A machine learning-based usability evaluation method for eLearning systems

  • Authors:

  • Asil Oztekin;Dursun Delen;Ali Turkyilmaz;Selim Zaim

  • Affiliations:

  • Department of Operations and Information Systems, Manning School of Business, University of Massachusetts Lowell, Lowell, MA 01854, USA;Department of Management Science and Information Systems, Spears School of Business, Oklahoma State University, Tulsa, OK 74106, USA;Department of Industrial Engineering, College of Engineering, Fatih University, Buyukcekmece, 34500 Istanbul, Turkey;Department of Mechanical Engineering, College of Technology, Marmara University, Kadikoy, 34722 Istanbul, Turkey

  • Venue:
  • Decision Support Systems
  • Year:
  • 2013

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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.