Learning satisfaction in virtual reality: the role of persuasive design
Wiafe, I., Ekpezu, A. O., Gyamera, G. O., Winful, F. B. P., Atsakpo, E. D., Nutropkor, C. and Gulliver, S. R.
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1080/10447318.2024.2440205 Abstract/SummaryGiven the positive impact of virtual reality learning environments on students’ learning satisfaction, it is imperative to identify the key features within these environments that contribute to such satisfaction. This study examined how persuasive features enhance students’ learning satisfaction via psychological outcomes within a persuasive immersive virtual reality learning environment (IVRLE). Using partial least squares structural equation modeling, quantitative data obtained from 115 IVRLE users were analyzed. The results show that by leveraging persuasive features such as unobtrusiveness, design aesthetics, primary task support, credibility support, dialogue support, and perceived persuasiveness, educators can create immersive learning environments that effectively engage students cognitively and emotionally, thereby enhancing learning satisfaction. Among the direct determinants of students’ learning satisfaction, perceived enjoyment exhibited the strongest impact. These results underscore the relevance of designing virtual reality learning environments as persuasive educational environments that shape learning behaviors and also caters to the psychological needs of students.
Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102(June 2019), 67–86. https://doi.org/10.1016/j.chb.2019.08.004
Alhammad, M. M., Wiafe, I., & Gulliver, S. R. (2021). Exploring the Impact of Persuasive Features on Customer Satisfaction Levels of E-Commerce Websites Based on the Kano Model. In R. Ali, B. Lugrin, & F. Charles (Eds.), PERSUASIVE 2021. Lecture Notes in Computer Science (Vol. 12684, pp. 11–13). Springer, Cham. https://doi.org/10.4037/aacnacc2022378
Almulla, M. A. (2024). Investigating influencing factors of learning satisfaction in AI ChatGPT for research: University students perspective. Heliyon, 10(11). https://doi.org/10.1016/j.heliyon.2024.e32220
Amerstorfer, C. M., & Freiin von Münster-Kistner, C. (2021). Student Perceptions of Academic Engagement and Student-Teacher Relationships in Problem-Based Learning. Frontiers in Psychology, 12(October), 1–18. https://doi.org/10.3389/fpsyg.2021.713057
Bohne, T., Heine, I., Gurerk, O., Rieger, C., Kemmer, L., & Cao, L. Y. (2021). Perception Engineering Learning with Virtual Reality. IEEE Transactions on Learning Technologies, 14(4), 500–514. https://doi.org/10.1109/TLT.2021.3107407
Burić, I., & Wang, H. (2024). Relationships among teacher enjoyment, emotional labor, and perceived student engagement: A daily diary approach. Journal of School Psychology, 103(July 2022), 101271. https://doi.org/10.1016/j.jsp.2023.101271
Burov, O. Yu., & Pinchuk, O. P. (2023). A meta-analysis of the most influential factors of the virtual reality in education for the health and efficiency of students’ activity. Educational Technology Quarterly, 2023(1), 58–68. https://doi.org/10.55056/etq.435
Chaouali, W., Ben Yahia, I., Lunardo, R., & Triki, A. (2019). Reconsidering the “what is beautiful is good” effect: When and how design aesthetics affect intentions towards mobile banking applications. International Journal of Bank Marketing, 37(7), 1525–1546. https://doi.org/10.1108/IJBM-12-2018-0337
Chen, C. C., & Tu, H. Y. (2021). The Effect of Digital Game-Based Learning on Learning Motivation and Performance Under Social Cognitive Theory and Entrepreneurial Thinking. Frontiers in Psychology, 12(December). https://doi.org/10.3389/fpsyg.2021.750711
Chen, F.-Q., Leng, Y.-F., Ge, J.-F., Wang, D.-W., Li, C., Chen, B., & Sun, Z.-L. (2020). Effectiveness of virtual reality in nursing education: Meta-analysis. Journal of Medical Internet Research, 22(9), 1–13. https://doi.org/10.2196/18290
Chen, J., Fu, Z., Liu, H., & Wang, J. (2023). Effectiveness of Virtual Reality on Learning Engagement: A Meta-Analysis. International Journal of Web-Based Learning and Teaching Technologies, 19(1), 1–14. https://doi.org/10.4018/IJWLTT.334849
Chen, T., Luo, H., Feng, Q., & Li, G. (2023). Effect of Technology Acceptance on Blended Learning Satisfaction: The Serial Mediation of Emotional Experience, Social Belonging, and Higher-Order Thinking. International Journal of Environmental Research and Public Health, 20(5). https://doi.org/10.3390/ijerph20054442
Cheon, J., Chung, S., & Lee, S. (2015). The roles of attitudinal perceptions and cognitive achievements in a serious game. Journal of Educational Computing Research, 52(1), 3–25. https://doi.org/10.1177/0735633114568851
Chou, S. W., & Liu, C. H. (2005). Learning effectiveness in a Web-based virtual learning environment: A learner control perspective. Journal of Computer Assisted Learning, 21(1), 65–76. https://doi.org/10.1111/j.1365-2729.2005.00114.x
Chow, Y. W., Susilo, W., Phillips, J. G., Baek, J., & Vlahu-Gjorgievska, E. (2017). Video games and virtual reality as persuasive technologies for health care: An overview. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 8(3), 18–35. https://doi.org/10.22667/JOWUA.2017.09.30.018
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (N. Hillsdale & Lawrence Erlbaum, Eds.; 2nd ed.). Routledge. https://doi.org/https://doi.org/10.4324/9780203771587
Dabi, J., Wiafe, I., Stibe, A., & Abdulai, J. D. (2018). Can an enterprise system persuade? The role of perceived effectiveness and social influence. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10809 LNCS, 45–55. https://doi.org/10.1007/978-3-319-78978-1_4
Dağhan, G., & Akkoyunlu, B. (2016). Modeling the continuance usage intention of online learning environments. Computers in Human Behavior, 60, 198–211. https://doi.org/10.1016/j.chb.2016.02.066
Devincenzi, S., Kwecko, V., De Toledo, F. P., Mota, F. P., Casarin, J., & Da Costa Botelho, S. S. (2017). Persuasive technology: Applications in education. Proceedings - Frontiers in Education Conference, FIE, 2017-Octob(January 2018), 1–7. https://doi.org/10.1109/FIE.2017.8190439
Efendi, D., Apriliyasari, R. W., Prihartami Massie, J. G. E., Wong, C. L., Natalia, R., Utomo, B., Sunarya, C. E., Apriyanti, E., & Chen, K. H. (2023). The effect of virtual reality on cognitive, affective, and psychomotor outcomes in nursing staffs: systematic review and meta-analysis. BMC Nursing, 22(1), 1–15. https://doi.org/10.1186/s12912-023-01312-x
Ekpezu, A. O., Wiafe, I., Nutrokpor, C., & Oinas-kukkonen, H. (2024). Examining Continuance Intention to Exercise in a Virtual Reality Environment. Proceedings of the 57th Hawaii International Conference on System Sciences, 1, 3527–3536. https://hdl.handle.net/10125/106810
Ekpezu, A. O., Wiafe, I., & Oinas-kukkonen, H. (2024). Technological Factors that Influence User Compliance with Behavior Change Support Systems : A Systematic Review. Proceedings of the 57th Hawaii International Conference on System Sciences, 1, 3434–3443.
Fogg, B. J. (2003). Persuasive Technology: Using Computers to Change What We Think and Do. In Persuasive Technology: Using Computers to Change What We Think and Do. Elsevier Inc. https://doi.org/10.1016/B978-1-55860-643-2.X5000-8
Fowler, C. (2015). Virtual reality and learning: Where is the pedagogy? British Journal of Educational Technology, 46(2), 412–422. https://doi.org/10.1111/bjet.12135
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
Gao, Y., & Zhu, X. (2023). Research on the learning experience of virtual simulation class experimental teaching and learning based on the perspective of nursing students. BMC Nursing, 22(1). https://doi.org/10.1186/s12912-023-01534-z
Hair, J. F., Hult, G. T., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2022a). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. In Handbook of Market Research. Springer Nature. https://doi.org/10.1007/978-3-319-57413-4_15
Hair, J. F., Hult, G. T., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2022b). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. In Handbook of Market Research. Springer Nature. https://doi.org/10.1007/978-3-319-57413-4_15
Hamzah, W. M. A. F. W., Ali, N. H., Saman, M. Y. M., Yusoff, M. H., & Yacob, A. (2015). Influence of Gamification on Students’ Motivation in using E-Learning Applications Based on the Motivational Design Model. International Journal of Emerging Technologies in Learning, 10(2), 30–34. https://doi.org/10.3991/IJET.V10I2.4355
Handayani, P. W., Gelshirani, N. B., Azzahro, F., Pinem, A. A., & Hidayanto, A. N. (2020). The influence of argument quality, source credibility, and health consciousness on satisfaction, use intention, and loyalty on mobile health application use. Informatics in Medicine Unlocked, 20, 100429. https://doi.org/10.1016/j.imu.2020.100429
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hirota, M., Kanda, H., Endo, T., Miyoshi, T., Miyagawa, S., Hirohara, Y., Yamaguchi, T., Saika, M., Morimoto, T., & Fujikado, T. (2019). Comparison of visual fatigue caused by head-mounted display for virtual reality and two-dimensional display using objective and subjective evaluation. Ergonomics, 62(6), 759–766. https://doi.org/10.1080/00140139.2019.1582805
Huang, C. (2021). Exploring the Continuous Usage Intention of Online Learning Platforms from the Perspective of Social Capital. Information, 12(141), 1–14. https://doi.org/https:// doi.org/10.3390/info12040141 Academic
Huang, C. M., Liao, J. Y., Lin, T. Y., Hsu, H. P., Charles Lee, T. C., & Guo, J. L. (2021). Effects of user experiences on continuance intention of using immersive three-dimensional virtual reality among institutionalized older adults. Journal of Advanced Nursing, 77(9), 3784–3796. https://doi.org/10.1111/jan.14895
Joo, Y. J., Park, S., & Shin, E. K. (2017). Students’ expectation, satisfaction, and continuance intention to use digital textbooks. Computers in Human Behavior, 69, 83–90. https://doi.org/10.1016/j.chb.2016.12.025
Khorasani, S., Victor Syiem, B., Nawaz, S., Knibbe, J., & Velloso, E. (2023). Hands-on or hands-off: Deciphering the impact of interactivity on embodied learning in VR. Computers & Education: X Reality, 3(August), 100037. https://doi.org/10.1016/j.cexr.2023.100037
Kim, D., & Ko, Y. J. (2019). The impact of virtual reality (VR) technology on sport spectators’ flow experience and satisfaction. Computers in Human Behavior, 93(July 2018), 346–356. https://doi.org/10.1016/j.chb.2018.12.040
Koranteng, F. N., Ham, J., Wiafe, I., & Matzat, U. (2021). The role of usability, aesthetics, usefulness and primary task support in predicting the perceived credibility of academic social networking sites. Behaviour and Information Technology, 0(0), 1–16. https://doi.org/10.1080/0144929X.2021.2009570
Lee, S., Ha, S., & Widdows, R. (2011). Consumer responses to high-technology products: Product attributes, cognition, and emotions. Journal of Business Research, 64(11), 1195–1200. https://doi.org/10.1016/j.jbusres.2011.06.022
Lehto, T., & Oinas-Kukkonen, H. (2015). Explaining and predicting perceived effectiveness and use continuance intention of a behaviour change support system for weight loss. Behaviour and Information Technology, 34(2), 176–189. https://doi.org/10.1080/0144929X.2013.866162
Lehto, T., Oinas-Kukkonen, H., & Drozd, F. (2012). Factors affecting perceived persuasiveness of a behavior change support system. International Conference on Information Systems, ICIS 2012, 3(December), 1926–1939.
Liu, K., Zhang, W., Li, W., Wang, T., & Zheng, Y. (2023). Effectiveness of virtual reality in nursing education: a systematic review and meta-analysis. BMC Medical Education, 23(1), 1–11. https://doi.org/10.1186/s12909-023-04662-x
Liu, R., Wang, L., Lei, J., Wang, Q., & Ren, Y. (2020). Effects of an immersive virtual reality-based classroom on students’ learning performance in science lessons. British Journal of Educational Technology, 51(6), 2034–2049. https://doi.org/10.1111/bjet.13028
Makransky, G., & Lilleholt, L. (2018). A structural equation modeling investigation of the emotional value of immersive virtual reality in education. Educational Technology Research and Development, 66(5), 1141–1164. https://doi.org/10.1007/s11423-018-9581-2
Makransky, G., & Petersen, G. B. (2021). The Cognitive Affective Model of Immersive Learning (CAMIL): a Theoretical Research-Based Model of Learning in Immersive Virtual Reality. Educational Psychology Review, 33(3), 937–958. https://doi.org/10.1007/s10648-020-09586-2
Makransky, G., Terkildsen, T. S., & Mayer, R. E. (2019a). Adding immersive virtual reality to a science lab simulation causes more presence but less learning. Learning and Instruction, 60(November), 225–236. https://doi.org/10.1016/j.learninstruc.2017.12.007
Makransky, G., Terkildsen, T. S., & Mayer, R. E. (2019b). Adding immersive virtual reality to a science lab simulation causes more presence but less learning. Learning and Instruction, 60(November), 225–236. https://doi.org/10.1016/j.learninstruc.2017.12.007
Mayer, R. E., Makransky, G., & Parong, J. (2023a). The Promise and Pitfalls of Learning in Immersive Virtual Reality. International Journal of Human-Computer Interaction, 39(11), 2229–2238. https://doi.org/10.1080/10447318.2022.2108563
Mayer, R. E., Makransky, G., & Parong, J. (2023b). The Promise and Pitfalls of Learning in Immersive Virtual Reality. International Journal of Human-Computer Interaction, 39(11), 2229–2238. https://doi.org/10.1080/10447318.2022.2108563
Menck, J. H. D., Lechte, H., Lembcke, T. B., Brendel, A. B., & Kolbe, L. M. (2023). Towards Design Principles for Experimental Simulations in Virtual Reality - Learning from Driving Simulators. Proceedings of the Annual Hawaii International Conference on System Sciences, 2023-Janua, 1303–1312.
Merz, M., Augsburg, U., Ackermann, L., & Ackermann, L. (2021). Design Principles of Persuasive Systems – Review and Discussion of the Persuasive Systems Design Model. Twenty-Seventh Americas Conference on Information Systems, Montreal, 2021, 1–10.
Moreira, G. J., Luna-Nevarez, C., & McGovern, E. (2022). It’S About Enjoying the Virtual Experience: the Role of Enjoyment and Engagement in the Adoption of Virtual Reality in Marketing Education. Marketing Education Review, 32(3), 224–239. https://doi.org/10.1080/10528008.2021.1965486
Mouatt, B., Smith, A. E., Mellow, M. L., Parfitt, G., Smith, R. T., & Stanton, T. R. (2020). The Use of Virtual Reality to Influence Motivation, Affect, Enjoyment, and Engagement During Exercise: A Scoping Review. Frontiers in Virtual Reality, 1(December). https://doi.org/10.3389/frvir.2020.564664
Mousas, C., Anastasiou, D., & Spantidi, O. (2018). The effects of appearance and motion of virtual characters on emotional reactivity. Computers in Human Behavior, 86, 99–108. https://doi.org/10.1016/j.chb.2018.04.036
Murillo-Muñoz, F., Navarro-Cota, C., Juárez-Ramírez, R., Jiménez, S., Nieto Hipólito, J. I., Molina, A. I., & Vazquez-Briseno, M. (2021). Characteristics of a persuasive educational system: A systematic literature review. Applied Sciences (Switzerland), 11(21). https://doi.org/10.3390/app112110089
O’Brien, H. L., Cairns, P., & Hall, M. (2018). A practical approach to measuring user engagement with the refined user engagement scale (UES) and new UES short form. International Journal of Human Computer Studies, 112(December 2017), 28–39. https://doi.org/10.1016/j.ijhcs.2018.01.004
Oduor, M., & Oinas-Kukkonen, H. (2021). Committing to change: a persuasive systems design analysis of user commitments for a behaviour change support system. Behaviour and Information Technology, 40(1), 20–38. https://doi.org/10.1080/0144929X.2019.1598495
Ohliati, J., & Abbas, B. S. (2019). Measuring students satisfaction in using learning management system. International Journal of Emerging Technologies in Learning, 14(4), 180–189. https://doi.org/10.3991/ijet.v14.i04.9427
Oinas-Kukkonen, H. (2013). A foundation for the study of behavior change support systems. Personal and Ubiquitous Computing, 17(6), 1223–1235. https://doi.org/10.1007/s00779-012-0591-5
Oinas-Kukkonen, H., & Harjumaa, M. (2009a). Persuasive systems design: Key issues, process model, and system features. Communications of the Association for Information Systems, 24(1), 485–500. https://doi.org/10.17705/1cais.02428
Oinas-Kukkonen, H., & Harjumaa, M. (2009b). Persuasive systems design: Key issues, process model, and system features. Communications of the Association for Information Systems, 24(1), 485–500. https://doi.org/10.17705/1cais.02428
Orji, F. A., Greer, J., & Vassileva, J. (2019). Exploring the effectiveness of socially-oriented persuasive strategies in education. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11433 LNCS(June), 297–309. https://doi.org/10.1007/978-3-030-17287-9_24
Radianti, J., Majchrzak, T. A., Fromm, J., & Wohlgenannt, I. (2020). A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Computers and Education, 147(December 2019), 103778. https://doi.org/10.1016/j.compedu.2019.103778
Ramírez-Correa, P. E., Rondán-Cataluña, F. J., & Arenas-Gaitán, J. (2018). Student information system satisfaction in higher education: the role of visual aesthetics. Kybernetes, 47(8), 1604–1622. https://doi.org/10.1108/K-08-2017-0297
Reeve, J., Cheon, S. H., & Jang, H. (2020). How and why students make academic progress: Reconceptualizing the student engagement construct to increase its explanatory power. Contemporary Educational Psychology, 62(July), 101899. https://doi.org/10.1016/j.cedpsych.2020.101899
Ryan, E., & Poole, C. (2019). Impact of Virtual Learning Environment on Students’ Satisfaction, Engagement, Recall, and Retention. Journal of Medical Imaging and Radiation Sciences, 50(3), 408–415. https://doi.org/10.1016/j.jmir.2019.04.005
Ryan, R. M., & Deci, E. L. (2000). Self-Determination Theory and the Facilitation if Intrinsic Motivation, Social Development, and Well-Being. American Psychologist, 55(1), 68–78. https://doi.org/https://doi.org/10.1037/0003-066X.55.1.68
Salimon, M. G., Sanuri, S. M. M., Aliyu, O. A., Perumal, S., & Yusr, M. M. (2021). E-learning satisfaction and retention: a concurrent perspective of cognitive absorption, perceived social presence and technology acceptance model. Journal of Systems and Information Technology, 23(1), 109–129. https://doi.org/10.1108/JSIT-02-2020-0029
Servotte, J. C., Goosse, M., Campbell, S. H., Dardenne, N., Pilote, B., Simoneau, I. L., Guillaume, M., Bragard, I., & Ghuysen, A. (2020). Virtual Reality Experience: Immersion, Sense of Presence, and Cybersickness. Clinical Simulation in Nursing, 38, 35–43. https://doi.org/10.1016/j.ecns.2019.09.006
Shmueli, G., Ray, S., Velasquez Estrada, J. M., & Chatla, S. B. (2016). The elephant in the room: Predictive performance of PLS models. In Journal of Business Research (Vol. 69, Issue 10, pp. 4552–4564). https://doi.org/10.1016/j.jbusres.2016.03.049
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189
Su, C.-H., & Cheng, C.-H. (2015). A mobile gamification learning system for improving the learning motivation and achievements. Journal of Computer Assisted Learning, 31(3), 268–286. https://doi.org/10.1111/jcal.12088
Tikka, P., & Oinas-Kukkonen, H. (2016). RightOnTime: The role of timing and unobtrusiveness in behavior change support systems. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9638(April 2016), 327–338. https://doi.org/10.1007/978-3-319-31510-2_28
Vesga, J. B., Xu, X., & He, H. (2021). The effects of cognitive load on engagement in a virtual reality learning environment. Proceedings - 2021 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2021, 645–652. https://doi.org/10.1109/VR50410.2021.00090
Vesisenaho, M., Juntunen, M., Häkkinen, P., Pöysä-Tarhonen, J., Fagerlund, J., Miakush, I., & Parviainen, T. (2019). Virtual Reality in Education: Focus on the Role of Emotions and Physiological Reactivity. Journal For Virtual Worlds Research, 12(1). https://doi.org/10.4101/jvwr.v12i1.7329
Walkington, C. A. (2013). Using adaptive learning technologies to personalize instruction to student interests: The impact of relevant contexts on performance and learning outcomes. Journal of Educational Psychology, 105(4), 932–945. https://doi.org/10.1037/a0031882
Wang, R., Bush-Evans, R., Arden-Close, E., Bolat, E., McAlaney, J., Hodge, S., Thomas, S., & Phalp, K. (2023). Transparency in persuasive technology, immersive technology, and online marketing: Facilitating users’ informed decision making and practical implications. Computers in Human Behavior, 139(July 2022), 107545. https://doi.org/10.1016/j.chb.2022.107545
Wiafe, I., Koranteng, F. N., Kastriku, F. A., & Gyamera, G. O. (2022). Assessing the impact of persuasive features on user’s intention to continuous use: the case of academic social networking sites. Behaviour and Information Technology, 41(4), 712–730. https://doi.org/10.1080/0144929X.2020.1832146
Wiafe, I., Koranteng, F. N., Owusu, E., Ekpezu, A. O., & Gyamfi, S. A. (2020). Persuasive social features that promote knowledge sharing among tertiary students on social networking sites: An empirical study. Journal of Computer Assisted Learning, 36(5), 636–645. https://doi.org/10.1111/jcal.12433
Wu, Y.-C., Hsieh, L.-F., & Lu, J.-J. (2015). What’s The Relationship between Learning Satisfaction and Continuing Learning Intention? Procedia - Social and Behavioral Sciences, 191, 2849–2854. https://doi.org/10.1016/j.sbspro.2015.04.148
Xiao, M., Tian, Z., & Xu, W. (2023). Impact of teacher-student interaction on students’ classroom well-being under online education environment. Education and Information Technologies, 28(11), 14669–14691. https://doi.org/10.1007/s10639-023-11681-0
Yang, H., Cai, M., Diao, Y., Liu, R., Liu, L., & Xiang, Q. (2023). How does interactive virtual reality enhance learning outcomes via emotional experiences? A structural equation modeling approach. Frontiers in Psychology, 13(January), 1–16. https://doi.org/10.3389/fpsyg.2022.1081372
Yin, X., Zhang, J., Li, G., & Luo, H. (2024). Understanding Learner Satisfaction in Virtual Learning Environments: Serial Mediation Effects of Cognitive and Social-Emotional Factors. Electronics (Switzerland), 13(12). https://doi.org/10.3390/electronics13122277
Zeng, Y., Zhang, W., Wei, J., & Zhang, W. (2023). The association between online class-related enjoyment and academic achievement of college students: a multi-chain mediating model. BMC Psychology, 11(1), 1–12. https://doi.org/10.1186/s40359-023-01390-1 University Staff: Request a correction | Centaur Editors: Update this record |
University of Reading
CentAUR: Central Archive at the University of Reading
Accessibility navigation
Learning satisfaction in virtual reality: the role of persuasive design
Wiafe, I., Ekpezu, A. O., Gyamera, G. O., Winful, F. B. P., Atsakpo, E. D., Nutropkor, C. and Gulliver, S. R.
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1080/10447318.2024.2440205 Abstract/SummaryGiven the positive impact of virtual reality learning environments on students’ learning satisfaction, it is imperative to identify the key features within these environments that contribute to such satisfaction. This study examined how persuasive features enhance students’ learning satisfaction via psychological outcomes within a persuasive immersive virtual reality learning environment (IVRLE). Using partial least squares structural equation modeling, quantitative data obtained from 115 IVRLE users were analyzed. The results show that by leveraging persuasive features such as unobtrusiveness, design aesthetics, primary task support, credibility support, dialogue support, and perceived persuasiveness, educators can create immersive learning environments that effectively engage students cognitively and emotionally, thereby enhancing learning satisfaction. Among the direct determinants of students’ learning satisfaction, perceived enjoyment exhibited the strongest impact. These results underscore the relevance of designing virtual reality learning environments as persuasive educational environments that shape learning behaviors and also caters to the psychological needs of students.
Altmetric Deposit Details References University Staff: Request a correction | Centaur Editors: Update this record |
Lists
Lists
![[img]](/style/images/fileicons/text.png)