NSF CCLI Showcase

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NSF CCLI Showcase

• SIGCSE 2006

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NSF CCLI ShowcaseSIGCSE 2006

• Thursday, 1030 a.m.1200 p.m.
• Program Visualization using Virtual Worlds
• Stephen Cooper, St. Josephs University
• Wanda Dann, Ithaca College
• Barbara Moskal, Colorado School of Mines
• Randy Pausch, Carnegie Mellon University
• Online Programming Tutors for Computer Science I
• Amruth Kumar, Ramapo College of New Jersey
• Extending the Next Generation Robot Laboratory to
  Increase Diversity in Undergraduate CS Programs
• Maria Gini, University of Minnesota
• Karen Sutherland, Augsburg College
• Janice Pearce, Berea College

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Program Visualization using Virtual Worlds
CCLI NSF-0339734
Novices author 3D movies and create interactive
computer games as they learn object-oriented
programming concepts -- Increasing retention --
Attracting more majors Running professional
development workshops for teachers

• Stephen Cooper, Saint Josephs University
• Wanda Dann, Ithaca College
• Barbara Moskal, Colorado School of Mines
• Randy Pausch, Carnegie Mellon University

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Problets Online Programming Tutors for Computer
Science I

• Helps students learn by solving problems
• Explains the execution of program code
• Adapts to the learning needs of students
• Available for C/C/Java/C
• For expressions, selection, loops, functions
• Provides class summary to instructors
• Runs on recent Java-aware browsers
• Free for educational use
• Contact amruth_at_ramapo.edu

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Extending the Next Generation Robot Laboratory to
Increase Diversity in Undergraduate CS Programs
Maria Gini, University of Minnesota Jan Pearce,
Berea College Karen Sutherland, Augsburg College
Process We are developing programming modules
designed to encourage undergraduates majoring in
fields such as health professions, business, art,
and education to take more computer science
courses. The approach is centered around using
the AIBO Sony robot dogs at the very beginning of
the computer science curriculum.

• Objectives
• To increase confidence through hands-on
  programming experiences.
• To solve real-world and open-ended problems.
• To foster cooperation by working in groups.

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NSF CCLI ShowcaseSIGCSE 2006

• Friday 1030 a.m.1200 p.m.
• The UC-WISE project Online Curricula for
  Monitored, Closed-lab First-year CS Courses
• Mike Clancy, Marcia Linn Nate Titterton,
  University of California at Berkeley
• Integrating Algorithm Visualization into Computer
  Science Education
• Scott Grissom, Grand Valley State University
• Myles McNally, Alma College
• Thomas Naps, University of Wisconsin, Oshkosh
• CS 0.5 A New Approach to Introductory Computer
  Science for Majors
• Robert H. Sloan Patrick Troy, University of
  Illinois at Chicago
• Intelligent Tutoring System for CS-I and II
  Laboratory
• Jungsoon Yoo, Sung Yoo, Chrisila Pettey, Judy
  Hankins, Cen Li Suk Seo, Middle Tennessee State
  University

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The UC-WISE project Online curricula for
monitored, closed-lab first-year CS
courses(Michael Clancy, Marcia Linn, Nate
Titterton, all at U.C. Berkeley)
CCLI project goal Provide technology and
curricula for lab-based CS 1 and CS
2. Longer-term goal Allow instructors to build
and customize courses, prototype new course
elements, and collect review comments from
experienced course developers.

• Curriculum Components
• a wide variety of programming activities,
  including interactive and constrained programming
  activities with real-time code analysis and
  execution
• online and face-to-face collaborations
• embedded assessments, surveys, and self-tests.

• Research Areas
• evaluation of activities not in
  traditional-format courses, and of various kinds
  of immediate feedback for students
• analysis of student misconceptions revealed by
  the rich variety of activities in UC-WISE
  courses
• comparison of online vs. face-to-face
  collaborative activities
• exploration of differential benefits for
  underrepresented populations
• design of instructor support systems during
  phases of course creation, delivery, refinement,
  and research.

Lab instructors monitor student understanding and
provide targeted tutoring where and when it will
be most appropriate.
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Integrating Algorithm Visualization into Computer
Science Courses

• JHAVÉ Java Hosted Algorithm Visualization
  Environment
• Goal Development of a comprehensive suite of
  visualization-based materials and best practices
  for teaching algorithms and data structures
• Principal Investigators
• Scott Grissom (Grand Valley State University)
• Myles McNally (Alma College)
• Thomas Naps (University of Wisconsin - Oshkosh)
• Website http//www.jhave.org

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CS 0.5 - A New Approach to Introductory Computer
Science

• Problem High Attrition Rates
• Cause Failure to Engage Students
• Cause One Size fits All Approach
• Solution Separate and Engage
• Allow Experience Student to test out
• Uses Media to Engage Students
• Results Higher Success Rate

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Adaptive Tutor for Online Learning

• Middle Tennessee State University
• J. Yoo, C. Pettey, S. Yoo J. Hankins, C. Li, S.
  Seo
• Web-based lab environment to provide personalized
  assistance.
• Tutor employs a two-step semi-supervised learning
  system for strategy adaptation
• Student progress monitoring system
• Question generation module
• Automatic grading

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NSF CCLI ShowcaseSIGCSE 2006

• Friday 200 p.m.330 p.m.
• jGRASP A Framework for Integrating
  Visualizations of Software (page 11)
• James Cross, Dean Hendrix David Umphress,
  Auburn University
• Project MLExAI Machine Learning Experiences in
  AI (page 12)
• Ingrid Russell, University of Hartford
• Zdravko Markov, Central Connecticut State
  University
• Todd Neller, Gettysburg College
• A Radical Approach to Teaching Object-oriented
  Programming(page 13)
• Kathryn E. Sanders Ann Moskol, Rhode Island
  College
• Increasing Interaction and Visualization in the
  Computability Course (page 14)
• Rakesh M. Verma, Pavan Podila Saquib Hakim,
  University of Houston

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jGRASP A Framework for Integrating
Visualizations of SoftwareJames Cross, Dean
Hendrix, David UmphressAuburn University
Control Structure Diagram
UML Class Diagram
Data Structure Diagram
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Project MLExAI Machine Learning Experiences in AI

• Ingrid Russell, University of Hartford
• Zdravko Markov, Central Connecticut State
  University
• Todd Neller, Gettysburg College

• Objectives
• Enhance student learning experience by
  implementing a unifying theme of machine learning
  to tie together core AI topics.
• Increase student interest and motivation to learn
  AI by providing a framework for the presentation
  of the major AI topics that emphasizes the strong
  connection between AI and computer science.
• Highlight the bridge that machine learning
  provides between AI technology and modern
  software engineering.
• Introduce students to an increasingly important
  research area, thus motivating them to pursue
  further study in this area.

Project Goal The project goal is to develop a
framework for teaching core AI topics with a
unifying theme of machine learning. A suite of
hands-on term-long projects are developed, each
involving the design and implementation of a
learning system that enhances a commonly-deployed
application.
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A radical approach to teaching object-oriented
programmingKate Sanders Ann MoskolRhode
Island College

• Objects, inheritance, and polymorphism early
• Graphical examples
• Stress on design

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A Visual and Interactive Automata Theory Course
Emphasizing Breadth of Automata
By Rakesh Verma, Pavan Podila and Saquib
Hakim Computer Science Department University of
Houston, Houston, TX 77204-3010 http//www.cs.uh.e
du/rmverma

• Objectives
• Enhance learning of automata and learning
  experience
• Expose students to current applications of
  automata
• Provide glimpses of the variety of finite
  automata including tree and DAG automata

• We have
• Enhanced JFLAP to generate random strings for
  debugging of automata designs
• We have developed
• Lecture notes with HTML and PowerPoint slide
  presentations that include JFLAP animations
• New course materials that include applications
  of finite automata
• New lab assignments and problem sets
• A Web repository with teaching materials
• Graphical interface, RuleMaker, for visualizing
  tree automata

• Background
• Problems in learning Automata Theory
• Too abstract
• Too difficult
• Student Perception Of little use
• Student Perception Dated material

• Conclusions Future Work
• It can be done!
• Add more recent applications, e.g., Buchi
  automata and model checking
• Write a new text book ?!
• Acknowledgements Thanks to M.F. Anwar. Work
  supported in part by NSF grant DUE 0311407

• Methods
• Enhance and integrate visualization tools, e.g.,
  JFLAP from Duke and LRR from University of
  Houston
• Add current applications
• Add useful examples of automata

• Results
• Inspired students to work on LRR and automata
  projects
• Positive end of course evaluations, tool/website
  appreciation
• Students helped with slides and web page

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NSF CCLI ShowcaseSIGCSE 2006

• Saturday 1030 a.m.1200 p.m.
• Due to logistical reasons, the Saturday showcase
  session has been cancelled