Virtual Reality Educational Institutions Evaluation: Evidential Reasoning Based on MAGDM Model Integrating Trust Network and Three-Dimensional Emotion

Abstract

Virtual reality (VR)-based education has gradually emerged as a significant trend in future educational development, as VR technology can concretize and visualize various abstract concepts in teaching. Given the importance of evaluating VR educational institutions to promote VR education, this study proposes a multi-attribute group decision-making model that integrates trust networks and three-dimensional emotions within an interval-valued Pythagorean fuzzy set (IVPFS) environment. First, considering the decision-makers’ affective cognition, a three-dimensional emotion model is incorporated into the IVPFS framework. Second, the Beta function is employed to describe changes in the trust relationships pre- and post-decision-making, thereby adjusting the weights of the decision-makers. Furthermore, the extended evidential reasoning (ER) method is utilized to rank and select VR educational institutions. The results indicate that VRSCHOOL (0.6338) outperforms VISTANDARD (0.6214), GROWLIB (0.5834), and STARUR (0.5738). Finally, the robustness and effectiveness of the proposed method are validated through sensitivity and comparative analyses. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2025.

Keywords

Author Keywords

Emotional model Evidential reasoning method Multi-attribute group decision making Trust networks VR educational institutions

Index Keywords

E-learning Engineering education Decision making Behavioral research Virtual reality Educational institutions Fuzzy sets Emotional models Evidential reasoning Evidential reasoning method Group Decision Making Multi-attribute group decision making Multi-attributes Reasoning methods Trust networks Virtual reality educational institution

Author Affiliations

College of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, Zhejiang, China, Economic Forecasting and Policy Simulation Laboratory, Zhejiang Gongshang University, Hangzhou, Zhejiang, China

College of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, Zhejiang, China

Funding & Acknowledgements

National Social Science Fund of China, NSSFC

Grant: 20CTJ016

This work was supported by the project of the National Social Science Fund of China (20CTJ016), Zhejiang Gongshang University \u201CDigital\u201D Disciplinary Construction Management Project (No. SZJ2022A007).

National Social Science Fund of China, NSSFC

This work was supported by the project of the National Social Science Fund of China (20CTJ016), Zhejiang Gongshang University \u201CDigital\u201D Disciplinary Construction Management Project (No. SZJ2022A007).

Zhejiang Gongshang University, ZJSU

Grant: SZJ2022A007

This work was supported by the project of the National Social Science Fund of China (20CTJ016), Zhejiang Gongshang University \u201CDigital\u201D Disciplinary Construction Management Project (No. SZJ2022A007).

Zhejiang Gongshang University, ZJSU

This work was supported by the project of the National Social Science Fund of China (20CTJ016), Zhejiang Gongshang University \u201CDigital\u201D Disciplinary Construction Management Project (No. SZJ2022A007).

References 10 References

1 Araiza-Alba, Paola, Are we ready for virtual reality in K–12 classrooms?, Technology, Pedagogy and Education, 31, 4, pp. 471-491, (2022)

2 Campbell, Laurie O., An exploration of factors that predict higher education faculty members’ intentions to utilize emerging technologies, Educational Technology Research and Development, 72, 2, pp. 643-659, (2024)

3 Caratachea, Matthew X., Making in virtual reality environments: a case study of K-12 teachers’ perceptions on the educational affordances of virtual reality for maker-centered learning, Educational Technology Research and Development, 72, 1, pp. 155-180, (2024)

4 Chen, Ji, Forecasting tourism demand with search engine data: A hybrid CNN-BiLSTM model based on Boruta feature selection, Information Processing and Management, 61, 3, (2024)

5 Chen, Shyiming, Multiple attribute decision making using Beta distribution of intervals, expected values of intervals, and new score function of interval-valued intuitionistic fuzzy values, Information Sciences, 579, pp. 863-887, (2021)

6 Chen, Ting-Yu, A comparative analysis of score functions for multiple criteria decision making in intuitionistic fuzzy settings, Information Sciences, 181, 17, pp. 3652-3676, (2011)

7 Clore, Gerald L., Psychological construction in the OCC model of emotion, Emotion Review, 5, 4, pp. 335-343, (2013)

8 Cohen, Arthur Clifford, On Estimating the Mean and Standard Deviation of Truncated Normal Distributions, Journal of the American Statistical Association, 44, 248, pp. 518-525, (1949)

9 Dincelli, Ersin, Immersive virtual reality in the age of the Metaverse: A hybrid-narrative review based on the technology affordance perspective, Journal of Strategic Information Systems, 31, 2, (2022)

10 Dong, Yucheng, The OWA-based consensus operator under linguistic representation models using position indexes, European Journal of Operational Research, 203, 2, pp. 455-463, (2010)

Virtual Reality Educational Institutions Evaluation: Evidential Reasoning Based on MAGDM Model Integrating Trust Network and Three-Dimensional Emotion

How to Cite