Multicriteria automatic essay assessor generation by using TOPSIS model and genetic algorithm

Authors:
Shu-ling Cheng;Hae-Ching Chang
Affiliations:
Department of Business Administration, National Cheng Kung University, Tainan, Taiwan;Department of Business Administration, National Cheng Kung University, Tainan, Taiwan
Venue:
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Year:
2006

Citing 4
Cited 0

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Abstract

With the advance of computer technology and computing power, more efficient automatic essay assessment is coming to use. Essay assessment should be a multicriteria decision making problem, because an essay is composed of multiple concepts. While prior works have proposed several methods to assess students' essays, little attention is paid to use multicriteria for essay evaluation. This paper presents a Multicriteria Automatic Essay Assessor (MAEA) based on combined Latent Semantic Analysis (LSA), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Genetic Algorithm (GA) to assess essays. LSA is employed to construct concept dimensions, TOPSIS incorporated to model the multicriteria essay assessor, and GA used to find the optimal concept dimensions among LSA concept dimensions. To show the effectiveness of the method, the essays of students majoring in information management are evaluated by MAEA. The results show that MAEA's scores are highly correlated with those of the human graders.