SelVReflect: A Guided VR Experience Fostering Reflection on Personal Challenges

Publication date

2023-04-19

Authors

Wagener, Nadine
Reicherts, Leon
Zargham, Nima
Bartłomiejczyk, Natalia
Scott, Ava Elizabeth
Wang, Katherine
Bentvelzen, MaritISNI 0000000506321945
Stefanidi, Evropi
Mildner, Thomas
Rogers, Yvonne

Editors

Schmidt, Albrecht
Väänänen, Kaisa

Advisors

Supervisors

Document Type

Part of book
Open Access logo

License

Abstract

Reflecting on personal challenges can be difficult. Without encouragement, the reflection process often remains superficial, thus inhibiting deeper understanding and learning from past experiences. To allow people to immerse themselves in and deeply reflect on past challenges, we developed SelVReflect, a VR experience which offers active voice-based guidance and a space to freely express oneself. SelVReflect was developed in an iterative design process (N=5) and evaluated in a user study with N=20 participants. We found that SelVReflect enabled participants to approach their challenge and its (emotional) components from different perspectives and to discover new relationships between these components. By making use of the spatial possibilities in VR, participants developed a better understanding of the situation and of themselves. We contribute empirical evidence of how a guided VR experience can support reflection. We discuss opportunities and design requirements for guided VR experiences that aim to foster deeper reflection.

Keywords

Creativity, Emotion, Expression, Guidance, Reflection, Self-care, Virtual Reality, Well-being, Software, Human-Computer Interaction, Computer Graphics and Computer-Aided Design

Citation

Wagener, N, Reicherts, L, Zargham, N, Bartłomiejczyk, N, Scott, A E, Wang, K, Bentvelzen, M, Stefanidi, E, Mildner, T, Rogers, Y & Niess, J 2023, SelVReflect: A Guided VR Experience Fostering Reflection on Personal Challenges. in A Schmidt & K Väänänen (eds), CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems., 323, Association for Computing Machinery, pp. 1-17. https://doi.org/10.1145/3544548.3580763