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
01 May 2017
Figure: Screenshot from a Video Lecture on Gamification
In this research paper we publish our results from a longitudal study where we observed ten courses that used video instruction as a part of a course. Overall the experiences with video-based learning were positive. The video lectures were perceived to be highly useful by the students and were rated to be the most useful component of the course in a majority of the observed courses. Also, some of the tutorial videos received a lot of traffic from external sources, indicating that the videos provided additional benefit to the wider public.
The problems identified from prior research, especially the added effort and costs of video production, were not not an issue. We also found that unlike in previous literature, the video length did not affect usage patterns or student satisfaction. Previously shorter videos have been recommended, but longer and well-structured videos worked just as well.
One notable statistic is that the majority of viewers used a desktop or a laptop machine (84%), while only a fraction (14%) used mobile devices such as smartphones or tables.
See also our previous work on flipped classroom teaching method, which depends heavily on video lectures (presentation slides on flipped classroom).
Preprint is available at ResearchGate.
Millennials have learned to seek information from the Internet whenever they need to know something and want to learn things. In this study, we present observations from several university courses with freely available online resources for the modern students. Ten different courses with video lectures were observed, often with positive outcomes and improved results compared to the previous course arrangements. Additionally, unlike in some previous literature, we observed that some issues such as the video length did not have a meaningful impact on the learning outcomes. Overall, the results indicate that videos offer excellent benefit-effort-ratio, and are an efficient way to reach the target audience: the students.
Antti Herala, Antti Knutas, Erno Vanhala, Jussi Kasurinen,”Experiences from Video Lectures in Software Engineering Education”, International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.5, pp.17-26, 2017. DOI: 10.5815/ijmecs.2017.05.03
11 Dec 2016
Figure: A Three Cycle view of Design Science Research Process (Hevner, 2007)
My recently defended doctoral thesis on computer-supported collaborative work is now available online. The application domain in university level engineering education, and gamification is one of the major methods I investigated and applied. The thesis also includes a rather thorough use of the design science methodology in design, implementation cycles and validation.
Find the PDF available for free from the Doria library archive.
Knutas, A. (2016). Increasing Beneficial Interactions in a Computer-Supported Collaborative Environment. Acta Universitatis Lappeenrantaensis.
Publications included in the thesis
- Knutas, A., Ikonen, J., & Porras, J. (2013). Communication patterns in collaborative software engineering courses: a case for computer-supported collaboration. In Proceedings of the 13th Koli Calling International Conference on Computing Education Research (pp. 169-177). ACM. Preprint from ResearchGate.
- Knutas, A., Ikonen, J., & Porras, J. (2015). COMPUTER-SUPPORTED COLLABORATIVE LEARNING IN SOFTWARE ENGINEERING EDUCATION: A SYSTEMATIC MAPPING STUDY. International Journal on Information Technologies & Security, 7(4). IJITS Archive. Preprint from ResearchGate.
- Ikonen, J., Knutas, A., Wu, Y., & Agudo, I. (2015, November). Is the world ready or do we need more tools for programming related teamwork?. In Proceedings of the 15th Koli Calling Conference on Computing Education Research (pp. 33-39). ACM. Preprint from ResearchGate.
- Knutas, A., Ikonen, J., Nikula, U., & Porras, J. (2014, June). Increasing collaborative communications in a programming course with gamification: a case study. In Proceedings of the 15th International Conference on Computer Systems and Technologies (pp. 370-377). ACM. Preprint from ResearchGate.
- Knutas, A., Ikonen, J., Maggiorini, D., Ripamonti, L., & Porras, J. (2016). Creating Student Interaction Profiles for Adaptive Collaboration Gamification Design. International Journal of Human Capital and Information Technology Professionals (IJHCITP), 7(3), 47-62. DOI: 10.4018/IJHCITP.2016070104. IGI Global.
University and software engineering teaching are changing in response to the industry demand for new skills. Learning is becoming more interactive, and the impact of student collaborative learning has increased. The extension of collaboration with computer-supported collaborative environments allows increased knowledge building between a wider range of participants. More flexible teaching structures independent of place or time, better monitoring of student understanding by instructors, and improved student productivity and satisfaction can also be facilitated. However, the collaboration has to be implemented carefully, or it will become a drawback instead of a benefit. The first objective of this study is to document the current state of the utilization of collaborative environments and methods in software engineering education.
The next stage is to use the results to first specify the requirements for a computer-supported collaborative environment, then to design and implement a prototype, and finally to use this prototype to evaluate and validate the design for improved collaboration. The research follows the design science research process, where a solution design is created through an iterative design and evaluation process and the solution is validated through its utility. A design for improving collaboration by improving issue-related and inter-team communication is created. The collaboration is promoted further by applying gamification to the design. The study shows that engineering students can be encouraged to collaborate online with the application of gamification, that the system increases connectivity in collaboration patterns, and in some cases this collaboration has positive results for learning goals. During the research, the state of gamification design for computer-supported collaboration was developed further by connecting it with the theory of player profiles. Different types of players respond best to different kinds of rewards, for example a simulated social status or additional challenges instead of just an increased score. This study also presents a method for creating gamification profiles from empirical observations in collaborative learning environments.
24 Aug 2016
Figure: Bartle’s Taxonomy of Player Types (1996)
Gamification is a hot topic in research and it has been widely applied to the web. However, it is not a magic bullet for user engagement and we propose that there can be a better approach than a “one size fits all” design. Our solution is to define several different user profiles and adaptively apply them for different types of users. For example, one type of person might like having most points and being on the top of the high score and another type of user might enjoy exploring new solutions and sharing with them with the community. Both types of users might do the same activity, but their internal motivation for enjoying the activity are different. The challenge in this approach is to detect the type of user and then adaptively present the right gamification elements to each type of user.
We used an evidence-based method for deciding which gamification elements to apply and how to apply them. In order to do this we built behavior profiles with interaction analysis and profiling surveys. These profiles can be used match types of user to most suitable gamification and game design elements in order to create or improve adaptive gamification systems.
We discovered four types of activitiy profiles, compared them with Bartle types (2004) and matched them with formal elements of game design (Fullerton, 2008) that might be most attractive to each cluster. These profiles can be used to design adaptive gamification approaches, especially for online collaborative systems.
Read more at the IJHCITP journal website. Unfortunately this time the licensing restriction prohibited publishing a pre-print version. If your library does not subscribe to IJHCITP and you still want to view the results, please contact me on Twitter or ResearchGate and we will figure out a solution.
We used Social Network Analysis and K-Means clustering to construct the profiles. There is a Prezi presentation from a previous study that visualizes some of the data collection methods.
Benefits of collaborative learning are established and gamification methods have been used to motivate students towards achieving course goals in educational settings. However, different users prefer different game elements and rewarding approaches and static gamification approaches can be inefficient. We present an evidence-based method and a case study where interaction analysis and k-means clustering are used to create gamification preference profiles. These profiles can be used to create adaptive gamification approaches for online learning or collaborative learning environments, improving on static gamification designs. Furthermore, we discuss possibilities for using our approach in collaborative online learning environments.
Below you can find a list of the activity clusters we discovered, with the corresponding Bartle player types and formal game elements that could be applied to each cluster.
Table: Discovered Profile Clusters
| Profile cluster
|| Exhibited Bartle player types
|| Most applicable game elements
|CL1: “Cooperative workers”
||Formal elements: Player interaction, rules, conflict, outcomes. Dramatic elements: Character, challenge, play.
|CL2: “Social team members”
||Heart, club, spade
||Formal elements: Player interaction, rules, resources, boundaries, outcomes. Dramatic elements: Premise, story, character, challenge, play.
|CL3: “Achievement-oriented leaders”
||Formal elements: Player interaction, rules, procedures, conflict, boundaries, outcomes. Dramatic elements: Challenge, play.
|CL4: “Task-oriented workers”
||Diamond, club, spade
||Formal elements: Rules, conflict, resources, outcomes. Dramatic elements: Premise, challenge, play.
Knutas, A., Ikonen, J., Maggiorini, D., Ripamonti, L., & Porras, J. (2016). Creating Student Interaction Profiles for Adaptive Collaboration Gamification Design. International Journal of Human Capital and Information Technology Professionals (IJHCITP), 7(3), 47-62. DOI: 10.4018/IJHCITP.2016070104