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Symmetry 2017, 9(1), 11; doi:10.3390/sym9010011

Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and Machine Learning

Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38542, Korea
Department of Statistics and Computer Science, Kunsan National University, Gunsan 54150, Korea
Sorrel College of Business, Troy University, Troy, AL 36082, USA
Author to whom correspondence should be addressed.
Academic Editor: Yuhua Luo
Received: 17 September 2016 / Revised: 3 January 2017 / Accepted: 6 January 2017 / Published: 12 January 2017
(This article belongs to the Special Issue Symmetry in Cooperative Applications II)
View Full-Text   |   Download PDF [683 KB, uploaded 12 January 2017]   |  


This study develops a model for essay scoring and article relevancy. Essay scoring is a costly process when we consider the time spent by an evaluator. It may lead to inequalities of the effort by various evaluators to apply the same evaluation criteria. Bibliometric research uses the evaluation criteria to find relevancy of articles instead. Researchers mostly face relevancy issues while searching articles. Therefore, they classify the articles manually. However, manual classification is burdensome due to time needed for evaluation. The proposed model performs automatic essay evaluation using multi-text features and ensemble machine learning. The proposed method is implemented in two data sets: a Kaggle short answer data set for essay scoring that includes four ranges of disciplines (Science, Biology, English, and English language Arts), and a bibliometric data set having IoT (Internet of Things) and non-IoT classes. The efficacy of the model is measured against the Tandalla and AutoP approach using Cohen’s kappa. The model achieves kappa values of 0.80 and 0.83 for the first and second data sets, respectively. Kappa values show that the proposed model has better performance than those of earlier approaches. View Full-Text
Keywords: data mining; text mining; computer-assisted assessment; article relevancy; prediction model data mining; text mining; computer-assisted assessment; article relevancy; prediction model

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Mehmood, A.; On, B.-W.; Lee, I.; Choi, G.S. Prognosis Essay Scoring and Article Relevancy Using Multi-Text Features and Machine Learning. Symmetry 2017, 9, 11.

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