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General Information
    • ISSN: 2010-3689
    • Frequency: Bimonthly (2011-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJIET
    • Editor-in-Chief: Prof. Dr. Steve Thatcher
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library, Google Scholar, Crossref and ProQuest
    • E-mail: ijiet@ejournal.net
Editor-in-chief
Prof. Dr. Steve Thatcher
University of South Australia, Australia
It is my honor to be the editor-in-chief of IJIET. The journal publishes good papers which focous on the advanced researches in the field of information and education technology. Hopefully, IJIET will become a recognized journal among the scholars in the filed of information and education technology.
IJIET 2014 Vol.4(2): 132-137 ISSN: 2010-3689
DOI: 10.7763/IJIET.2014.V4.384

Automatic Medical Case Study Essay Scoring by Support Vector Machine and Genetic Algorithms

S. Yenaeng, S. Saelee, and W. Samai
Abstract—The study of medical various institutions in Thailand found that the success for teaching and learning by means of Problem Based Learning (PBL) depends on several factors including correct attitudes and cognitive learning. Problem of teaching a small group of PBL is consistent with the results of the audit were to evaluate the group's facilitator, diverse and inaccurate. Especially, the grading of a medical case study essay reports (Clinical case summaries). To solve such problems, we proposed automatic medical case study essay scoring for PBL of medical students. SVM with Genetic Algorithms (GA-SVM) was used to assess the quality medical case study essays written by medical students in the subject matter of muscular systems and movement. The medical case study essays written in response to a question were each evaluated by facilitators and assigned a human score. In the experiment, we used raw term frequency vectors of the essays and their corresponding human scores to train the SVM while GA was used for choosing the kernel function type and its parameter values to find a proper solution to an optimization and obtain the machine scores. The experimental results show that the addition of GA-SVM technique improves scoring performance.

Index Terms—Essay scoring, medical case study, SVM, genetic algorithms (GA).

S. Yenaeng is with the Department of Computer Education, Faculty of Education, Bansomdejchaopraya Rajabhat University, Bangkok, Thailand (e-mail: modssk@gmail.com).
S. Saelee is with Department of Computer Education, Faculty of Technical Education, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand.
W. Samai is with the Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand.

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Cite: S. Yenaeng, S. Saelee, and W. Samai, "Automatic Medical Case Study Essay Scoring by Support Vector Machine and Genetic Algorithms," International Journal of Information and Education Technology vol. 4, no. 2, pp. 132-137, 2014.

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