Assisted Learning, 37(1), 39–50. https://doi.org/10.1111/jcal.12466
Spielmann, J., Yoon, H. J. R., Ayoub, M., Chen, Y., Eckland, N. S., Trautwein, U., Zheng, A., & Roberts, B. W. (2022). An in-depth review of conscientiousness and educational issues. Educational Psychology Review, 34, 2745–2781. https://doi.org/10.1007/s10648-022-09693-2
Sulea, C., van Beek, I., Sarbescu, P., Virga, D., & Schaufeli, W. B. (2015). Engagement, boredom, and burnout among students: Basic need satisfaction matters more than personality traits. Learning and Individual Differences, 42, 132–138. https://doi.org/10.1016/j.lindif.2015.08.018
Tang, Y. M., Chen, P. C., Law, K. M. Y., Wu, C. H., Lau, Y., Guan, J., He, D., & Ho, G. T. S. (2021). Comparative analysis of Student’s live e-learning readiness during the coronavirus (COVID-19) pandemic in the higher education sector. Computers & Education, 168, 104211. https://doi.org/10.1016/j.compedu.2021.104211
Varela, O. E., Cater, J. J., & Michel, N. (2012). E-learning in management education: An empirical study of the role of personality traits. Journal of Computing in Higher Education, 24(3), 209–225. https://doi.org/10.1007/s12528-012-9059-x
Wang, Y., Cullen, K. L., Yao, X., & Li, Y. (2013). Personality, freshmen proactive social behavior, and college transition: Predictors beyond academic strategies. Learning and Individual Differences, 23, 205–212. https://doi.org/10.1016/j.lindif.2012.09.010
Wang, J., Liu, R., Ding, Y., Xu, L., Liu, Y., & Zhen, R. (2017). Teacher’s autonomy support and engagement in Math: Multiple mediating roles of self-efficacy, intrinsic value, and boredom. Frontiers in Psychology, 8, 1006. https://doi.org/10.3389/fpsyg.2017.01006
Wang, Y., Cao, Y., Gong, S., Wang, Z., Li, N., & Ai, L. (2022). Interaction and learning engagement in e-learning: The mediating roles of e-learning self-efficacy and academic emotions. Learning and Individual Differences, 94, 102128. https://doi.org/10.1016/j.lindif.2022.102128
Wang, X., Liu, Y., Ying, B., & Lin, J. (2023). The effect of learning adaptability on Chinese middle school students’ English academic engagement: The chain mediating roles of foreign language anxiety and English learning self-efficacy. Current Psychology, 42(8), 6682–6692. https://doi.org/10.1007/s12144-021-02008-8
Watson, D., & Hubbard, B. (1996). Adaptational style and dispositional structure: Coping in the context of the Five-Factor model. Journal of Personality, 64(4), 737–774. https://doi.org/10.1111/j.1467-6494.1996.tb00943.x
Wester, E. R., Walsh, L. L., Arango-Caro, S., & Callis-Duehl, K. L. (2021). Student engagement declines in STEM undergraduates during COVID-19–driven remote learning. Journal of Microbiology & Biology Education, 22(1), 1–11. https://doi.org/10.1128/jmbe.v22i1.2385
Wildermuth, C. D. M. E. S., Vaughan, A. G., & Christo-Baker, E. A. (2013). A path to passion: connecting personality, psychological conditions, and emotional engagement. Journal of Psychological Issues in Organizational Culture, 3(4), 18–45. https://doi.org/10.1002/jpoc.21082
Wilmot, M. P., & Ones, D. S. (2022). Agreeableness and its consequences: A quantitative review of meta-analytic findings. Personality and Social Psychology Review, 26(3), 242–280. https://doi.org/10.1177/10888683211073007
Woods, S. A., & Sofat, J. A. (2013). Personality and engagement at work: The mediating role of psychological meaningfulness. Journal of Applied Social Psychology, 43(11), 2203–2210. https://doi.org/10.1111/jasp.12171
Wu, F., & Teets, T. S. (2021). Effects of the COVID-19 pandemic on student engagement in a general chemistry course. Journal of Chemical Education, 98(12), 3633–3642. https://doi.org/10.1021/acs.jchemed.1c00665
Wu, C. H., Parker, S. K., Wu, L. Z., & Lee, C. (2018). When and why people engage in different forms of proactive behavior: Interactive effects of self-construals and work characteristics. Academy of Management Journal, 61(1), 293–323. https://doi.org/10.5465/amj.2013.1064
Wu, R., & Yu, Z. (2022). The influence of social isolation, technostress, and personality on the acceptance of online meeting platforms during the COVID-19 Pandemic. International Journal of Human–Computer Interaction, 1–18. https://doi.org/10.1080/10447318.2022.2097779
Yan, L., Whitelock-Wainwright, A., Guan, Q., Wen, G., Gašević, D., & Chen, G. (2021). Students’ experience of e-learning during the COVID-19 pandemic: A province-wide survey study. British Journal of Educational Technology, 52(5), 2038–2057. https://doi.org/10.1111/bjet.13102
Yilmaz, M. B., Orhan, F., & Zeren, S. G. (2022). Adolescent emotion scale for online lessons: A study from Turkey. Education and Information Technologies, 27(3), 3403–3420. https://doi.org/10.1007/s10639-021-10734-6
Yu, Z. (2021). The effects of gender, educational level, and personality on e-learning outcomes during the COVID-19 pandemic. International Journal of Educational Technology in Higher Education, 18(1), 14. https://doi.org/10.1186/s41239-021-00252-3
Yu, Z. (2022). A meta-analysis and bibliographic review of the effect of nine factors on e-learning outcomes across the world. Education and Information Technologies, 27(2), 2457–2482. https://doi.org/10.1007/s10639-021-10720-y
Yu, Z., Xu, W., & Sukjairungwattana, P. (2022). A meta-analysis of eight factors influencing MOOC-based learning outcomes across the world. Interactive Learning Environments, 1–20. https://doi.org/10.1080/10494820.2022.2096641
Yusoff, M. S. B., Hadie, S. N. H., & Yasin, M. A. M. (2021). The roles of emotional intelligence, neuroticism, and academic stress on the relationship between psychological distress and burnout in medical students. BMC Medical Education, 21(1), 293. https://doi.org/10.1186/s12909-021-02733-5
Zhang, K., Wu, S., Xu, Y., Cao, W., Goetz, T., & Parks-Stamm, E. J. (2021). Adaptability promotes student engagement under COVID-19: The multiple mediating effects of academic emotion. Frontiers in Psychology, 11, 633265. https://doi.org/10.3389/fpsyg.2020.633265
Zhang, R., Bi, N. C., & Mercado, T. (2023). Do zoom meetings really help? A comparative analysis of synchronous and asynchronous e-learning during Covid-19 pandemic. Journal of Computer Assisted Learning, 39(1), 210–217. https://doi.org/10.1111/jcal.12740
Zhao, L., Cao, C., Li, Y., & Li, Y. (2022). Determinants of the digital outcome divide in E-learning between rural and urban students: Empirical evidence from the COVID-19 pandemic based on capital theory. Computers in Human Behavior, 130, 107177. https://doi.org/10.1016/j.chb.2021.107177
Zheng, Y., Yu, S., & Liu, Z. (2023). Understanding individual differences in lower-proficiency students’ engagement with teacher written corrective feedback. Teaching in Higher Education, 28(2), 301–321. https://doi.org/10.1080/13562517.2020.1806225
Zyphur, M. J., Bonner, C. V., & Tay, L. (2023). Structural equation modeling in organizational research: The state of our science and some proposals for its future. Annual Review of Organizational Psychology and Organizational Behavior, 10(1), 495–517. https://doi.org/10.1146/annurev-orgpsych-041621-031401
Acknowledgements
The authors would like to extend sincere gratitude to anonymous reviewers and those who contribute to this study. This work is supported by Key Research and Application Project of the Key Laboratory of Key Technologies for Localization Language Services of the State Administration of Press and Publication, “Research on Localization and Intelligent Language Education Technology for the 'Belt and Road Initiative” (Project Number: CSLS 20230012), Special fund of Beijing Co-construction Project-Research and reform of the “Undergraduate Teaching Reform and Innovation Project” of Beijing higher education in 2020-innovative “multilingual +” excellent talent training system (202010032003), The Fundamental Research Funds for the Central Universities, and the Research Funds of Beijing Language and Culture University (23YCX004), and Beijing Language and Culture University Excellent Doctoral Dissertation Cultivation Program Funding Project.
Funding
This work is supported by Key Research and Application Project of the Key Laboratory of Key Technologies for Localization Language Services of the State Administration of Press and Publication, “Research on Localization and Intelligent Language Education Technology for the 'Belt and Road Initiative” (Project Number: CSLS 20230012), Special fund of Beijing Co-construction Project-Research and reform of the “Undergraduate Teaching Reform and Innovation Project” of Beijing higher education in 2020-innovative “multilingual +” excellent talent training system (202010032003), The Fundamental Research Funds for the Central Universities, and the Research Funds of Beijing Language and Culture University (23YCX004), and Beijing Language and Culture University Excellent Doctoral Dissertation Cultivation Program Funding Project.
Author information
Authors and Affiliations
Contributions
Rong Wu: Methodology, Investigation, Editing, and Writing-Original Draft.
Zhonggen Yu: Conceptualization and Funding acquisition.
Corresponding author
Ethics declarations
Ethics approval statement
The study was approved by the institutional review board of Beijing Language and Culture University. All researchers can provide written informed consents.
Informed consent
All participants voluntarily signed the consent form before answering the questionnaire.
Conflict of interest/competing interests
We have no conflicts of interest to declare that are relevant to the content of this article.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Zhonggen Yu, Research Fellow of Academy of International Language Services and Director of Center for Intelligent Language Education Research, National Base for Language Service Export, Beijing Language and Culture University.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wu, R., Yu, Z. Relationship between university students’ personalities and e-learning engagement mediated by achievement emotions and adaptability. Educ Inf Technol 29, 10821–10850 (2024). https://doi.org/10.1007/s10639-023-12222-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-023-12222-5