Creating Gamified Collaboration Software for Education: A Design - - PowerPoint PPT Presentation

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, complete, and mature knowledge Level 3. Well-developed design theory about embedded phenomena Design theories (mid-range and grand theories) Level 2. Nascent design theory— knowledge as operational principles/architecture Constructs, methods, models, design principles, technological rules. More specific, limited, and less mature knowledge Level 1. Situated implementation

• f artefact

Instantiations (software products or implemented processes) Gregor & Hevner, 2013

DSR: Evaluation

Ostrowski, Helfert, et al. (2011-2013); Goldkuhl & Lind (2010)

Abstract design knowledge informs the creation of situational design. Situational validates abstract. All steps are grounded.

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
• ptions.
• #5 Facilitate social interaction.
• #7 Align gamification with the goal of the

activity in question.

• #8 Create a need-supporting context.

Marczewski’s1 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 Example CN2 rule:

IF Hexad = Free Spirit AND Chat Activity != Low AND Ownteam

• pentasks = high AND Own- team

task age = high AND Ownteamactivity != high THEN Challenge_class = 7

• 1. Interaction
• 4. Response and

gamification tasks (2). User behavior parameters (3). Gamification task proposal, if 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

• nline!

Web: http://anttiknutas.net Twitter: @aknutas Email: antti.knutas@lut.fi