Looking for Usability and Functionality Issues: A Case Study

  • Karina Jiménes
  • Jhonny PincayEmail author
  • Mónica Villavicencio
  • Alberto Jiménez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 721)


Looking for quality issues in a system can be a very demanding activity. In this article, we propose an approach based on text mining techniques to quickly identify usability and functionality drawbacks in a learning management system - LMS. The techniques were performed to 421 comments written by university students who frequently use a LMS. Results indicate that a dendrogram is a suitable tool to have a quick look of the issues faced by LMS’ users as well as their expectations about new functionalities that the system should provide. By using these techniques, we identified more than ten usability issues and the need for seven new functionalities to be implemented in the system.


Software engineering Text mining Dendrogram Usability Functionality LMS 


  1. 1.
    Alla, M., et al.: The impact of system quality in e-learning system. J. Comput. Sci. Inf. Technol. 1(2), 14–23 (2013)MathSciNetGoogle Scholar
  2. 2.
    ALMazroui, Y.A.: A survey of Data mining in the context of E-learning. Int. J. Inf. Technol. Comput. Sci. 7(3), 8–18 (2013)Google Scholar
  3. 3.
    Alturki, U.T., et al.: Evaluating the usability and accessibility of LMS “Blackboard” at King Saud University. Contemp. Issues Educ. Res. Online 9(1), 33 (2016)CrossRefGoogle Scholar
  4. 4.
    Azam, N., Yao, J.: Comparison of term frequency and document frequency based feature selection metrics in text categorization. Expert Syst. Appl. 39(5), 4760–4768 (2012)CrossRefGoogle Scholar
  5. 5.
    Cheng, B., et al.: Research on e-learning in the workplace 2000–2012: a bibliometric analysis of the literature. Educ. Res. Rev. 11, 56–72 (2014)CrossRefGoogle Scholar
  6. 6.
    Cios, K.J., et al.: Data Mining: A Knowledge Discovery Approach. Springer Science & Business Media, New York (2007)zbMATHGoogle Scholar
  7. 7.
    Dumas, J.S., Fox, J.E.: Usability testing: current practice and future directions. Hum.-Comput. Interact. Dev. Process. 231, 1–7 (2009)Google Scholar
  8. 8.
    El-Halees, A.M.: Software usability evaluation using opinion mining. JSW 9, 2 (2014)CrossRefGoogle Scholar
  9. 9.
    Freire, L., et al.: A literature review about usability evaluation methods for e-learning platforms (2012).
  10. 10.
    ISO/IEC: ISO/IEC 25010:2011 - System and Software Quality Models.!iso:std:35733:en
  11. 11.
    Joachims, T.: A probabilistic analysis of the Rocchio Algorithm with TFIDF for text categorization. DTIC Document (1996)Google Scholar
  12. 12.
    Junus, I.S., et al.: Usability evaluation of the student centered e-learning environment. Int. Rev. Res. Open Distrib. Learn. 16, 4 (2015)Google Scholar
  13. 13.
    Kiget, N.K., et al.: Evaluating usability of e-learning systems in universities. Int. J. Adv. Comput. Sci. Appl. 5, 97–102 (2014)Google Scholar
  14. 14.
    Masood, M., Musman, A.: The usability and its influence of an e-learning system on student participation. Procedia-Soc. Behav. Sci. 197, 2325–2330 (2015)CrossRefGoogle Scholar
  15. 15.
    Murtagh, F., Legendre, P.: Ward’s hierarchical clustering method: clustering criterion and agglomerative algorithm. arXiv Preprint arXiv:11116285 (2011)
  16. 16.
    Nielsen, J.: Designing Web Usability: The Practice of Simplicity. New Riders Publishing, Indianapolis (1999)Google Scholar
  17. 17.
    Orfanou, K., et al.: Perceived usability evaluation of learning management systems: empirical evaluation of the system usability scale. Int. Rev. Res. Open Distrib. Learn. 16, 2 (2015)Google Scholar
  18. 18.
    Oztekin, A., et al.: A machine learning-based usability evaluation method for eLearning systems. Decis. Support Syst. 56, 63–73 (2013)CrossRefGoogle Scholar
  19. 19.
    Pincay, J., Ochoa, X.: Automatic classification of answers to discussion forums according to the cognitive domain of blooms taxonomy using text mining and a Bayesian classifier. In: Proceedings of EdMedia Conference, pp. 626–634 (2013)Google Scholar
  20. 20.
    Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24(5), 513–523 (1988)CrossRefGoogle Scholar
  21. 21.
    Shah, F., Pfahl, D.: Evaluating and improving software quality using text analysis techniques-a mapping study. In: REFSQ Workshops (2016)Google Scholar
  22. 22.
    Shen, C., et al.: Integrating clustering and multi-document summarization by bi-mixture probabilistic latent semantic analysis (PLSA) with sentence bases. In: AAAI (2011)Google Scholar
  23. 23.
    Thacker, J., et al.: Learning Management System Comparative Usability Study (2014)Google Scholar
  24. 24.
    Tripathy, P., Tripathy, P.K.: Fundamentals of Research: A Dissective View (2015)Google Scholar
  25. 25.
    Zhang, Y., et al.: Learning the semantic correlation: an alternative way to gain from unlabeled text. In: Advances in Neural Information Processing Systems, pp. 1945–1952 (2009)Google Scholar
  26. 26.
    Rocha, Á.: Framework for a global quality evaluation of a website. Online Inf. Rev. 36(3), 374–382 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Karina Jiménes
    • 1
  • Jhonny Pincay
    • 1
    Email author
  • Mónica Villavicencio
    • 1
  • Alberto Jiménez
    • 1
  1. 1.Facultad de Ingeniería en Electricidad y ComputaciónEscuela Superior Politécnica del Litoral, ESPOLGuayaquilEcuador

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