Automatic and Semi-Automatic Skill Coding With a View Towards Supporting On-Line Assessment

Authors:
Carolyn Rosé;Pinar Donmez;Gahgene Gweon;Andrea Knight;Brian Junker;William Cohen;Kenneth Koedinger;Neil Heffernan
Affiliations:
Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh PA, 15213;Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh PA, 15213;Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh PA, 15213;Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh PA, 15213;Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh PA, 15213;Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh PA, 15213;Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh PA, 15213;Worcester Polytechnic Institute, 100 Institute Road, Worcester MA, 01609-5357
Venue:
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Year:
2005

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Abstract

This paper explores the problem of automatic and semi-automatic coding of on-line test items with a skill coding that allows the assessment to occur at a level that is both indicative of overall test performance and useful for providing teachers with information about specific knowledge gaps that students are struggling with. In service of this goal, we evaluate a novel text classification approach for improving performance on skewed data sets that exploits the hierarchical nature of the coding scheme used. We also address methodological concerns related to semi-automatic coding.