29 Apr 2018
Figure: System and Experiment Structure (Palancin-Silva et al., 2018)
Over the winter I had the privilege to participated in a pilot project initiated my colleague Palacin-Silva to investigate the impact of gamification on user engagement in environmental sensing. We used a participatory sensing approach where citizens actively participate in monitoring their environment. Participatory sensing can be said to be a type of civic technology in the sense that it empowers citizens to actively participate, instead of being passive recipients of environmental data. In this case the participants monitored the thickness of lake ice in Lappeenranta, Finland, where there was a pre-existing community. This community for example maintains their section of the Lake Wiki.
In our approach, we created two applications for monitoring lake ice thickness, one with gamification and one without to specifically evaluate the impact of gamification. The gamified application had a statistically significant effect on user effectiveness, as measured by the number of interactions and new data inputted by the user compared to the time the application was open. Both groups found the application as usable and as satisfying to use.
We published the following design reflections to consider when implementing gamification in participatory sensing:
- support personalized notification triggers;
- support customizable challenges to avoid negative feedback (e.g. discouragement) triggered from the lack of achievement;
- support social interaction between users;
- allow users to explore submitted data;
- enhance indoor experiences;
- support interactive feedback.
See more details about our recommendations in pg. 5 of the paper.
An open access version of the paper is available at the ACM Digital Library.
In the upcoming, more advanced approach the civic engagement and civic technology aspects are deepened. In this upcoming SENSEI Project citizens are included in the co-design and co-creation. Again, all props to my colleague for initiating and leading the project.
Participatory sensing (PS) and citizen science hold promises for a genuinely interactive and inclusive citizen engagement in meaningful and sustained collection of data about social and environmental phenomena. Yet the underlying motivations for public engagement in PS remain still unclear particularly regarding the role of gamification, for which HCI research findings are often inconclusive. This paper reports the findings of an experimental study specifically designed to further understand the effects of gamification on citizen engagement. Our study involved the development and implementation of two versions (gamified and non-gamified) of a mobile application designed to capture lake ice coverage data in the sub-arctic region. Emerging findings indicate a statistically significant effect of gamification on participants’ engagement levels in PS. The motivation, approach and results of our study are outlined and implications of the findings for future PS design are reflected.
Palacin-Silva, M. V., Knutas, A., Ferrario, M. A., Porras, J., Ikonen, J., & Chea, C. (2018). The Role of Gamification in Participatory Environmental Sensing: A Study In the Wild. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM. (Open access at ACM)
18 Apr 2018
Figure: Collecting data with RuuviTag and receiving it with Node-RED
Projects with small embedded devices have become a lot more easy in recent years, thanks to affordable and easy Internet of Things devices and more accessible programming environments. Just one year ago I did a small, fun project with a friend after receiving a RuuviTag from Kickstarter. We measured sauna temperatures with the intention of getting an alarm when it was hot enough to enjoy. We used a Node-RED server running on a RaspberryPI device. From there we forwarded the data to the ThingSpeak service for web-based visualization. I should note that nowadays there are more visually impressive services available as well, such as Grafana. It’s open source, too.
I have done earlier projects with devices similar to Arduino and they involving a lot of C programming and soldering cables to ordinary sensor devices. They’re still available and more reliable in some sense, but for some purposes devices like RuuviTag just make everything so pain-free. You don’t always feel like wiring your apartment with new cabling and struggling with C code. Also, the visualization platforms available nowadays are just impressive.
11 Dec 2017
Figure: Topic modeling -based analysis of current application areas in gamification
Gamification, or the application of game elements in non-game environments, is nowadays an increasingly popular field of research. The number of yearly publications on the topic has grown overwhelmingly. In this paper, we mapped the publication trends in gamification, and analyzed which in which fields the application of gamification is most popular. Health, play, education, crowdsourcing, and software development were identified as the most trending topics.
The paper is available at ScienceDirect. Alternatively, you can request a preprint at ResearchGate.
The term gamification and gamified systems are a trending area of research. However, gamification can indicate several different things, such as applying the game-like elements into the design of the user interface of a software, but not all gamification is necessarily associated with software products. Overall, it is unclear what different aspects are studied under the umbrella of ‘gamification’, and what is the current state of the art in the gamification research. In this paper, 1164 gamification studies are analyzed and classified based on their focus areas and the research topics to establish what the research trends in gamification are. Based on the results, e-learning and proof-of-concept studies in the ecological lifestyle and sustainability, assisting computer science studies and improving motivation are the trendiest areas of gamification research. Currently, the most common types of research are the proof-of-concept studies, and theoretical works on the different concepts and elements of gamification.
Kasurinen, J., Knutas, A. (2018). Publication trends in gamification: A systematic mapping study. Computer Science Review, 27, 33-44. DOI: 10.1016/j.cosrev.2017.10.003
14 Oct 2017
Figure: Marczewski’s (2015) Gamification User Type Hexad
Gamification is a trending topic in both research and commercial applications. However, there has been uncertainity of when and where gamification is effective, and which approaches are suitable different users and environments. Recent studies have proposed that personalization is a key and gamification is not necessarily a one-size fits all method. In this study, we have used the gamification user type hexad (Marczewski, 2015; Tondello et al., 2016), van Roy’s (2017) heuristics for effective gamification design, and Deterding’s (2015) design lenses to create an adaptive gamification system for personalized gamification. The CN2 rule induction machine learning method was used to distill the expert panel created gamification ruleset into a decision-making algorithm. The gamification algorithm matches situations and user types with specific gamification challenges.
The application domain is a computer-supported collaborative learning environment, where software engineering students work together in teams. The aim of the system is to encourage beneficial interactions along the principles of self-determination theory (Deci & Ryan, 2012).
Read more at the CEUR Workshop Proceedings archive of the Proceedings of the 1st Workshop on Games-Human Interaction (GHITALY 2017) or the preprint at ResearchGate. Alternatively get an overview from the conference presentation slides.
The paper is freely available as open access. Additionally, project materials and source code are available both in GitHub, and permanently archived in Zenodo, the open access research data repository. See links for both below in the references section.
In this paper we present an approach for personalizing gamification to the needs of each individual person. We designed the personalization for computer-supported collaborative learning environments by synthesizing three existing design frameworks: the lens of intrinsic skill atoms, gamification user type hexad and heuristics for effective design of gamification. The result of the design process is a context-aware and personalized gamification ruleset for collaborative environments. We also present a method for translating gamification rulesets to machine-readable classifier algorithm using the CN2 rule inducer and a framework for connecting the produced algorithm to collaborative software. Lastly, we present an example software for personalized gamification that was produced by applying the process presented in this paper.
Knutas, A., van Roy, R., Hynninen, T., Granato, M., Kasurinen, J., & Ikonen, J. (2017). Profile-Based Algorithm for Personalized Gamification in Computer-Supported Collaborative Learning Environments. In Proceedings of the 1st Workshop on Games-Human Interaction (GHITALY 2017). (CEUR-WS | Preprint from ResearchGate)
Knutas, A., van Roy, R., Hynninen, T., Granato, M., Kasurinen, J., & Ikonen, J. (2017, September 18). Online Appendix for “Profile-Based Algorithm for Personalized Gamification in Computer-Supported Collaborative Learning Environments”. Zenodo. http://doi.org/10.5281/zenodo.827225
Deci, E. L., & Ryan, R. M. (2012). Motivation, personality, and development within embedded social contexts: An overview of self-determination theory. The Oxford Handbook of Human Motivation, 85–107.
Deterding, S. (2015). The Lens of Intrinsic Skill Atoms: A Method for Gameful Design. Human–Computer Interaction, 30(3–4), 294–335.
Marczewski, A. (2015). User Types. In Even Ninja Monkeys Like to Play: Gamification, Game Thinking and Motivational Design (1st ed., pp. 65-80). CreateSpace Independent Publishing Platform.
Tondello, G. F., Wehbe, R. R., Diamond, L., Busch, M., Marczewski, A., & Nacke, L. E. (2016). The Gamification User Types Hexad Scale. In Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play (pp. 229–243). New York, NY, USA: ACM.
Roy, R. van, & Zaman, B. (2017). Why Gamification Fails in Education and How to Make It Successful: Introducing Nine Gamification Heuristics Based on Self-Determination Theory. In M. Ma & A. Oikonomou (Eds.), Serious Games and Edutainment Applications (pp. 485–509). Springer International Publishing.
11 Sep 2017
Figure: Wearables Acceptance Model
Wearable devices are in the first big peak of the hype cycle. In the article “Intended use of smartwatches and pedometers in the university environment: an empirical analysis” my collague Jayden and the rest of the team investigate what motivates people to use them. We used his prototype wearable acceptance model (WAM) and partial-least squares path modeling to find causalities in people’s views and their intention to use wearables.
In this initial study we found that the following factors affect people’s decision to use wearables:
- Performance Expectancy, or the belief that the device will help the user achieve his or her daily goals.
- Social Influence, or peer pressure.
- Privacy Concerns.
- Wearability, or how the devices can be worn. By contrast, aesthetics or other physical characteristics did not affect intention to use.
Additionally, there was an interesting non-affecting factor: Effort Expectancy. Users’ beliefs about the ease of use did not have an impact on their intention to use.
The paper is available as a preprint at ResearchGate. Full paper metadata is available at ACM Digital Library.
In this work, we empirically examine factors that influence the intention to use wearables e.g. smartwatch or pedometer, in the university environment through a Wearable Acceptance Model (WAM). WAM incorporates UTAUT model and additional variables like wearable characteristics (e.g. wearability, design and physical characteristics), attitude and privacy. WAM was used with an online survey of 129 university faculty, staff and students. Further, partial least square (PLS) path modeling was applied in analysis of 14 hypotheses to validate WAM results. In accordance to WAM, wearability and attitude tend to have a direct effect on intention to use, whereas performance and effort expectancy had only a direct influence on attitude and no direct influence on usage intention. Similarly, privacy concern, social influence had a positive influence on the intention to use both directly and indirectly through attitude. However, design and physical characteristics had no effect on intention to use. This study makes a unique empirical contribution to wearable research by extending knowledge on university users’ behavior regarding wearables for well-being.
Jayden Khakurel, Antti Knutas, Mika Immonen, and Jari Porras. 2017. Intended use of smartwatches and pedometers in the university environment: an empirical analysis. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers (UbiComp ‘17). ACM, New York, NY, USA, 97-100. DOI: 10.1145/3123024.3123147