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Leveraging Children’s Behavioral Distribution and Singularities in New Interactive Environments: Study in Kindergarten Field Trips

  • Inseok Hwang
  • Hyukjae Jang
  • Taiwoo Park
  • Aram Choi
  • Youngki Lee
  • Chanyou Hwang
  • Yanggui Choi
  • Lama Nachman
  • Junehwa Song
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7319)

Abstract

The behavior observations on young children in new, first-in-the-life environments have significant implications. We can often uniquely observe a child’s unforeseen interaction with the environment and peer-children. It would be not only a piece of discovery but a beginning of an open quest worth exploring. Out-of-classroom activities like kindergarten’s field trips are perfect opportunities, but those are quite different from regular classroom activities where the teachers’ conventional observation methods are hardly practical. This paper proposes a novel approach to extend the teachers’ awareness on the children’s field trip behaviors by means of mobile and sensor technology. We adopt the notion of behavioral distribution and singularities. We estimate the children’s representative behavioral state in a given context, and study the effect of focusing on the behaviors which are unlikely in this context. We discuss our 14-month collaborative study and various qualitative benefits through multiple deployments on actual kindergarten field trips.

Keywords

Behavior distribution singularity children kindergarten field trip smartphone sensor 

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References

  1. 1.
    Chang, Y.-C., Lo, J.-L., Huang, C.-J., Hsu, N.-Y., Chu, H.-H., Wang, H.-Y., Chi, P.-Y., Hsieh, Y.-L.: Playful Toothbrush: Ubicomp Technology for Teaching Tooth Brushing to Kindergarten Children. In: CHI 2008, pp. 363–372. ACM, New York (2008)CrossRefGoogle Scholar
  2. 2.
    Chipman, G., Druin, A., Beer, D., Fails, J.A., Guha, M.L., Simms, S.: A Case Study of Tangible Flags: A Collaborative Technology to Enhance Field Trips. In: Proc. IDC 2006, pp. 1–8. ACM, New York (2006)CrossRefGoogle Scholar
  3. 3.
    Cramer, M., Hayes, G.R.: Acceptable Use of Technology in Schools. Pervasive Computing 9(3), 37–44 (2010)CrossRefGoogle Scholar
  4. 4.
    Dodge, K.A.: Behavioral Antecedents of Peer Social Status. Child Development 54(6), 1386–1399 (1983)CrossRefGoogle Scholar
  5. 5.
    Druin, A.: The Role of Children in the Design of New Technology. Behavior and Information Technology 21(1), 1–25 (2002)Google Scholar
  6. 6.
  7. 7.
    Falk, J.H., Dierking, L.D.: School Field Trips: Assessing Their Long-Term Impact. Curator 40(3), 211–218 (1997)CrossRefGoogle Scholar
  8. 8.
    Hayes, G.R., Gardere, L.M., Abowd, G.D., Truong, K.N.: CareLog: a Selective Archiving Tool for Behavior Management in Schools. In: CHI 2008, pp. 685–694. ACM (2008)Google Scholar
  9. 9.
    Hayes, G.R., Kientz, J.A., Truong, K.N., White, D.R., Abowd, G.D., Pering, T.: Designing Capture Applications to Support the Education of Children with Autism. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, pp. 161–178. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Hodges, S., Williams, L., Berry, E., Izadi, S., Srinivasan, J., Butler, A., Smyth, G., Kapur, N., Wood, K.: SenseCam: A Retrospective Memory Aid. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 177–193. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Hourcade, J.P.: Interaction Design and Children. Foundations and Trends in HCI 1(4), 277–392 (2007)Google Scholar
  12. 12.
    Hwang, I., Jang, H., Nachman, L., Song, J.: Exploring Inter-child Behavioral Relativity in a Shared Social Environment: A Field Study in a Kindergarten. In: UbiComp 2010, pp. 271–280. ACM, New York (2010)CrossRefGoogle Scholar
  13. 13.
    International Commission on Non-Ionizing Radiation Protection. Guidelines for limiting exposure to time-varying electric, magnetic, and electromagnetic fields (up to 300GHz). Health Physics 74(4), 494–522 (1998) Google Scholar
  14. 14.
    Kientz, J.A., Abowd, G.D.: KidCam: Toward an Effective Technology for the Capture of Children’s Moments of Interest. In: Tokuda, H., Beigl, M., Friday, A., Brush, A.J.B., Tobe, Y. (eds.) Pervasive 2009. LNCS, vol. 5538, pp. 115–132. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Kientz, J.A., Hayes, G.R., Westeyn, T.L., Starner, T., Abowd, G.D.: Pervasive computing and autism: Assisting caregivers of children with special needs. IEEE Pervasive Computing 6(1), 28–35 (2007)CrossRefGoogle Scholar
  16. 16.
    Lane, N.D., Xu, Y., Lu, H., Hu, S., Choudhury, T., Campbell, A.T., Zhao, F.: Enabling Large-scale Human Activity Inference on Smartphones using Community Similarity Networks (CSN). In: UbiComp 2011, pp. 355–364. ACM, New York (2011)Google Scholar
  17. 17.
    MacKenzie, A.A., White, R.T.: Fieldwork in Geography and Long-Term Memory Structures. American Educational Research Journal 19(4), 623–632 (1982)Google Scholar
  18. 18.
    Madan, A., Cebrian, M., Lazer, D., Pentland, A.: Social Sensing for Epidemological Behavior Change. In: UbiComp 2010, pp. 291–300. ACM, New York (2010)CrossRefGoogle Scholar
  19. 19.
    Martinez, R., Kay, J., Wallace, J.R., Yacef, K.: Modelling Symmetry of Activity as an Indicator of Collocated Group Collaboration. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 207–218. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  20. 20.
    Olguin, D.O., Waber, B.N., Kim, T., Mohan, A., Ara, K., Pentland, A.: Sensible Organization: Technology and Methodology for Automatically Measuring Organizational Behavior. IEEE Trans. Systems, Man, and Cybernetics 39(1), 43–55 (2009)CrossRefGoogle Scholar
  21. 21.
    Overy, K., Nicolson, R.I., Fawcett, A.J., Clarke, E.F.: Dyslexia and Music: Measuring Musical Timing Skills. Dyslexia 9(1), 18–36 (2003)CrossRefGoogle Scholar
  22. 22.
    Poole, E.S., Miller, A.D., Xu, Y., Eiriksdottir, E., Catrambone, R., Mynatt, E.D.: The Place for Ubiquitous Computing in Schools: Lessons Learned from a School-based Intervention for Youth Physical Activity. In: UbiComp 2011, pp. 395–404. ACM, New York (2011)Google Scholar
  23. 23.
    Ramsay, M.C., Reynolds, C.R., Kamphaus, R.W.: Essentials of Behavioral Assessment. John Wiley and Sons, Inc., New York (2002)Google Scholar
  24. 24.
    Rogers, Y., Price, S., Randell, C., Fraser, D.S., Weal, M., Fitzpatrick, G.: Ubi-learning Integrates Indoor and Outdoor Experiences. Communications of the ACM 48(1), 55–59 (2005)CrossRefGoogle Scholar
  25. 25.
    Soloway, E., Jackson, S.L., Klein, J., Quintana, C., Reed, J., Spitulnik, J., Stratford, S.J., Studer, S., Eng, J., Scala, N.: Learning Theory in Practice: Case Studies of Learner-centered Design. In: CHI 1996, pp. 189–196. ACM, New York (1996)CrossRefGoogle Scholar
  26. 26.
    Squires, J., Nickel, R.E., Eisert, D.: Early Detection of Developmental Problems: Strategies for Monitoring Young Children in the Practice Setting. Developmental and Behavioral Pediatrics 17(6), 420–427 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Inseok Hwang
    • 1
  • Hyukjae Jang
    • 1
  • Taiwoo Park
    • 1
  • Aram Choi
    • 1
  • Youngki Lee
    • 1
  • Chanyou Hwang
    • 1
  • Yanggui Choi
    • 2
  • Lama Nachman
    • 3
  • Junehwa Song
    • 1
  1. 1.Korea Advanced Institute of Science and TechnologyDaejeonRepublic of Korea
  2. 2.Yerang KindergartenDaejeonRepublic of Korea
  3. 3.Intel CorporationSanta ClaraUSA

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