Cluster Computing

, Volume 21, Issue 1, pp 1045–1058 | Cite as

An adaptable UI/UX considering user’s cognitive and behavior information in distributed environment

  • Hyesung Ji
  • Youdong Yun
  • Seolhwa Lee
  • Kuekyeng Kim
  • Heuiseok LimEmail author


User interface (UI) and user experience (UX) is the first thing users come to interact when accessing internet services. However, UI/UX, which did not put users into consideration when designed, causes many inconveniences. In order to provide a customized UI/UX, the accurate analysis of users are important and must be optimized for users through continuous updates. In this paper, we propose a method of analyzing the user’s cognitive and behavioral information in a distributed environment, providing a customized UI/UX based on the analysis. The proposed method measures the user’s cognitive ability and generates an initial profile, then provides a custom UI/UX by modifying the profile based on the user’s behavioral information. To generate the user profile, the cognitive response measurement and modeling of 122 users were performed and the proposed model was evaluated by 200 users. As a result of the experiment, overall satisfying results were obtained.


User interface User experience Personalized service User model Cognitive User behavior 



This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning (NRF-2016R1A2B2015912).


  1. 1.
    Dery-Pinna, A.M., Fierstone, J., Picard, E.: Component model and programming: a first step to manage human–computer interaction adaptation. Int. Conf. Mob. Hum. Comput. Interact. 2795, 456–460 (2003)Google Scholar
  2. 2.
    Park, R.C., Jung, H., Shin, D.K., Kim, G.J., Yoon, K.H.: M2M-based smart health service for human UI/UX using motion recognition. Clust. Comput. 18(1), 221–232 (2015)CrossRefGoogle Scholar
  3. 3.
    Park, J., Han, S.H., Kim, H.K., Cho, Y., Park, W.: Developing elements of user experience for mobile phones and services: survey, interview, and observation approaches. Hum. Factors Ergon. Manuf. Serv. Ind. 23(4), 279–293 (2013)CrossRefGoogle Scholar
  4. 4.
    Voutilainen, J.P., Salonen, J., Mikkonen, T.: On the design of a responsive user interface for a multi-device web service. In: Proceedings of the Second ACM International Conference on Mobile Software Engineering and Systems, pp. 60–63 (2015)Google Scholar
  5. 5.
    Moon, J., Lim, T.B., Kim, K.W., Lee, S.P., Lee, S.: Advanced responsive web framework based on MPEG-21. In: Proceedings of the IEEE International Conference, pp. 197–199. Consumer Electronics, Berlin (2012)Google Scholar
  6. 6.
    Rajpal, K.S., Kaur, M.: Automated UI & UX framework. Int. J. Adv. Res. Ideas Innov. Technol. 3, 16–22 (2017)Google Scholar
  7. 7.
    Park, H.S., Kim, H.W., Park, C.J.: Dynamic-interaction UI/UX design for the AREIS. Proc. Int. Conf. Hum. Comput. Intera. 9732, 412–418 (2016)Google Scholar
  8. 8.
    Kim, Y., Kwak, M., Kim, E.: The development of the user-customizable favorites-based smart phone UX/UI using tap pattern similarity. J. Korea. Soc. Comput. 19(8), 95–106 (2014)Google Scholar
  9. 9.
    Chen, X.A., Grossman, T., Wigdor, D.J. Fitzmaurice, G.: Duet: exploring joint interactions on a smart phone and a smart watch. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 159–168. (2014)Google Scholar
  10. 10.
    Hooshyar, D., Ahmad, R.B., Yousefi, M., Fathi, M., Horng, S.J., Lim, H.: Applying an online game-based formative assessment in a flowchart-based intelligent tutoring system for improving problem-solving skills. Comput. Educ. 94, 18–36 (2016)CrossRefGoogle Scholar
  11. 11.
    Hooshyar, D., Ahmad, R.B., Yousefi, M., Fathi, M., Horng, S.J., Lim, H.: SITS: a solution-based intelligent tutoring system for students’ acquisition of problem-solving skills in computer programming. Innov. Educ. Teach. Int. (2016). doi: 10.1080/14703297.2016.1189346
  12. 12.
    Hooshyar, D., Yousefi, M., Lim, H.: A Procedural Content Generation-Based Framework for Educational Games: Toward a Tailored Data-Driven Game for Developing Early English Reading Skills. J. Educ. Compu. Res. (2017). doi: 10.1177/0735633117706909
  13. 13.
    Timmann, D., Drepper, J., Frings, M., Maschke, M., Richter, S., Gerwig, M.E.E.A., Kolb, F.P.: The human cerebellum contributes to motor, emotional and cognitive associative learning. A review. Cortex 46(7), 845–857 (2010)CrossRefGoogle Scholar
  14. 14.
    Keele, Steven W.: Movement control in skilled motor performance. Psychol. Bull. 70(6), 387 (1968)CrossRefGoogle Scholar
  15. 15.
    Caplan, B.: Edinburgh handedness inventory. Encyclopedia of clinical neuropsychology. Springer, New York (2011)Google Scholar
  16. 16.
    Harrington, M., Sawyer, M.: L2 working memory capacity and L2 reading skill. Stud. Secon. Lang. Acquis. 14(1), 25–38 (1992)CrossRefGoogle Scholar
  17. 17.
    Dannenbring, G.L., Briand, K.: Semantic priming and the word repetition effect in a lexical decision task. Can. J. Psychol. 36(3), 435 (1982)CrossRefGoogle Scholar
  18. 18.
    Newman, S.D., Carpenter, P.A., Varma, S., Just, M.: Frontal and parietal participation in problem solving in the Tower of London: fMRI and computational modeling of planning and high-level perception. Neuropsychologia 41(12), 1668–1682 (2003)CrossRefGoogle Scholar
  19. 19.
    Kimura, D.: Spatial localization in left and right visual fields. Can. J. Psychol. 23(6), 445 (1969)CrossRefGoogle Scholar
  20. 20.
    Lee, B.J., Kwon, J.S., Go, G.C., Choi, Y.L.: A method for analyzing web log of the hadoop system for analyzing a effective pattern of web ysers. J. Korea Soc. IT serv. 13(4), 231–243 (2014)CrossRefGoogle Scholar
  21. 21.
    Choi, S.I., Kim, N.G.: Identifying the interests of web category visitors using topic analysis. J. Inf. Technol. Appl. Manage. 21(4), 415–429 (2014)Google Scholar
  22. 22.
    Lee, H.J.: An analysis on communication behaviors performed by digital photography community users–focusing on the photo gallery service, NAVER. Yonsei University Graduate School of Journalism and Broadcasting Master’s Thesis (2011)Google Scholar
  23. 23.
    Lee, D.C., Lee, E.J., Kim, B.S., Jin, G.O.: Study of influencing factors in internet shopping of the consumer’s purchase intention. Manage. Inf. Syst. Rev. 30(1), 211–226 (2011)Google Scholar
  24. 24.
    Jang, S.H.: Web site analysis using data mining: around web log analysis. Korea University Graduate School of Computer and Information System Master’s Thesis (2010)Google Scholar
  25. 25.
    Park, K., Lim, H.: Acquiring lexical knowledge using raw corpora and unsupervised clustering method. Clust. comput. 17(3), 901–910 (2014)CrossRefGoogle Scholar
  26. 26.
    Piga, L., Bergamaschi, R.A., Rigo, S.: Empirical and analytical approaches for web server power modeling. Clust. Comput. 17(4), 1279–1293 (2014)CrossRefGoogle Scholar
  27. 27.
    Lee, S., Hooshyar, D., Ji, H., Nam, K., Lim, H.: Mining biometric data to predict programmer expertise and task difficulty. Clust. Comput. 22, 1–11 (2017)Google Scholar
  28. 28.
    Islam, M.J., Wu, Q.J., Ahmadi, M., Sid-Ahmed, M.A.: Investigating the performance of naive-bayes classifiers and k-nearest neighbor classifiers. J. Conv. Inf. Technol. 5(2), 133–137 (2007)Google Scholar
  29. 29.
    Pal, M., Mather, P.M.: An assessment of the effectiveness of decision tree methods for land cover classification. Remot. Sens. Env. 86(4), 554–565 (2003)CrossRefGoogle Scholar
  30. 30.
    Mazurowski, M.A., Habas, P.A., Zurada, J.M., Lo, J.Y., Baker, J.A., Tourassi, G.D.: Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural Netw. 21(2), 427–436 (2008)CrossRefGoogle Scholar
  31. 31.
    Saxena, A., Saad, A.: Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems. Appl. Soft Comput. 7(1), 441–454 (2007)CrossRefGoogle Scholar
  32. 32.
    Mavroforakis, M.E., Theodoridis, S.: A geometric approach to support vector machine (SVM) classification. IEEE Transact. Neural Netw. 17(3), 671–682 (2006)CrossRefGoogle Scholar
  33. 33.
    Moreno, P.J., Ho, P.P., Vasconcelos, N.: A Kullback-Leibler divergence based kernel for SVM classification in multimedia applications. Advances in neural information processing systems. MIT Press, Cmabridge (2003)Google Scholar
  34. 34.
    Yousefi, M.: An agent-based simulation combined with group decision-making technique for improving the performance of an emergency department. Braz. J. Med. Biol. 50(5), e5955 (2017)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Hyesung Ji
    • 1
  • Youdong Yun
    • 1
  • Seolhwa Lee
    • 1
  • Kuekyeng Kim
    • 1
  • Heuiseok Lim
    • 1
    Email author
  1. 1.Department of Computer Science and EngineeringKorea UniversitySeoulSouth Korea

Personalised recommendations