DEVELOPING GAMES FOR LEARNING Presented by Sarah Toms Wharton Learning Lab March 25th 2015 KNOWLEDGE FOR ACTION INTRODUCTIONS @sarahetoms stoms@wharton.upenn.edu IT Director, Wharton Learning Lab http://simulations.wharton.upenn.edu Sarah E. Toms, IT Director stoms@wharton.upenn.edu KNOWLEDGE FOR ACTION Types of Game Thinking KNOWLEDGE FOR ACTION Wharton Computing 3 SERIOUS GAMES are a mental contest, played in accordance with specific rules to further government or corporate training, education, health, public policy, and strategic communication objectives Source: Mike Zyda, “From Visual Simulation to Virtual Reality to Games”, IEEE Computer 2005 KNOWLEDGE FOR ACTION SIMULATION is the imitation of the operation of a real-world process or system over time. Source: Wikipedia - http://en.wikipedia.org/wiki/Simulation KNOWLEDGE FOR ACTION Solve a problem Experiential KNOWLEDGE FOR ACTION Experiment Fun Anatomy of a Serious Game Faculty Interface Student Interface Model & Game Logic / Rules KNOWLEDGE FOR ACTION Wharton Computing 7 Single Player vs Multi Player Player Decisions Model / Rules Player n Decisions Player 1 Decisions Model / Rules Result Result KNOWLEDGE FOR ACTION Wharton Computing 8 Player Roles 1. Same or Different? 2. Background • Complexity • Implications on model and game outcomes 3. Rules for each role • Differing game play? KNOWLEDGE FOR ACTION Wharton Computing 9 Rounds & Scaffolding 1 • Acclimation to game rules • Base Concept(s) 2 • Feedback • Achievement x • Pull lever – introduce new concept & increase complexity y • Level Up – based on achievement, increase complexity KNOWLEDGE FOR ACTION Wharton Computing 10 Storyline Period n Period 2 Period 1 Intro Background Rules Problem KNOWLEDGE FOR ACTION Event A Decision X Decision Y Trigger XY Event B Decision X Decision Z Wharton Computing 11 Self Determination Theory Optimally Challenged Choices Connection with others Promoting intrinsic motivation Source: https://engaming.wordpress.com/2012/04/25/gamification-thoughts-on-definition-and-design/ KNOWLEDGE FOR ACTION Wharton Computing 12 Flow Theory Source: https://engaming.wordpress.com/2012/04/25/gamification-thoughts-on-definition-and-design/ KNOWLEDGE FOR ACTION Wharton Computing 13 Agility: Key to Success 1. Iterative Approach • Bite sized chunks & regular check ins 2. User Stories • In Players Prepare mode a student player can login with their Penn Key and read about their role and the rules of the game • In Play mode a student player can login and will see a gameboard interface for entering their decisions 3. Testing KNOWLEDGE FOR ACTION Wharton Computing 14 LEARNING OBJECTIVES KNOWLEDGE FOR ACTION Simple Multiplayer: Duopoly 1. Learning Objective: Game Theory, Prisoner’s Dilemma, Collusion, Competition 2. Model 3. 20 rounds KNOWLEDGE FOR ACTION Wharton Computing 16 Complex Multiplayer: OPEQ 1. Learning Objective: Game Theory, Multi Party Prisoner’s Dilemma, Collusion, Competition, Shadow of the Past, Negotiations 2. Model 3. 3 Players per team 4. Role: Oil minister 5. Configurable # rounds KNOWLEDGE FOR ACTION Wharton Computing 17 KNOWLEDGE FOR ACTION Wharton Computing 18 KNOWLEDGE FOR ACTION Wharton Computing 19 DEBRIEF & RESULTS KNOWLEDGE FOR ACTION KNOWLEDGE FOR ACTION