Advertisement

Towards Supporting Multigenerational Co-creation and Social Activities: Extending Learning Analytics Platforms and Beyond

  • Shin’ichi KonomiEmail author
  • Kohei Hatano
  • Miyuki Inaba
  • Misato Oi
  • Tsuyoshi Okamoto
  • Fumiya Okubo
  • Atsushi Shimada
  • Jingyun Wang
  • Masanori Yamada
  • Yuki Yamada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10922)

Abstract

As smart technologies pervade our everyday environments, they change what people should learn to live meaningfully as valuable participants of our society. For instance, ubiquitous availability of smart devices and communication networks may have reduced the burden for people to remember factual information. At the same time, they may have increased the benefits to master the uses of new digital technologies. In the midst of such a social and technological shift, we could design novel integrated platforms that support people at all ages to learn, work, collaborate, and co-create easily. In this paper, we discuss our ideas and first steps towards building an extended learning analytics platform that elderly people and unskilled adults can use. By understanding the characteristics and needs of elderly learners and addressing critical user interface issues, we can build pervasive and inclusive learning analytics platforms that trigger contextual reminders to support people at all ages to live and learn actively regardless of age-related differences of cognitive capabilities. We discuss that resolving critical usability problems for elderly people could open up a plethora of opportunities for them to search and exploit vast amount of information to achieve various goals.

Keywords

Pervasive learning Learning analytics Multigenerational co-creation Elderly people Learning environment Super-aging societies 

Notes

Acknowledgement

This work was supported by JST Mirai Grant Number 17-171024547, Japan.

References

  1. 1.
    Yamada, M., Oi, M., Konomi, S.: Effective learning environment design for aging well: a review. In: Streitz, N., Konomi, S. (eds.) DAPI 2018. LNCS, vol. 10922, pp. 253–264. Springer, Heidelberg (2018)Google Scholar
  2. 2.
    Taniguchi, Y., Gao, Y., Kojima, K., Konomi, S.: Evaluating learning style-based grouping strategies in real-world collaborative learning environment. In: Streitz, N., Konomi, S. (eds.) DAPI 2018. LNCS, vol. 10922, pp. 227–239. Springer, Heidelberg (2018)Google Scholar
  3. 3.
    Shimada, A.: Potential of wearable technology for super-aging societies. In: Streitz, N., Konomi, S. (eds.) DAPI 2018. LNCS, vol. 10922, pp. 214–226. Springer, Heidelberg (2018)Google Scholar
  4. 4.
    Blikstein, P.: Multimodal learning analytics. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge, pp. 102–106. ACM, New York (2013)Google Scholar
  5. 5.
    Hutchins, E.: Cognition in the Wild. MIT Press, Cambridge (1995)Google Scholar
  6. 6.
    Fischer, G., Arias, E., Carmien, S., Eden, H., Gorman, A., Konomi, S., Sullivan, J.: Supporting collaboration and distributed cognition in context-aware pervasive computing environments. In: Paper Presented at the 2004 Meeting of the Human Computer Interaction Consortium “Computing Off the Desktop”, 25 pp. (2004)Google Scholar
  7. 7.
    Marmasse, N., Schmandt, C.: Location-aware information delivery with comMotion. In: Proceedings of 2nd International Symposium on Handheld and Ubiquitous Computing, pp. 157–171 (2000)CrossRefGoogle Scholar
  8. 8.
    Dey, A.K., Abowd, G.D.: CybreMinder: a context-aware system for supporting reminders. In: Thomas, P., Gellersen, H.-W. (eds.) HUC 2000. LNCS, vol. 1927, pp. 172–186. Springer, Heidelberg (2000).  https://doi.org/10.1007/3-540-39959-3_13CrossRefGoogle Scholar
  9. 9.
    Dey, A.K., Abowd, G.D., Salber, D.A.: Conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Hum. Comput. Interact. 16, 97–166 (2001)CrossRefGoogle Scholar
  10. 10.
    Warren, I., Meads, A., Srirama, S., Weerasinghe, T., Paniagua, C.: Push notification mechanisms for pervasive smartphone applications. IEEE Pervasive Comput. 13(2), 61–71 (2014)CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Sohn, T., Li, K.A., Lee, G., Smith, I., Scott, J., Griswold, W.G.: Place-Its: a study of location-based reminders on mobile phones. In: Beigl, M., Intille, S., Rekimoto, J., Tokuda, H. (eds.) UbiComp 2005. LNCS, vol. 3660, pp. 232–250. Springer, Heidelberg (2005).  https://doi.org/10.1007/11551201_14CrossRefGoogle Scholar
  13. 13.
    Ludford, P.J., Frankowski, D., Reily, K., Wilms, K., Terveen, L.: Because I carry my cell phone anyway: functional location-based reminder applications. In: Proceedings of CHI 2006, pp. 889–898 (2006)Google Scholar
  14. 14.
    Pielot, M., Church, K., de Oliveira, R.: An in-situ study of mobile phone notifications. In: Proceedings of MobileHCI 2014, pp. 233–242 (2014)Google Scholar
  15. 15.
    Shirazi, A.S., Henze, N., Dingler, T., Pielot, M., Weber, D., Schmidt, A.: Large-scale assessment of mobile notifications. In: Proceedings of CHI 2014, pp. 3055–3064 (2014)Google Scholar
  16. 16.
    Fischer, J.E., Yee, N., Bellotti, V., Good, N., Benford, S., Greenhalgh, C.: Effects of content and time of delivery on receptivity to mobile interruptions. In: Proceedings of MobileHCI 2010, pp. 103–112 (2010)Google Scholar
  17. 17.
    Fischer, J.E., Greenhalgh, C., Benford, S.: Investigating episodes of mobile phone activity as indicators of opportune moments to deliver notifications. In: Proceedings of MobileHCI 2011, pp. 181–190 (2011)Google Scholar
  18. 18.
    Ho, J., Intille, S.S.: Using context-aware computing to reduce the perceived burden of interruptions from mobile devices. In: Proceedings of CHI 2005, pp. 909–918 (2005)Google Scholar
  19. 19.
    DeVaul, R.W., Clarkson, B., Pentland, A.S.: The memory glasses: towards a wearable, context aware, situation-appropriate reminder system. In: Proceedings of CHI 2000 Workshop on Situated Interaction in Ubiquitous Computing (2000)Google Scholar
  20. 20.
    Fogarty, J., Hudson, S.E., Atkeson, C.G., Avrahami, D., Forlizzi, J., Kiesler, S., Lee, J.C., Yang, J.: Predicting human interruptibility with sensors. ACM Trans. Comput. Hum. Inter. 12(1), 119–146 (2005)CrossRefGoogle Scholar
  21. 21.
    Pejovic, V., Musolesi, M.: InterruptMe: designing intelligent prompting mechanisms for pervasive applications. In: Proceedings of UbiComp 2014, pp. 897–908 (2014)Google Scholar
  22. 22.
    Pielot, M., De Oliveira, R., Kwak, H., Oliver, N.: Didn’t you see my message? Predicting attentiveness to mobile instant messages. Proc. CHI 2014, 3319–3328 (2014)Google Scholar
  23. 23.
    Rosenthal, S., Dey, A.K., Veloso, M.: Using decision-theoretic experience sampling to build personalized mobile phone interruption models. In: Lyons, K., Hightower, J., Huang, E.M. (eds.) Pervasive 2011. LNCS, vol. 6696, pp. 170–187. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-21726-5_11CrossRefGoogle Scholar
  24. 24.
    Smith, J., Lavygina, A., Ma, J., Russo, A., Dulay, N.: Learning to recognise disruptive smartphone notifications. In: Proceedings of MobileHCI 2014, pp. 121–124 (2014)Google Scholar
  25. 25.
    Hatano, K.: Can machine learning techniques provide better learning support for elderly people? In: Streitz, N., Konomi, S. (eds.) DAPI 2018. LNCS, vol. 10922, pp. 178–187. Springer, Heidelberg (2018)Google Scholar
  26. 26.
    Abowd, G.D., Atkeson, C.G., Hong, J., Long, S., Kooper, R., Pinkerton, M.: Cyberguide: a mobile context-aware tour guide. Wirel. Netw. 3(5), 421–433 (1997)CrossRefGoogle Scholar
  27. 27.
    Cheverst, K., Davies, N., Mitchell, K., Friday, A., Efstratiou, C.: Developing a context-aware electronic tourist guide: some issues and experiences. In: Proceedings of CHI, pp. 17–24 (2000)Google Scholar
  28. 28.
    Bellotti, V., Begole, B., Chi, E.E., Ducheneaut, D., Fang, J., Isaacs, E., King, T., Newman, M.W., Partridge, K., Price, B., Rasmussen, P., Roberts, M., Schiano, D.J., Walendowski, A.: Activity-based serendipitous recommendations with the Magitti mobile leisure guide. In: Proceedings of CHI 2008, pp. 1157–1166 (2008)Google Scholar
  29. 29.
    Ardissono, L., Kuflik, T., Petrelli, D.: Personalization in cultural heritage: the road travelled and the one ahead. User Model. User-Adap. Inter. 22(1–2), 73–99 (2011)Google Scholar
  30. 30.
    Cranshaw, J.B., Luther, K., Gage, P., Norman, K., Kelley, P.G., Sadeh, N.: Curated city: capturing individual city guides through social curation. In: Proceedings of CHI, pp. 3249–3258 (2014)Google Scholar
  31. 31.
    Sasao, T., Konomi, S. Kostakos, V., Kuribayashi, K., Goncalves, J.: Community reminder: participatory contextual reminder environments for local communities. Int. J. Hum. Comput. Stud. 102, 41–53 (2017)CrossRefGoogle Scholar
  32. 32.
    Carmien, S., Fischer, G.: Tools for living and tools for learning. In: Proceedings of HCI International Conference (HCII), Las Vegas, CD-ROM (2005)Google Scholar
  33. 33.
    Van Deursen, A., van Dijk, J.: Internet skills and the digital divide. New Media Soc. 13(6), 893–911 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Shin’ichi Konomi
    • 1
    Email author
  • Kohei Hatano
    • 1
  • Miyuki Inaba
    • 1
  • Misato Oi
    • 1
  • Tsuyoshi Okamoto
    • 1
  • Fumiya Okubo
    • 1
  • Atsushi Shimada
    • 2
  • Jingyun Wang
    • 3
  • Masanori Yamada
    • 1
  • Yuki Yamada
    • 1
  1. 1.Faculty of Arts and ScienceKyushu UniversityFukuokaJapan
  2. 2.Graduate School of Information Science and Electrical EngineeringKyushu UniversityFukuokaJapan
  3. 3.Research Institute for Information TechnologyKyushu UniversityFukuokaJapan

Personalised recommendations