A machine learning-based usability evaluation method for eLearning systems

Asil Oztekin, Dursun Delen, Ali Turkyilmaz, Selim Zaim

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)63-73
Number of pages11
JournalDecision Support Systems
Issue number1
Publication statusPublished - Dec 2013


  • Information fusion
  • Machine learning
  • Sensitivity analysis
  • Severity index
  • Usability engineering
  • eLearning (web-based learning/distance learning)

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

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