Back to Search Start Over

Gamified online survey to elicit citizens' preferences and enhance learning for environmental decisions.

Authors :
Aubert, Alice H.
Lienert, Judit
Source :
Environmental Modelling & Software. Jan2019, Vol. 111, p1-12. 12p.
Publication Year :
2019

Abstract

Abstract Multi-Criteria Decision Analysis (MCDA) requires a critical step, namely to elicit individual preferences. On the basis of learning theories, we formalize preference construction as learning about facts and values, and as a process; we also conceptualize an online preference elicitation survey that offers learning loops to increase factual learning and support preference construction. Another originality is gamification. Game elements (a narrative and non-player characters as motivational affordance) keep respondents engaged in the demanding task of weight elicitation. Our tool enables broad public participation in MCDA, allowing reliable online preference elicitation. The survey concept was tested with 107 students and a control treatment. Quantitative and qualitative data indicate that the concept works. Participants' factual knowledge increased. The survey helped students to learn about their own preferences concerning the importance of objectives. The practical implication is that weighting can be reliably elicited by online surveys. Participants reported a positive experience; further ways to improve it are thoroughly discussed. Graphical abstract Image 1 Highlights • Proof of concept of a gamified online weight elicitation survey for MCDA. • Two-level learning loops enable factual learning and preference construction. • Survey positively perceived, particularly with story and non-player characters. • Suggestions to improve engagement in the gamified survey are discussed. • Survey to enhance citizen participation in complex environmental decision-making. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13648152
Volume :
111
Database :
Academic Search Index
Journal :
Environmental Modelling & Software
Publication Type :
Academic Journal
Accession number :
133257704
Full Text :
https://doi.org/10.1016/j.envsoft.2018.09.013