Wallner, G., and S. Kriglstein. “Visualization-Based Analysis of Gameplay Data-A Review of Literature.” Entertainment Computing (2013).
This paper reviews literature on visualization-based analysis of game metric data in order to give an overview of the current state of this field of research. Exactly, this paper reviews and classifies the visualization methods for game play metrics. Gameplay metrics are automatically collected by logging user initiated events (events that occur when a player interacts with a game). Furthermore, Gameplay metrics are numerical data about players’ behavior and interactions with the game and have become a valuable source for the analysis of how a game is played.
The paper first introduced application areas of visualization methods and then its classification of these visualization methods based on different aspects. Finally authors discussed their results of their review.
The authors classified the visualization methods for gameplay metrics, which intended for the analysis of data internal to gameplay sessions. Table 1 summarizes this classification.
Table 1-Summary of the paper classification
The Unreal Master Control Program
Flying Lab Software
||Rockstar Games Social Club
|Field of application
||Lithium (for Return to Castle Wolfenstein: Enemy Territory game)
Data Cracker(as a Dead Space 2 tool)
Tracking Real-Time User Experience (TRUE)
Microsoft Game Studios
Age of Empires II
Dragon Age: Origins
Sony Online Entertainment
Unreal Master Control Program.
||Charts and diagrams
Half Life: Episode 2
Psychostats (for Half-Life 1 and 2
Tomb Raider: Underworld
World of Warcraft
World of Warcraft
Tomb Raider: Underworld
In the discussion part of the paper, at first, authors mentioned some practical problems in collecting and analyzing gameplay metrics data. They also discussed some suggestion about how we should overcome the existing issues in data collection and analyzing. Then, they shortly discussed open problems and future directions for research which mainly evolve around six broad areas. Table 2 briefly mentioned these areas.
Table 2-Future direction and research
||Which data should be tracked and how it should be analyzed
Statistical techniques or aggregated data
Clustering, SOMs or graphs for data with unknown patterns
Frameworks or heuristics
Transforming, cleaning, analyzing and visualizing the very large amount of data
Ways to reduce the visual complexity of the visualizations
|Using clustering in visualization
||Misinterpretations of the data
Including contextual data
Being combined with user research methods like playtesting, video capture, or thinking aloud protocols
||Automatic detection of patterns in the data to assist human analysis
Landmarks in virtual environments
Automatic identification of roles that avatars take in a group
The development of unsupervised and supervised machine-learning systems
||Effective game analytical tools
||Integrating game analytical tools in the development process
||Understanding causal relationships to enhance playability
To be honest, I didn’t like the paper. Exactly the topic of the paper is really boring. Reading such a tedious paper took lots of time of me, and killed me. However, in the technical view, the paper is written well and includes what it should. The authors mentioned that they reviewed 42 papers in this context, but they provided lots of other examples and references. The number of references, 130 papers, shows their deep analysis.
Yannakakis, Georgios N., and John Hallam. “Entertainment modeling through physiology in physical play.” International Journal of Human-Computer Studies 66.10 (2008): 741-755.
The goal of this paper was to provide an entertainment children-user model which predicts fun when children play a physical game. This entertainment model would predict the fun, by analyzing physiological measures.
They had two challenges to overcome for creating this model. The first challenge was how can we create this model, I mean using which existing technique? And the second challenge was how can we eliminate the impact of physical activities from captured physiological measures (I mean a physical activity and a physical game have some common features that are noises in prediction model)?
To producing this model, the authors designed two experiments, and used some feature selection algorithms and classification mechanisms. In the other words, they used and compared two feature extraction algorithms and three classification algorithms.
At the end, an entertainment model generates a number y that shows how much “fun” is it. For example in comparison of two games, the more fun game gets the higher value.
Main Experiment (the first experiment)
Seventy two normal-weighted children whose ages cover a range between 8 and 10 years participated in the main experiment. They played Bug-Smasher in nine variants on the Playware playground. The description of Bug-Smasher game is as follows:
The “Bug-Smasher” game is used as Continue reading
Mandryk, R. L., Inkpen, K. M., & Calvert, T. W. (2006). Using psychophysiological techniques to measure user experience with entertainment technologies. Behaviour & Information Technology, 25(2), 141-158.
Image from http://www.jingyif.com/dux/darpa-web-experience-for-kids/
This paper describes two experiments designed to test the efficacy of physiological measures as evaluators of user experience with entertainment technologies. The authors described two experiments that were designed to test the two main conjectures:
- Physiological measures can be used to measure a player’s experience with entertainment technology.
- Normalized physiological measures of experience with entertainment technology will correspond to subjective reports. Continue reading
Elizabeth A. Boyle, Thomas M. Connolly, Thomas Hainey, James M. Boyle, Engagement in digital entertainment games: A systematic review, Computers in Human Behavior, Volume 28, Issue 3, May 2012, Pages 771-780, ISSN 0747-5632, 10.1016/j.chb.2011.11.020.
Keywords: Engagement; Enjoyment; Entertainment; Computer games; Flow; Motives
The aim of this paper is to advance the understanding of the engagement in computer games by reporting a review of recent literature about this topic. At first, the paper reports how the authors had collected and classified the papers. Then, the authors mention their results of their work. In the result part, the authors again review their works in collecting, categorizing the referenced papers, and their research designs. After that, the authors systemically study the previous works in six contexts:
- Subjective feelings of enjoyment exprienced during playing games
- Physiological responses to playing games
- Motives and reasons for playing games
- Games usage
- Game market and player loyalty
- Impact of game-playing on life satisfaction
The methodology that authors used is very interesting. They carried out a literature search to develop a searchable database of papers since 1961 to 2011 revelant to the impacts and outcomes of computer games. For this Continue reading
Image from magazine.liquida.it http://magazine.liquida.it/2012/07/25/playstation-network-offline-per-14-ore/
Elizabeth Boyle, Thomas M. Connolly, Thomas Hainey, The role of psychology in understanding the impact of computer games, Entertainment Computing, Volume 2, Issue 2, 2011, Pages 69-74, ISSN 1875-9521, 10.1016/j.entcom.2010.12.002.
Keywords: Computer games; Psychology; Engagement; Serious games; Learning
This paper reviews the role of psychology in understanding the impact of computer games. Authors study the role of theory constructs and research concepts in psychology in understanding the positive and negative impacts of computer games, attraction of games and the potential of serious games in learning, skill acquisitions and training. At first the paper reviews the impacts of computer games and how psychology theories have been used to explain the impacts, and then discuss about serious games and how psychology theories and analyses are applied to make this type of games more successful and affective to achieve to their goals.