An Adaptive Virtual Reality Game for Programming Education Using Fuzzy Cognitive Maps and Pedagogical Models
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| Title: | An Adaptive Virtual Reality Game for Programming Education Using Fuzzy Cognitive Maps and Pedagogical Models |
|---|---|
| Language: | English |
| Authors: | Andreas Marougkas, Christos Troussas (ORCID |
| Source: | Smart Learning Environments. 2025 12. |
| Availability: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 60 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Computer Simulation, Computer Games, Educational Games, Computer Science Education, Programming, Cognitive Mapping, Models, Electronic Learning, Gamification, Learner Engagement, Learning Motivation, Task Analysis |
| DOI: | 10.1186/s40561-025-00392-3 |
| ISSN: | 2196-7091 |
| Abstract: | Virtual Reality has proven to be highly promising within the field of learning. Most VR learning methods do not effectively implement pedagogical models or adapt to the individual's learning style. This research aims to bridge this gap by integrating Fuzzy Cognitive Maps (FCMs), Flow Theory and Gamification within an educational Virtual Reality video game to introduce and teach learners to Java programming. This new integration offers real-time accommodation to the learners' performance through dynamically balancing challenges and competencies (Flow Theory) and personalized, data-driven feedback (FCMs) and motivational stimulation through interactive gamified mechanisms (Gamification). With the use of FCMs to enable real-time personalization, this approach offers the ideal balance between competence and challenge to ensure deeper understanding of the subject matter. A comprehensive analysis verified significant improvements to the task performance, knowledge outcomes, along with the reduction of errors, validating the effectiveness of this adaptive VR method. The future of a more efficient and adaptive learning VR is made possible through this research that offers new knowledge about the integration of cognitive engagement, motivational aspects, and adaptive AI-powered learning. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1490244 |
| Database: | ERIC |
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