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Parameters driving effectiveness of automated essay scoring with LSA

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conference contribution
posted on 26.05.2006, 11:52 by Fridolin Wild, Christina Stahl, Gerald Stermsek, Gustaf Neumann
Automated essay scoring with latent semantic analysis (LSA) has recently been subject to increasing interest. Although previous authors have achieved grade ranges similar to those awarded by humans, it is still not clear which and how parameters improve or decrease the effectiveness of LSA. This pa-per presents an analysis of the effects of these parameters, such as text pre-processing, weighting, singular value dimensionality and type of similarity measure, and benchmarks this effectiveness by comparing machine-assigned with human-assigned scores in a real-world case. We show that each of the identified factors significantly influences the quality of automated essay scor-ing and that the factors are not independent of each other.

History

School

  • University Academic and Administrative Support

Department

  • Professional Development

Research Unit

  • CAA Conference

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237476 bytes

Citation

WILD et al, 2005. Parameters driving effectiveness of automated essay scoring with LSA. IN: Proceedings of the 9th CAA Conference, Loughborough: Loughborough University

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© Loughborough University

Publication date

2005

Notes

This is a conference paper.

Language

en

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Loughborough Publications

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