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Best Model Wins: Using Games to Teach Advanced Modeling and Data Science

Listen and learn

Listen and learn : Lecture


Saturday, December 5, 7:45–8:30 am PST (Pacific Standard Time)

Joseph Baird  
Learn how analytics students at the Kelley School of Business are competitively hunting spies, predicting floods and harvesting giant peaches to master advanced concepts in data modeling. Then, practice using a versatile design framework to design games for your own classroom — whether your students are kindergartners, college seniors or beyond.

Audience: Curriculum/district specialists, Teachers, Teacher education/higher ed faculty
Skill level: Beginner
Attendee devices: Devices not needed
Topic: Games for learning & gamification
Grade level: Community college/university
Subject area: Math, STEM/STEAM
ISTE Standards: For Educators:
Designer
  • Explore and apply instructional design principles to create innovative digital learning environments that engage and support learning.
For Students:
Computational Thinker
  • Students formulate problem definitions suited for technology-assisted methods such as data analysis, abstract models and algorithmic thinking in exploring and finding solutions.
  • Students collect data or identify relevant data sets, use digital tools to analyze them, and represent data in various ways to facilitate problem-solving and decision-making.

Proposal summary

Purpose & objective

Participants will learn and practice implementing a versatile framework for building educational games in the classroom. The framework caters specifically to educators that feel they are incapable of designing games because they lack experience, aren't tech-savvy, or teach in a field that isn't well-suited for games.
A sample of principles to be discussed:
What are the rules/characteristics of a simplified “universe” where mastery of the objective is useful?
What systems do we want to expose the player to?
What need, pain, or unsolved problem do we want students to feel at a visceral level?
What real-world consequences can we remove or reduce?
What feedback loops from the real world can we compress into a tighter, shorter frame?
What opportunities exist for players to compete with each other?
Are there real-world feedback loops that can be exaggerated or made more dramatic?
The session will allow for both discussion and implementation of these questions in fields specific to the participants.

Outline

INTRO
FRAMEWORK / PRINCIPLES
KELLEY EXAMPLES
WORKSHOP
CLOSING

Supporting research

.

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Presenters

Photo
Joseph Baird, Indiana University

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