What happened in Tampere?

After successfully organizing three GamifIR workshops in 2014, 2015 and 2016 the main conclusion, primarily motivated by Sebastian Deterdings keynote, of the last GamifIR 2016 was that we need to have more theory for Gamification design. “Without theory, we cannot ask the grand question, inviting a new science of design: “How do you design features to affect cognitive states, which than affect user behavior”?” (Gabriella Kazai, Informer). Consequently, this workshop aims to find AI and data-driven opportunities for building up and developing Gamification design theory.

Proceedings: http://ceur-ws.org/Vol-1978/

Workshop Activities

After a brief welcome and introductory recap of the last three GamifIR workshops we started the presentation and discussion session.

During and after the paper presentations we discussed different aspects of player types. For instance, we talked about how goals drive motivation and different user types have different goals. But there exist not only ten or 20 different types of goals, there are millions of goals and needs to be assigned to different types of player and user groups. It is also important to consider already existing incentives and rewards when interpreting behavior driven by gamification because there could always exist side effects by motivation outside the gamification application like bonus system in workplace environments. Thus, the environment or the context is very important for analysis.

Another aspect we discussed was that an application or system creates affordances. The gamified system facilitates need or goal fulfillment, but without the user having a congruent goal or need the system is not motivating, only through a combination of actual need and facilitated fulfillment of that need can motivation arise. Robin Brouwer underlined that he disbelieves in a basic set of game design elements that always works. Instead, you always need to design something in line with the context in which the game elements are placed. In order to optimize this interplay between context and design elements you need a designer for at least the initial design!

Furthermore, we had a discussion on the necessity of pre-development insights about intended users for the gamification design or if it is possible to assign a set of game design elements based on users behavior data maybe after a short machine learning phase. This resulted in a discussion about how to detect engagement drop-offs by specific player or user types to create affordances to re-engage them. Maybe different phases of user engagement and user experience exists and it would be very interesting to know how much exist and whether we could detect them automatically?

Conclusion

We concluded that time or timing is very important for successful gamified systems but it is hard to detect and implement the right user journey or user phases and behavior sections: Do the right at the right time!

It is not clear if we need player types as a gamification design starting point or not. We had different opinions and long discussions about this. Another approach could be to just ask the user about her contexts and goals (inside the application) and later target on different types and moods. We agreed that we need user feedback for evaluation of different machine learning approaches. This could be general ratings by the users or deduced ratings on the gamified application.

However, to be able to classify the findings in data-driven gamification design we need to develop objective measures of success, like the level of gameful experience (emotion, immersion, well-being, etc.), to evaluate data-driven gamification design. Data-Driven Gamification Design should provide more insights on the different actual behavior patterns of different player types maybe without knowing or naming the types. Beyond that it would be interesting to compare actual behavior of different user types to theoretically intended behavior of self-assigned types e.g. within a player type tests. Another important dimension additionally to the player type dimension might be the behavior change on different time phases.

For another workshop on DDGD we would expect submission on research result about data-driven generated player types, adapting challenge level, different user phase detection and first insight on adapting a gamification design automatically.

Accepted Papers

Proceedings: http://ceur-ws.org/Vol-1978/

Each submitted paper has been peer-reviewed by three members of the programme committee consisting of experts drawn from different communities guaranteeing a mix of industrial and academic backgrounds. Accepted papers include:

AuthorTitle
Robin Brouwer and Kieran Conboy A Theoretical Perspective on the Inner workings of Gamification in the Workplace
Md Sanaul Haque, Timo Jämsä and Maarit Kangas A Theory-Driven System Model to Promote Physical Activity in the Working Environment with a Persuasive and Gamified Application
Sami Hyrynsalmi, Kai Kimppa, Jani Koskinen, Jouni Smed and Sonja Hyrynsalmi The Shades of Grey: Datenherrschaft in Data-Driven Gamification
Michael Meder, Till Plumbaum and Sahin Albayrak A Primer on Data Driven Gamification Design
Marigo Raftopoulos Data-Driven Gamification Design: An Enterprise Systems Perspective from the Front Line
Dorina Rajanen and Mikko Rajanen Personalized Gamification: A Model for Play Data Profiling

Pictures