Creating Gamified Collaboration Software for Education: A Design Science Perspective Antti Knutas Lappeenranta University of Tech. & Lero, the Irish Software Research Centre
Structure 1. Introduction 2. Design science research 3. Designing for gamification 4. An approach to create algorithm-based personalization 5. Future work and conclusion
To Start With (terms and introduction)
Defining terms • Gamification • ”applying game mechanics to non -game environments for gameful or playful affordances” • Socio-technical system • “ a complex system which involves both physical – technical elements and networks of interdependent actors ” • Collaboration • “the action of working together with the same goals ”
Introduction • Topics of the day: Gamification, collaborative software and design science research approach • Gamification – well research and applied • Collaboration – Johnson & Johnson and others • Descriptive knowledge in gamification – well supported by theories (Deci & Ryan and others) • How about results and rigour in prescriptive knowledge? (gamified system design and implementation)
Design Science Research
Design Science Research • From information system sciences • Applicable where technological and social systems intersect • Aims to create prescriptive knowledge through the application of innovative artefacts • Both useful and help to understand the problem • “Validity evaluated through utility”
DSR: The Big Picture (one possible setup) Knutas et al. 2018 (forthcoming)
DSR: Three Cycle View Hevner et al., 2004
Design Science Artefacts Contribution type Example artefact More abstract, Level 3. Well-developed design Design theories (mid-range complete, and mature theory about embedded and grand theories) knowledge phenomena Level 2. Nascent design theory — Constructs, methods, knowledge as operational models, design principles, principles/architecture technological rules. More specific, limited, Instantiations (software Level 1. Situated implementation and less mature products or implemented of artefact knowledge processes) Gregor & Hevner, 2013
DSR: Evaluation Abstract design knowledge informs the creation of situational design. Situational validates abstract. All steps are grounded. Ostrowski, Helfert, et al. (2011-2013); Goldkuhl & Lind (2010)
Designing for Gamification
Designing for Gamification • “ Gameful and playful experiences” • Often used for engagement or motivation • System is more than a sum of its parts • Just as difficult as designing any engaging experience or a “fun” game • Experience of fun varies. Userbase is heterogenous. • Often misunderstood: Pointsification and “evil gamification”
Designing for Gamification: Deterding’s “Lens of Intrinsic Skill Atoms” • “User's activity entails certain inherent, skill -based challenges” • “ Intrinsic integration between the content and the gamification mechanic ” • Gameful system should support user goals by • Directly facilitating their attainment • Removing all extraneous challenges • Restructuring remaining inherent challenges into nested, interlinked feedback loops (of goals, actions, objects, rules, and feedback that afford motivating experiences) Deterding, 2015
Designing for Gamification: Deterding’s design steps + personalization algorithm 1. Define gamification strategy 2. Research 3. Select personalization strategy (novel) 4. Synthesis: Activity – challenge – motivation clusters 5. Ideation 6. Distill rules into an algorithm (novel) 7. Rapid prototyping Deterding, 2015; Knutas et al., 2018 (forthcoming)
Algorithm-based Personalization Artefact Design Process 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)
Research goals Motivation -> Gamification, a one size fits all solution? 1. How can personalized gamification features be designed to address the preferences of different user types? 2. How could customized, profile-based gamification challenges be assigned to different users in CSCL environments?
Personalization -> effectiveness? • Different users interpret, functionalize and evaluate the same game elements in radically different way (Koster) • E.g. there are five different functions a user can ascribe to a badge (Anton & Churchill) • Personalization has been successful in other digital contexts
Approach • Deterding’s gamification design process • Synthesis: Apply relevant theories • Self-determination theory + • Design heuristics for effective gamification (van Roy et al.) • Ideation: How to personalize? • Marczewski’s gamification user types + • Lens of intrinsic skill atoms (Deterding) • Iterative prototyping: Rules -> CN2-based rule generator based on expert panel created examples
Design heuristics for effective gamification (van Roy et al.; relevant examples) • #1 Avoid obligatory uses. • #2 Provide a moderate amount of meaningful options. • #5 Facilitate social interaction. • #7 Align gamification with the goal of the activity in question. • #8 Create a need-supporting context.
Marczewski’s 1 gamification user type hexad 1. 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.
Constructing the rules (an example) • Goal: Get other team to assist yours • Action: a) Point out a task to the other team b) Task is solved • Object: (system state) • Rules: (system functionality) • Feedback: Notifications, team status • Challenge: (inherent difficulty) • Motivation: Relatedness
Algorithm and system architecture Backend: CN2 rule inducer 4. Response and 1. Interaction gamification tasks Example CN2 rule: IF Hexad = Free Spirit AND Chat Activity != Low AND Ownteam opentasks = high AND Own- team (2). User (3). Gamification task age = high AND Ownteamactivity behavior task proposal, if != high THEN Challenge_class = 7 parameters conditions match
Application environment #1
Application environment #2
Outcomes • Novel approach to create personalized gamification rulesets using a framework for effective gamification (level 2; method artefact). • Novel results: Personalization of rules and content through user preferences - one of the first implementations for gamification (level 1; instantiation artefact) • What next: Evaluation of both levels of artefacts -> design evidence
Outcomes bonus: All material available libre https://github.com/aknutas/ludusengine
To Sum It Up (conclusion and future work)
In conclusion • Design science research can benefit overall gamification research in the form of design theories and better evidence • Social sciences research can contribute to (applied) gamification research in the form of better kernel theories • What is missing in the field: More design recommendations for the application domain rigorously supported by evidence (and connected to kernel theories)
Future work • Formalizing, publishing, and evaluating personalization design process • Publication forthcoming • Higher level artefact – more challenging evaluation • Evaluating the connection between gamification features and types of motivation • Design recommendations require concrete evidence – currently missing in the field
Thank you; let’s keep up the discussion online! Web: http://anttiknutas.net Twitter: @aknutas Email: antti.knutas@lut.fi
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