- J. Tuomas Harviainen, Timo Lainema, Jaakko Suominen, and Erno Soinila. Development of a Finnish Community of Game Scholars (available free, DOI=10.1177/1046878113513533)
- Kimmo Oksanen. Subjective Experience and Sociability in a Collaborative Serious Game
- Janne Paavilainen, Juho Hamari, Jaakko Stenros, and Jani Kinnunen. Social Network Games: Players’ Perspectives
- Carolina Islas Sedano, Verona Leendertz, Mikko Vinni, Erkki Sutinen, and Suria Ellis. Hypercontextualized Learning Games: Fantasy, Motivation, and Engagement in Reality
- Lauri-Matti Palmunen, Elina Pelto, Anni Paalumäki, and Timo Lainema.
Formation of Novice Business Students’ Mental Models Through Simulation Gaming
- Simo Järvelä, J. Matias Kivikangas, Jari Kätsyri, and Niklas Ravaja. Physiological Linkage of Dyadic Gaming Experience
- Benjamin Cowley, Ilkka Kosunen, Petri Lankoski, J. Matias Kivikangas, Simo Järvelä, Inger Ekman, Jaakko Kemppainen, and Niklas Ravaja. Experience Assessment and Design in the Analysis of Gameplay
- Hanna Wirman. Gender and Identity in Game-Modifying Communities
Experience Assessment and Design in the Analysis of Gameplay is available in Simulation and Gaming (online first version).
We report research on player modeling using psychophysiology and machine learning, conducted through interdisciplinary collaboration between researchers of computer science, psychology, and game design at Aalto University, Helsinki. First, we propose the Play Patterns And eXperience (PPAX) framework to connect three levels of game experience that previously had remained largely unconnected: game design patterns, the interplay of game context with player personality or tendencies, and state-of-the-art measures of experience (both subjective and non-subjective). Second, we describe our methodology for using machine learning to categorize game events to reveal corresponding patterns, culminating in an example experiment. We discuss the relation between automatically detected event clusters and game design patterns, and provide indications on how to incorporate personality profiles of players in the analysis. This novel interdisciplinary collaboration combines basic psychophysiology research with game design patterns and machine learning, and generates new knowledge about the interplay between game experience and design.
Keywords: game design, gameplay patterns, psychophysiology, personality profiles, PPAX framework.
- Cowley, Kosunen, Lankoski, Kivikangas, Järvelä, Ekman, Kemppainen, Ravaja, forthcoming. Experience Assessment and Design in the Analysis of Gameplay. Simulation and Gaming. DOI=10.1177/1046878113513936
The figures from the poster Modeling Player-character Engagement in Single-player Character-Driven Games in ACE Netherlands (2013):
Below is link to the data file and R code used to in the final models in “Models for Story Consistency and Interestingness in Single-Player RPGs” (in Mindtrek 2013) and “Modeling Player-character engagement in Single-player character-driven games” (in ACE 2013 Netherlands). The models q4 and q7 are used in the first paper and and the model q8 is used in the second paper.
In ACE 2013 Netherlands, pp. 572-575. Copyright Springer 2013. This is author’s version. The definitive version DOI: 10.1007/978-3-319-03161-3_56.
This pilot study looks at how the formal features of character-driven games can be used to explain player-character engagement. Questionnaire data (N=206), formal game features (in 11 games), and ordinal regression were used in the analysis. The results show that interactive dialogue and cut-scenes showing the romances between the player-character and another character relates to higher character engagement scores, while romance modeling and friendship modeling relate to lower character engagement scores.
Keywords: ordinal regression, player-character, engagement, identification
A free R book:
Note for myself. Check this:
Nakagawa, S. & Schielzeth, H., 2013, A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4: 2, pp 133–142, DOI: 10.1111/j.2041-210x.2012.00261.x
R implementation is available: http://jslefche.wordpress.com/2013/03/13/r2-for-linear-mixed-effects-models/
Also MuMIn package contains implementation (http://cran.r-project.org/web/packages/MuMIn/index.html)