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https://ceur-ws.org/Vol-1978/preface.pdf
First International Workshop on Data-Driven
Gamification Design (DDGD2017)
Michael Meder Amon Rapp Till Plumbaum
Technische Universität Berlin University of Torino Technische Universität Berlin
Berlin, Germany Torino, Italy Berlin, Germany
meder@dai-labor.de amon.rapp@gmail.com till@dai-labor.de
Frank Hopfgartner
University of Glasgow
Glasgow, United Kingdom
frank.hopfgartner@glasgow.ac.uk
1 Introduction to gamification designers and practitioners.
1
Three editions of the GamifIR workshop have shown • Personalized gamified systems that exploit physi-
that a better theoretical underpinning of gamifica- ological, psychological, environmental, emotional
tion design is necessary to advance the state of the and social data to provide tailored game elements
art. This was primarily motivated by Sebastian De- to users with different characteristics.
terding’s keynote[1] and accepted papers at the last
GamifIR[2] in 2016. This workshop aims to find AI • Domain-dependent gamified services and applica-
and data-driven opportunities for building up and de- tions addressed to contexts like health, learning,
veloping gamification design theory. It took place on workplace, security, crowdsourcing, and so on.
20 September 2017 in conjunction with the Mindtrek
2017 conference in Tampere, Finland [3]. Six full pa- • Field evaluations of gamified systems in specific
pers were selected by the programme committee from contexts of use, and new techniques to envision,
a total of eight submissions. design and assess gamification design techniques.
2 Workshop Goals • Theoretical reflections and ethical considerations
on the future of gamification enabled by the in-
The call for papers solicited submissions of position pa- creasing availability of data.
pers as well as novel research papers addressing prob-
lems related to data-driven gamification design includ-
Each submitted paper has been peer-reviewed by
ing topics such as:
three members of the programme committee consisting
• Gamified systems that exploit data mining, ma- of experts drawn from different communities guaran-
chine learning and AI techniques. teeing a mix of industrial and academic backgrounds.
Accepted papers include:
• Insights on game design elements built upon em-
pirical data that can expand the catalog available • Robin Brouwer and Kieran Conboy. A Theoreti-
cal Perspective on the Inner workings of Gamifi-
Copyright c by the paper’s authors. Copying permitted for cation in the Workplace.
private and academic purposes.
In: M. Meder, A. Rapp, T. Plumbaum, and F. Hopfgartner • Md Sanaul Haque, Timo Jämsä and Maarit Kan-
(eds.): Proceedings of the Data-Driven Gamification Design
Workshop, Tampere, Finland, 20-September-2017, published at
gas. A Theory-Driven System Model to Promote
http://ceur-ws.org Physical Activity in the Working Environment
1 GamifIR Workshops: http://gamifir.com/ with a Persuasive and Gamified Application.
• Sami Hyrynsalmi, Kai Kimppa, Jani Koskinen, of user engagement and user experience exists and it
Jouni Smed and Sonja Hyrynsalmi. The Shades would be very interesting to know how much exist and
of Grey: Datenherrschaft in Data-Driven Gamifi- whether we could detect them automatically?
cation.
4 Conclusion
• Michael Meder, Till Plumbaum and Sahin Al-
bayrak. A Primer on Data Driven Gamification We concluded that time or timing is very important
Design. for successful gamified systems but it is hard to detect
and implement the right user journey or user phases
• Marigo Raftopoulos. Data-Driven Gamification and behavior sections: Do the right at the right time!
Design: An Enterprise Systems Perspective from It is not clear if we need player types as a gamifica-
the Front Line. tion design starting point or not. We had different
opinions and long discussions about this. Another ap-
• Dorina Rajanen and Mikko Rajanen. Personal- proach could be to just ask the user about her contexts
ized Gamification: A Model for Play Data Profil- and goals (inside the application) and later target on
ing. different types and moods. We agreed that we need
user feedback for evaluation of different machine learn-
3 Workshop Activities ing approaches. This could be general ratings by the
After a brief welcome and introductory recap of the users or deduced ratings on the gamified application.
last three GamifIR workshops we started the presen- However, to be able to classify the findings in data-
tation and discussion session. During and after the driven gamification design we need to develop ob-
paper presentations we discussed different aspects of jective measures of success, like the level of game-
player types. For instance, we talked about how goals ful experience (emotion, immersion, well-being, etc.),
drive motivation and different user types have differ- to evaluate data-driven gamification design. Data-
ent goals. But there exist not only ten or 20 different Driven Gamification Design should provide more in-
types of goals, there are millions of goals and needs sights on the different actual behavior patterns of dif-
to be assigned to different types of player and user ferent player types maybe without knowing or nam-
groups. It is also important to consider already exist- ing the types. Beyond that it would be interesting to
ing incentives and rewards when interpreting behavior compare actual behavior of different user types to the-
driven by gamification because there could always ex- oretically intended behavior of self-assigned types e.g.
ist side effects by motivation outside the gamification within a player type tests. Another important dimen-
application like bonus system in workplace environ- sion additionally to the player type dimension might
ments. Thus, the environment or the context is very be the behavior change on different time phases.
important for analysis. For another workshop on DDGD we would expect
Another aspect we discussed was that an applica- submission on research result about data-driven gen-
tion or system creates affordances. The gamified sys- erated player types, adapting challenge level, different
tem facilitates need or goal fulfillment, but without the user phase detection and first insight on adapting a
user having a congruent goal or need the system is not gamification design automatically.
motivating, only through a combination of actual need
and facilitated fulfillment of that need can motivation 5 Acknowledgements
arise. Robin Brouwer underlined that he disbelieves We would like to thank Mindtrek [3] for hosting us. We
in a basic set of game design elements that always acknowledge the efforts of the programme committee,
works. Instead, you always need to design something namely:
in line with the context in which the game elements
are placed. In order to optimize this interplay between • Raian Ali, Bournemouth University (UK)
context and design elements you need a designer for at
least the initial design! • Jon Chamberlain, University of Essex (UK)
Furthermore, we had a discussion on the necessity
of pre-development insights about intended users for • Sebastian Deterding, University of York (UK)
the gamification design or if it is possible to assign a
• Udo Kruschwitz, University of Essex (UK)
set of game design elements based on users behavior
data maybe after a short machine learning phase. This • Elisa Mekler, Universität Basel (CH)
resulted in a discussion about how to detect engage-
ment drop-offs by specific player or user types to create • Ashok Ranchhod, University of Southampton
affordances to re-engage them. Maybe different phases (UK)
• Susanne Strahringer, TU Dresden (GER)
• Albert Weichselbraun, University of Applied Sci-
ences Chur (CH)
We thank all PC members and authors of submitted
papers for making DDGD 2017 possible.
References
[1] S. Deterding. Desperately seeking theory: Gam-
ification, design, data, machine learning, and the
promise of double loop learning systems. In Gam-
ifIR 2016 workshop co-located with SIGIR 2016,
page 1, 2016.
[2] M. Meder, F. Hopfgartner, G. Kazai, and U. Kr-
uschwitz. Gamifir 2016: SIGIR 2016 workshop on
gamification for information retrieval. SIGIR Fo-
rum, 50(2):47–50, 2016.
[3] M. Turunen, H. Väätäjä, J. Paavilainen, and
T. Olsson, editors. Proceedings of the 21st Interna-
tional Academic Mindtrek Conference, Academic-
Mindtrek 2017, Tampere, Finland, September 20 -
21, 2017. ACM, 2017.