Difference between revisions of "Towards Improving Regional Contest Software Environment"

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By
 
By
  
Andrew Ray
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Andrew Ray<br>
Maung  M. Htay
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Maung  M. Htay<br>
aaray,mhtay@radford.edu
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aaray,mhtay@radford.edu<br>
  
Radford University
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Radford University<br>
 
United States of America
 
United States of America
  
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[[Media:CLIS-Presentation-Ray.pdf|Presentation]]
  
 
Abstract
 
Abstract

Latest revision as of 14:54, 29 May 2011

Towards Improving Regional Contest Software Environment

By

Andrew Ray
Maung M. Htay
aaray,mhtay@radford.edu

Radford University
United States of America

Presentation

Abstract


In this paper, we at the Mid-Atlantic region describe our methodology for providing a competitive learning infrastructure for our regional contest. Our major contribution shows how to provide a pseudo-centralized contest using software meant for multi-site contests. We have found that there are major advantages to this methodology compared to our previous method—namely lower system administrator requirements. The only downside is that server-grade hardware is required for this approach. Our methodology allows for a standard PC^2 experience for the teams, and reduced the overhead necessary to create the infrastructure for our contest. After a post-mortem analysis of our methodology, along with our previous experience with automated testing systems, our second contribution in this paper was created-- a set of recommendations for a next generation of web-based contest software. By providing a web-based solution, systems administration effort will be drastically reduced. Our recommendations also provide for a seamless experience for students at the local, regional and world levels. Numerous web-based software testing and submission packages already exist, but most are not suited for multi-site contests. By providing these recommendations we hope to bring the type of system that the ACM worlds uses to the regional level. There are many challenges for creating a web-based contest system, and it will take time to reach the feature parity and polish that the current generation of PC^2 currently provides. However, our recommendations will help guide the process for a next generation competitive learning infrastructure.