Using a MaxEnt Classifier for the Automatic Content Scoring of Free-Text Responses

Criticisms against multiple-choice item assessments in the USA have prompted researchers and organizations to move towards constructed-response (free-text) items. Constructed-response (CR) items pose many challenges to the education community - one of which is that they are expensive to score by hum...

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Bibliographic Details
Main Author: Sukkarieh, Jana Z.
Format: Conference Proceeding
Language:English
Subjects:
CHARGES
CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS
COMPUTERS
ENTROPY
GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
PHYSICAL PROPERTIES
PROBABILITY
SIMULATION
THERMODYNAMIC PROPERTIES
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Summary:Criticisms against multiple-choice item assessments in the USA have prompted researchers and organizations to move towards constructed-response (free-text) items. Constructed-response (CR) items pose many challenges to the education community - one of which is that they are expensive to score by humans. At the same time, there has been widespread movement towards computer-based assessment and hence, assessment organizations are competing to develop automatic content scoring engines for such items types - which we view as a textual entailment task. This paper describes how MaxEnt Modeling is used to help solve the task. MaxEnt has been used in many natural language tasks but this is the first application of the MaxEnt approach to textual entailment and automatic content scoring.
ISSN:0094-243X
1551-7616
DOI:10.1063/1.3573647