@article{2901559839c841099ca10375168bf6c5,
title = "A machine learning-based usability evaluation method for eLearning systems",
abstract = "The research presented in this paper proposes a new machine learning-based evaluation method for assessing the usability of eLearning systems. Three machine learning methods (support vector machines, neural networks and decision trees) along with multiple linear regression are used to develop prediction models in order to discover the underlying relationship between the overall eLearning system usability and its predictor factors. A subsequent sensitivity analysis is conducted to determine the rank-order importance of the predictors. Using both sensitivity values along with the usability scores, a metric (called severity index) is devised. By applying a Pareto-like analysis, the severity index values are ranked and the most important usability characteristics are identified. The case study results show that the proposed methodology enhances the determination of eLearning system problems by identifying the most pertinent usability factors. The proposed method could provide an invaluable guidance to the usability experts as to what measures should be improved in order to maximize the system usability for a targeted group of end-users of an eLearning system.",
keywords = "eLearning (web-based learning/distance learning), Information fusion, Machine learning, Sensitivity analysis, Severity index, Usability engineering",
author = "Asil Oztekin and Dursun Delen and Ali Turkyilmaz and Selim Zaim",
note = "Funding Information: Dr. Asil Oztekin is an Assistant Professor of Operations and Information Systems Department, Manning School of Business at University of Massachusetts Lowell. He received the B.S. degree from Yildiz Technical University, Istanbul, Turkey, in 2004, and the M.S. degree from Fatih University, Istanbul, Turkey, in 2006, both in industrial engineering. He also completed the Ph.D. degree in the School of Industrial Engineering and Management at Oklahoma State University (OSU), Stillwater, in 2010. Prior to joining UMass Lowell, he worked as a Visiting Assistant Professor in the Department of Statistics at Oklahoma State University. His research interests include human–computer interaction, medical informatics, healthcare engineering, decision analysis, multivariate data analysis, data mining, and quality engineering. His work has been published in leading journals such as Decision Support Systems, International Journal of Production Research, Production Planning & Control, International Journal of Medical Informatics, Artificial Intelligence in Medicine and International Journal of Industrial Ergonomics. He edited a special issue as a guest editor entitled “Intelligent Computational Techniques in Science, Engineering, and Business” in Expert Systems with Applications journal. Dr. Oztekin is serving as an editorial review board member of the for the Journal of Computer Information Systems, International Journal of Services and Operations Management, International Journal of Operations Research and Information Systems, Journal of Modelling in Management, International Journal of Data Analysis Techniques & Strategies, International Journal of Business Analytics, and for International Journal of Business Intelligence and Systems Engineering. His research in medical decision making has been recently funded by the Advancing Research, Scholarship, and Creative Work Grant at UMass Lowell. Dr. Oztekin has served as the session chair for the mini-tracks “Predictive Analytics and Big Data” at the HICSS 46 and “Data, Text, and Web Mining for Managerial Decision Support” at the HICSS 47. He is a member of HIMMS, ASQ, IIE, and INFORMS and was the recipient of the Alpha Pi Mu Outstanding Industrial Engineering and Management Research Assistant Award from OSU in 2009. Copyright: Copyright 2013 Elsevier B.V., All rights reserved.",
year = "2013",
month = dec,
day = "1",
doi = "10.1016/j.dss.2013.05.003",
language = "English",
volume = "56",
pages = "63--73",
journal = "Decision Support Systems",
issn = "0167-9236",
publisher = "Elsevier B.V.",
number = "1",
}