Open AccessAutomated Scoring for Essay Questions In E-learning
Author(s) -
Manar Joundy Hazar,
Zinah Hussein Toman,
Sarah Hussein Toman
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1294/4/042014
Subject(s) - computer science , measure (data warehouse) , process (computing) , pearson product moment correlation coefficient , artificial intelligence , mean squared error , the internet , data collection , correlation coefficient , intelligent tutoring system , software , e learning , machine learning , natural language processing , data mining , world wide web , statistics , mathematics , programming language , operating system
E-learning is employing technology to help and promote learning begun decades ago.one of most important example of using intelligent software agent in e-learning is Intelligent Tutoring systems (ITSs). Assessment plays a significant role in the educational process. Automated Essay Scoring (AES) is defined as the computer technology that evaluates and scores the subjective answers. To evaluate students essay answers based on a linguistic knowledge in this project we presented a suitable model. Getting results from the recommended system application in calculating simulation results indicates high precision in performance associated with other methods. We used square root error rate (RMSE) and Pearson correlation coefficient metrics to measure the system efficiency and also use student answer acquired from the University of North Texas data collection, this data accessible on the Internet.