Award Abstract #0426021
CRCD/EI: Adaptive Explanatory Visualization for Learning Programming Concepts

NSF Org:	OCI Office of CyberInfrastructure
Initial Amendment Date:	September 8, 2004
Latest Amendment Date:	August 15, 2005
Award Number:	0426021
Award Instrument:	Continuing grant
Program Manager:	Jose L. Munoz OCI Office of CyberInfrastructure O/D OFFICE OF THE DIRECTOR
Start Date:	September 15, 2004
Expires:	August 31, 2008 (Estimated)
Awarded Amount to Date:	$220000
Investigator(s):	Peter Brusilovsky peterb@mail.sis.pitt.edu (Principal Investigator) Michael Spring (Co-Principal Investigator)
Sponsor:	University of Pittsburgh University Club Pittsburgh, PA 15213 412/624-7400
NSF Program(s):	CISE EDUCAT RES & CURRIC DEVEL
Field Application(s):	0000912 Computer Science
Program Reference Code(s):	HPCC, 9217
Program Element Code(s):	1709

ABSTRACT

0426021

Peter Brusilovsky

University of Pittsburgh

$220,000

'CRCD/EI: Adaptive Explanatory Visualization for Learning Programming Concepts'

This project, involving a partnership between the University of Pittsburgh and Ramapo College, engages in an exploration of adaptive explanatory visualization as a technology to teach programming concepts. Interactive visualization provides a visual metaphor for understanding complicated concepts and uncovering the dynamics of processes that are hidden. An emerging and active application area for visualization research is visualization of program execution in Computer and Information Science (CIS) education, particularly in the context of machine level languages, high-level languages, and algorithms and data structures. This project focuses on promising approaches to useful visualization called engaging visualization, explanatory visualization, and adaptive visualization and attempts to answer the following research questions pertaining to visualization: What is the value of adaptation and explanation in the context of program visualization? Will visualization enhancement technologies produce better understanding of programming concepts? Can recent advancements in model-based generation and centralized student modeling produce efficient and scaleable adaptive explanatory visualization? Can adaptive explanatory visualization help teach the most challenging parts of programming-oriented courses? Starting with a proof-of-concept system the research team develops a set of practical tools based on adaptive explanatory visualization and examines their effectiveness in classroom studies at both partner institutions.

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