Profile-Based Algorithm for Personalized Gamification ...

Profile-Based Algorithm for Personalized Gamification in Computer-Supported Collaborative Learning Environments Antti Knutas, Rob van Roy, Timo Hynninen, Marco Granato, Jussi Kasurinen, Jouni Ikonen Lappeenranta University of Tech. & LERO, Irish Software Research Centre KU Leuven University of Milan South-Eastern Finland University of Applied Sciences

Introduction and structure 1. 2. 3. 4. 5. 6. 7. 8.

Terms and concepts Research questions Need for personalization Overall approach Relevant design theories Rule design structure Outcomes Conclusions

Terms and concepts • Collaborative learning • “students working towards a shared goal with a teacher as a facilitator” • Gamification • ”applying game mechanics to non-game environments” • Self-determination theory • One explanation for why people act (voluntarily) as they do, by Deci & Ryan

Introduction and research questions 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 & Deterding; 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’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: Action:

• • • • •

Object: Rules: Feedback: Challenge: Motivation:

Get other team to assist yours a) Point out a task to the other team b) Task is solved (system state) (system functionality) Notifications, team status (inherent difficulty) Relatedness

Algorithm and system architecture Backend: CN2 rule inducer 1. Interaction

4. Response and gamification tasks

(2). User behavior parameters

(3). Gamification task proposal, if conditions match

Example CN2 rule: IF Hexad = Free Spirit AND Chat Activity != Low AND Ownteam opentasks = high AND Own- team task age = high AND Ownteamactivity != high THEN Challenge_class = 7

Application environment #1

Application environment #2

Conclusions •

We presented an approach to create personalized gamification rulesets using a framework for effective gamification (Goal 1). • This ruleset algorithm can be used as a plugin in computer-supported collaborative learning environments (Goal 2) • Novel results • Personalization through adaptation (one of the first implementations for gamification) • Separating rules from presentation

Bonus slide: All material available libre https://github.com/aknutas/ludusengine