The Gamification Design Problem
This addresses the challenge of optimizing gamification design for diverse user types to achieve specific goals, though it appears incremental by applying existing machine learning methods to this domain.
The authors tackled the problem of designing gamification by framing it as assigning game design elements to users to maximize their expected contribution toward a goal, showing it reduces to a statistical learning problem and suggesting matrix factorization as a solution when user interaction data is available.
Under the assumptions that (i) gamification consists of various types of users that experience game design elements differently; and (ii) gamification is deployed in order to achieve some goal in the broadest sense, we pose the gamification problem as that of assigning each user a game design element that maximizes their expected contribution in order to achieve that goal. We show that this problem reduces to a statistical learning problem and suggest matrix factorization as one solution when user interaction data is given. The hypothesis is that predictive models as intelligent tools for supporting users in decision-making may also have potential to support the design process in gamification.