Advertisement

ShowMeHow: Using Smart, Interactive Tutorials in Elderly Software Development

  • Drew WilliamsEmail author
  • Mong-Te Wang
  • Chih-Hung Chang
  • Sheikh Iqbal Ahamed
  • William Chu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8456)

Abstract

Many elderly users fail to use technology simply because of the fear of failure, and perceiving themselves as “too old” to use new technology. Studies show that coaching is helpful in assisting the elderly with learning new technology, but unfortunately many do not like to ask for help. We propose the development of smart tutorials that detect user frustration by tracking various affective markers, communicate with the user clearly and offer customized tutorials for the users’ convenience. We hope that such a system will benefit the elderly by giving them on-demand, customized assistance while allowing them to retain their independence.

Keywords

Human computer interfaces Elderly people Tutorials Intelligent interfaces 

Notes

Acknowledgements

This work is sponsored by TUNGHAI UNIVERSITY ‘The U-Care ICT Integration Platform for the Elderly – No.102 GREEnS004 – 2’, 2013.

References

  1. 1.
    Alm, N., Gregor, P., Newell, A.F.: Older people and information technology are ideal partners. In: Proceedings of the International Conference for Universal Design (UD2002), Yokohoma, Japan, pp. 1–7 (2002)Google Scholar
  2. 2.
    Williams, D., Ul Alam, M.A., Ahamed, S.I., Chu, W.: Considerations in designing human-computer interfaces for elderly people. In: 2013 13th International Conference on Quality Software (QSIC), pp. 372–377. IEEE (2013)Google Scholar
  3. 3.
    Kantner, L., Rosenbaum, S.: Usable computers for the elderly: applying coaching experiences. In: Proceedings of the 2003 IEEE International Professional Communication Conference, pp. 92–101. IEEE (2003)Google Scholar
  4. 4.
    Durick, J., Robertson, T., Brereton, M., Vetere, F., Nansen, B.: Dispelling ageing myths in technology design. In: Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration, pp. 467–476. ACM, New York (2013)Google Scholar
  5. 5.
    Gajos, K., Christianson, D., Hoffmann, R., Shaked, T., Henning, K., Long, J.J., Weld, D.S.: Fast and robust interface generation for ubiquitous applications. In: Beigl, M., Intille, S.S., Rekimoto, J., Tokuda, H. (eds.) UbiComp 2005. LNCS, vol. 3660, pp. 37–55. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Peissner, M., Schuller, A., Spath, D.: A design patterns approach to adaptive user interfaces for users with special needs. In: Jacko, J.A. (ed.) Human-Computer Interaction, Part I, HCII 2011. LNCS, vol. 6761, pp. 268–277. Springer, Heidelberg (2011)Google Scholar
  7. 7.
    Magee, J., Betke, M.: HAIL: hierarchical adaptive interface layout. In: Miesenberger, K., Klaus, J., Zagler, W., Karshmer, A. (eds.) ICCHP 2010, Part 1. LNCS, vol. 6179, pp. 139–146. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Sarrafzadeh, A., Alexander, S., Dadgostar, F., Fan, C., Bigdeli, A.: How do you know that I don’t understand? A look at the future of intelligent tutoring systems. Comput. Hum. Behav. 24(4), 1342–1363 (2008)CrossRefGoogle Scholar
  9. 9.
    Hunter, A., Sayers, H., McDaid, L.: An evolvable computer interface for elderly users. In: Support Human Memory with Interactive Systems; HCI Conference, Lancaster, UK, pp. 29–32 (2007)Google Scholar
  10. 10.
    Bixler, R., D’Mello, S.: Detecting boredom and engagement during writing with keystroke analysis, task appraisals, and stable traits. In: Proceedings of the 2013 International Conference on Intelligent User Interfaces - IUI ’13, pp. 225–234. ACM Press, New York (2013)Google Scholar
  11. 11.
    Asai, H., Yamana, H.: Detecting student frustration based on handwriting behavior. In: Proceedings of the Adjunct Publication of the 26th Annual ACM Symposium on User Interface Software and Technology - UIST ’13 Adjunct, pp. 77–78. ACM Press, New York (2013)Google Scholar
  12. 12.
    Obdrzalek, S.A.N., Kurillo, G., Ofli, F., Bajcsy, R., Seto, E., Jimison, H., Pavel, M.: Accuracy and robustness of Kinect pose estimation in the context of coaching of elderly population. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1188–1193. The Printing House, Wisconsin (2012)Google Scholar
  13. 13.
    Tan, C.S.S., Schoning, J., Luyten, K., Coninx, K.: Informing intelligent user interfaces by inferring affective states from body postures in ubiquitous computing environments. In: Proceedings of the 2013 International Conference on Intelligent User Interfaces - IUI ’13, pp. 235–246. ACM Press, New York (2013)Google Scholar
  14. 14.
    Sato, D., Kobayashi, M., Takagi, H., Asakawa, C., Tanaka, J.: How voice augmentation supports elderly web users. In: The Proceedings of the 13th International ACM SIGACCESS Conference on Computers and Accessibility - ASSETS ’11, pp. 155–162. ACM Press, New York (2011)Google Scholar
  15. 15.
    Mantoro, T., Johnson, C.W., Ayu, M.A.: A framework in ubiquitous computing environment for providing intelligent responses. In: 2009 Third International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, pp. 289–294. IEEE Computer Society, Washington (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Drew Williams
    • 1
    Email author
  • Mong-Te Wang
    • 2
  • Chih-Hung Chang
    • 3
  • Sheikh Iqbal Ahamed
    • 1
  • William Chu
    • 2
  1. 1.Department of Mathematics, Statistics and Computer ScienceMarquette UniversityMilwaukeeUSA
  2. 2.Department of Computer ScienceTunghai UniversityTaichung CityTaiwan
  3. 3.Department of Computer Science and Information EngineeringHsiuping University of Science and TechnologyTaichung CityTaiwan

Personalised recommendations