Information Extraction and Machine Learning: Auto-Marking Short Free Text Responses to Science Questions

Authors:
Jana Z. Sukkarieh;Stephen G. Pulman
Affiliations:
Computational Linguistics Group, University of Oxford, OX1 2HG, OXFORD, UK;Computational Linguistics Group, University of Oxford, OX1 2HG, OXFORD, UK
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

Traditionally, automatic marking has been restricted to item types such as multiple choice that narrowly constrain how students may respond. More open ended items have generally been considered unsuitable for machine marking because of the difficulty of coping with the myriad ways in which credit-worthy answers may be expressed. Successful automatic marking of free text answers would seem to presuppose an advanced level of performance in automated natural language understanding. However, recent advances in computational linguistics techniques have opened up the possi-bility of being able to automate the marking of free text responses typed into a computer without having to create systems that fully understand the answers. This paper describes the use of information extraction and machine learning techniques in the marking of short, free text responses of up to around five lines.