Advertisement

Question-Answer Cards for an Inclusive Micro-tasking Framework for the Elderly

  • Masatomo Kobayashi
  • Tatsuya Ishihara
  • Akihiro Kosugi
  • Hironobu Takagi
  • Chieko Asakawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8119)

Abstract

Micro-tasking (e.g., crowdsourcing) has the potential to help “long-tail” senior workers utilize their knowledge and experience to contribute to their communities. However, their limited ICT skills and their concerns about new technologies can prevent them from participating in emerging work scenarios. We have devised a question-answer card interface to allow the elderly to participate in micro-tasks with minimal ICT skills and learning efforts. Our survey identified a need for skill-based task recommendations, so we also added a probabilistic skill assessment model based on the results of the micro-tasks. We also discuss some scenarios to exploit the question-answer card framework to create new work opportunities for senior citizens. Our experiments showed that untrained seniors performed the micro-tasks effectively with our interface in both controlled and realistic conditions, and the differences in their skills were reliably assessed.

Keywords

Micro-Tasks Gamification Skill Assessment Ageing Elderly Senior Workforce 

References

  1. 1.
    Statistics Bureau, Monthly Report, http://www.stat.go.jp/english/data/jinsui/tsuki/ (retrieved April 15, 2013)
  2. 2.
    Ouchi, Y., Akiyama, H. (eds.): Gerontology – Overview and Perspectives, 3rd edn. Univ. of Tokyo Press (2010) (in Japanese)Google Scholar
  3. 3.
    United Nations University, Active Ageing, http://wisdom.unu.edu/en/active-aging
  4. 4.
    Leibold, M., Voelpel, S.: Managing the Aging Workforce. John Wiley & Sons (2006)Google Scholar
  5. 5.
    Loten, A.: Small firms, Start-ups Drive Crowdsourcing Growth. Wall Street Journal (February 28, 2012)Google Scholar
  6. 6.
    Ross, J., Irani, L., Silberman, M.S., Zaldivar, A., Tomlinson, B.: Who are the Crowdworkers? Shifting Demographics in Mechanical Turk. In: Proc. CHI EA 2010, pp. 2863–2872. ACM (2010)Google Scholar
  7. 7.
    Hiyama, A., Nagai, Y., Kobayashi, M., Takagi, H., Hirose, M.: Question First: Passive Interaction Model for Gathering Experience and Knowledge from the Elderly. In: Proc. PerCol 2013, pp. 151–156. IEEE (2013)Google Scholar
  8. 8.
    Office for National Statistics: Use of ICT at Home (2007)Google Scholar
  9. 9.
    Kurniawan, S.: Older People and Mobile Phones: A Multi-Method Investigation. Int. J. Hum.-Comput. Stud. 66(12), 889–901 (2008)CrossRefGoogle Scholar
  10. 10.
    Leung, R., Tang, C., Haddad, S., McGrenere, J., Graf, P., Ingriany, V.: How Older Adults Learn to Use Mobile Devices: Survey and Field Investigations. ACM Trans. Access. Comput. 4(3), Article 11 (2012)Google Scholar
  11. 11.
    Bigham, J.P., Jayant, C., Ji, H., Little, G., Miller, A., Miller, R.C., Miller, R., Tatarowicz, A., White, B., White, S., Yeh, T.: VizWiz: Nearly Real-Time Answers to Visual Questions. In: Proc. UIST 2010, pp. 333–342. ACM (2010)Google Scholar
  12. 12.
    von Ahn, L., Maurer, B., McMillen, C., Abraham, D., Blum, M.: reCAPTCHA: Human-based Character Recognition via Web Security Measures. Science 321(5895), 1465–1468 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Callison-Burch, C.: Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon’s Mechanical Turk. In: Proc. EMNLP 2009, pp. 286–295. ACL and AFNLP (2009)Google Scholar
  14. 14.
    Kittur, A., Chi, E.H., Suh, B.: Crowdsourcing User Studies with Mechanical Turk. In: Proc. CHI 2008, pp. 453–456. ACM (2008)Google Scholar
  15. 15.
    Guy, I., Perer, A., Daniel, T., Greenshpan, O., Turbahn, I.: Guess Who? Enriching the Social Graph through a Crowdsourcing Game. In: Proc. CHI 2011, pp. 1373–1382. ACM (2011)Google Scholar
  16. 16.
    Kulkarni, A., Can, M., Hartmann, B.: Collaboratively Crowdsourcing Workflows with Turkomatic. In: Proc. CSCW 2012, pp. 1003–1012. ACM (2012)Google Scholar
  17. 17.
    Bernstein, M.S., Little, G., Miller, R.C., Hartmann, B., Ackerman, M.S., Karger, D.R., Crowell, D., Panovich, K.: Soylent: A Word Processor with a Crowd Inside. In: Proc. UIST 2010, pp. 313–322. ACM (2010)Google Scholar
  18. 18.
    Noronha, J., Hysen, E., Zhang, H., Gajos, K.Z.: PlateMate: Crowdsourcing Nutritional Analysis from Food Photographs. In: Proc. UIST 2011, pp. 1–12. ACM (2011)Google Scholar
  19. 19.
    Leonardi, C., Albertini, A., Pianesi, F., Zancanaro, M.: An Exploratory Study of a Touch-based Gestural Interface for Elderly. In: Proc. NordiCHI 2010, pp. 845–850. ACM (2010)Google Scholar
  20. 20.
    Kobayashi, M., Hiyama, A., Miura, T., Asakawa, C., Hirose, M., Ifukube, T.: Elderly User Evaluation of Mobile Touchscreen Interactions. In: Campos, P., Graham, N., Jorge, J., Nunes, N., Palanque, P., Winckler, M. (eds.) INTERACT 2011, Part I. LNCS, vol. 6946, pp. 83–99. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Web Accessibility and Older People: Meeting the Needs of Ageing Web Users, http://www.w3.org/WAI/older-users/
  22. 22.
  23. 23.
    Whitehill, J., Ruvolo, P., Wu, T., Bergsma, J., Movellan, J.: Whose Vote should Count More? Optimal Integration of Labels from Labelers of Unknown Expertise. In: Proc. NIPS 2009, pp. 2035–2043 (2009)Google Scholar
  24. 24.
    Heimerl, K., Gawalt, B., Chen, K., Parikh, T., Hartmann, B.: CommunitySourcing: Engaging Local Crowds to Perform Expert Work via Physical Kiosks. In: Proc. CHI 2012, pp. 1539–1548. ACM (2012)Google Scholar
  25. 25.
    Macdonald, C., Ounis, I.: Voting for Candidates: Adapting Data Fusion Techniques for an Expert Search Task. In: Proc. CIKM 2006, pp. 387–396. ACM (2006)Google Scholar
  26. 26.
    Guy, I., Jacovi, M., Shahar, E., Meshulam, N., Soroka, V., Farrell, S.: Harvesting with SONAR: The Value of Aggregating Social Network Information. In: Proc. CHI 2008, pp. 1017–1026. ACM (2008)Google Scholar
  27. 27.
    Pfeil, U., Arjan, R., Zaphiris, P.: Age Differences in Online Social Networking – A Study of User Profiles and the Social Capital Divide among Teenagers and Older Users in MySpace. Comput. Hum. Behav. 25(3), 643–654 (2009)CrossRefGoogle Scholar
  28. 28.
  29. 29.
    Yeh, M.-C., Tai, J.: A Hierarchical Approach to Practical Beverage Package Recognition. In: Ho, Y.-S. (ed.) PSIVT 2011, Part I. LNCS, vol. 7087, pp. 348–357. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  30. 30.
    Kobayashi, M., Ishihara, T., Itoko, T., Takagi, H., Asakawa, C.: Age-based Task Specialization for Crowdsourced Proofreading. In: Stephanidis, C., Antona, M. (eds.) UAHCI/HCII 2013, Part II. LNCS, vol. 8010, pp. 104–112. Springer, Heidelberg (2013)Google Scholar
  31. 31.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating Collaborative Filtering Recommender Systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar
  32. 32.
    Yuen, M.-C., King, I., Leung, K.-S.: TaskRec: Probabilistic Matrix Factorization in Task Recommendation in Crowdsourcing Systems. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part II. LNCS, vol. 7664, pp. 516–525. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  33. 33.
    Watanabe, K., Matsuda, S., Yasumura, M., Inami, M., Igarashi, T.: CastOven: A Microwave Oven with Just-in-Time Video Clips. In: Proc. Ubicomp 2010 Adjunct, pp. 385–386. ACM (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Masatomo Kobayashi
    • 1
  • Tatsuya Ishihara
    • 1
  • Akihiro Kosugi
    • 1
  • Hironobu Takagi
    • 1
  • Chieko Asakawa
    • 1
  1. 1.IBM Research – TokyoKotoJapan

Personalised recommendations