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Design Case study

Game-based Learning to Teach Computational Thinking

Learning approach for high school students

Lokesh Kumar V Mand Prof. Ravi Pooviah

IDC, IIT Bombay

Case Study - Slide Show

Design process that was followed in this project:

• Introduction

• Content
   - Why Computational thinking?
   - What is Computational Thinking?
   - Comparison between Design thinking and computational thinking
   - Importance of Computational thinking
   - Concepts in Computational Thinking
   - Scope of project
   - Concepts in focus
      . Decomposition
      . Pattern Recognition
      . Abstraction
      . Algorithm
   - Ways of Learning CT
   - Directions to explore

• Literature review
   - Piaget’s Theory of Cognitive Development
   - Theories on Play
   - Caillois’s Attitudes in Play Experience
   - Csikszentmihalyi’s Flow Theory
   - Play Pyramid by Kudrowitz and Wallace
   - Theories on Learning
   - Relationships of CT, pedagogy of programming, & Bloom’s Taxonomy
   - Frameworks of game design

• Secondary Research
   - Market study
   - Tangible product
      . Taco coding by play shifu
      . Tangiplay
      . Google Project Bloks
   - Visual coding language / interface
      . SCratch
      . MIT App inventor
      . Agent Sheets / Agent cubes
      . Google Project Bloks
   - Games
      . Lightbot
      . CodeSpark
      . Algorithm city
      . Human resource machine
      . The case study of Crabs and Turtles
      . RaBit EscApe:
   - Market study insights
   - Informal interview with teachers

• Defining
   - Various ways to do CT
   - Skill needed to perform CT
   - Requirements to create the game:
   - Defining learning objectives

• Ideations
   - Ideation based on topic
   - Pattern recognition using tangrams
   - Shoot a question”, to lean decomposition
   - Instruct” to teach Algorithm
   - Ideation based on context
   - A new Crafting system
   - Computer science game
   - “Object forming” game
   - Construction game
   - Idea Selection

• Content design of the game

• Game Design
   - Game Placement
      . Game Elements
      . Game setting
      . Player Actions
      . Winning conditions
      . The game flow starts from here
      . Scaffolding for providing Instruction in algorithm mode
      . Previous Iterations 54
      . Results of Games 55
   - Gameplay from the player's point of view 56
   - Where and when they learn CT in the gameplay

• Evaluation
   - Evaluation method
   - Game quality Evaluation using the MEEGA+ model
      . Quantitative results for measuring game quality
      . Inference
   - Qualitative analysis
      . Observations:
      . Strong aspects of the game
   - Learning evaluation
      . Think aloud analysis on gameplay
   - Improvements