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Integrating Empirical Methods into the Computer Science Curriculum Central Themes of Our Approach See empirical methods early and often Incremental exposure to empirical methods Integrate empirical methods into existing CS courses with minimal overhead See: Core Empirical Skills and Concepts for Computer Science Braught, Miller, Reed; SIGCSE 2004 Empirical Investigation Throughout the Curriculum Reed, Miller, Braught; SIGCSE 2000
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Teaching Empirical Skills and Teaching Empirical Skills and Concepts in Computer Science Concepts in Computer Science
using Random Walksusing Random Walks
Grant BraughtDickinson College
Integrating Empirical Methods into Integrating Empirical Methods into the Computer Science Curriculumthe Computer Science Curriculum• Collaborators:
• David Reed, Creighton University• Craig Miller, DePaul University• Grant Braught, Dickinson College
• Goals:• Identify core empirical skills and
concepts• Develop and assess materials
Integrating Empirical Methods into Integrating Empirical Methods into the Computer Science Curriculumthe Computer Science Curriculum• Central Themes of Our Approach
• See empirical methods early and often• Incremental exposure to empirical
methods• Integrate empirical methods into
existing CS courses with minimal overhead
• See:• Core Empirical Skills and Concepts for Computer Science
Braught, Miller, Reed; SIGCSE 2004• Empirical Investigation Throughout the Curriculum
Reed, Miller, Braught; SIGCSE 2000
Exploring Random WalksExploring Random Walks
• Expose students to issues most central to experimentation with simulations• Consistency and Accuracy• Hypothesis testing
• Unique Aspects• Designed for first week of the first
course• Makes use of an underutilized time slot• Extensions support incremental
exposure
The Random Walks ApplicationThe Random Walks Application
• Demonstration
Exploring Random WalksExploring Random Walks
• Accuracy and Averaging Multiple Runs
Runs / Experiment1 5 20
Exp RMS % Error RMS % Error RMS % Error1 3.346 66.54% 11.687 16.87% 9.706 2.94%2 9.985 0.15% 9.109 8.91% 9.385 6.15%3 2.237 77.63% 10.039 0.39% 10.752 7.52%4 4.382 56.18% 8.446 15.54% 9.208 7.92%5 5.492 45.08% 8.672 13.28% 9.319 6.81%
Exploring Random WalksExploring Random Walks
• Consistency and Accuracy1 Run/Experiment 5 Runs/Experiment 20 Runs/Experiment
Exploring Random WalksExploring Random Walks
• Hypothesis Testing
• How does the expected RMS distance of an unconstrained random walk compare to that of a random city-walk?
•Smaller? Larger? The Same?
•Typical results: RMSUnconst = 9.027RMSCity =10.542
Extensions & VariationsExtensions & Variations
• Later in First Course:• An objects early assignment
• Implementing the Turtle object•Split assignment content into two
parts
• Later in the Curriculum• A language interpreter
• Implementing the TurtleInterpreter object
Project Web SiteProject Web Site
•www.empirical.cs.creighton.edu•Links to Related Papers •Assignment Repository
•Classroom testers and evaluators needed
Partial support for this work was provided by the National Science Foundation’s Course, Curriculum and laboratory Improvement Program. (Grant: DUE-0230950)