Predicting user types with symbolic images: An empirical validation based on two card-sorting studies

Abstract

In gamification, personalization is considered an important field of research since it can improve user engagement and motivation. Particularly, effective gamified learning needs to strike a balance between gameful design that promotes engagement and does not negatively affect content comprehension and absorption. The existing traditional user-type questionnaires approaches are time-consuming and intrusive, hampering the user’s focus. Previous research in gamification demonstrated how to automate player profile creation through log file analysis of gameful systems instead of traditional approaches, however, psychological archetypes and motivational groups can simplify the process of collecting data to achieve a balanced level of personalization for gamified systems, which are grounded on the user’s profile and preferences which can help with their immersion. To attend to the needs of a wide range of fields that introduce gamification to systems that are not classified as games, a new ludic approach was created to define user types based on user interaction using symbolic images. In this exploratory research, we tested this approach in two card-sorting studies (N=35 and N=19). Our results show that the images can be used to predict the users’ archetypes and motivational groups accurately. Thus, we contribute with validating an image-based user type classification method that is easier to deploy in systems that do not aim to behave as a game but are looking to reap the benefits of personalizing a user’s content and gameful experience where relevant. Our second contribution is delivering guidelines to replicate this validation method to other existing approaches. We expect that with this guideline, we facilitate personalization with other user typologies without disrupting the gameful experience.