A Notification and Recommender Mobile App for Educational Online Discussion: A Design Research Approach

  • Kittisak SirisaengtaksinEmail author
  • Lorne Olfman
  • Nimer Alrushiedat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9192)


This research presents an information system design theory (ISDT) to integrate a notification and recommendation system (NARS) into online discussion forums on mobile devices. The artifact is designed with respect to awareness and information overload as kernel theories. Furthermore, the design includes an intuitive way to improve the accuracy of short-text clustering used to extract semantic topics from posts. The paper describes a prototype of the design artifact, experiments to evaluate the proposed short-text clustering method, and a survey to evaluate the quality of the artifact prototype.


Online discussion Design research Mobile Notification Recommender 


  1. 1.
    Callum, K., Kinshuk, : Mobile discussion boards: an analysis on mobile collaboration. Int. J. Interact. Mob. Technol. 2, 5–9 (2008)Google Scholar
  2. 2.
    Hill, T.R., Roldan, M.: Toward third generation threaded discussions for mobile learning: opportunities and challenges for ubiquitous collaborative environments. Inf. Syst. Front. 7, 55–70 (2005)CrossRefGoogle Scholar
  3. 3.
    Wojciechowski, A.: Supporting social networks by event-driven mobile notification services. In: Meersman, R., Tari, Z. (eds.) OTM-WS 2007, Part I. LNCS, vol. 4805, pp. 398–406. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Walls, J.G., Widmeyer, G.R., El Sawy, O.A.: Building an information system design theory for vigilant EIS. Inf. Syst. Res. 3, 36–59 (1992)CrossRefGoogle Scholar
  5. 5.
    Walls, J.G., Widmeyer, G.R., El Sawy, O.A.: Assessing information system design theory in perspective: how useful was our, initial rendition? J. Inf. Technol. Theory Appl. (JITTA) 6(2004), 43–58 (1992)Google Scholar
  6. 6.
    Gregor, S., Jones, D.: The anatomy of a design theory. J. Assoc. Inf. Syst. 8, 325–335 (2007)Google Scholar
  7. 7.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 734–749 (2005)CrossRefGoogle Scholar
  8. 8.
    Hornsby, A., Bouzazizi, I., Defee, I.: Notification service for DVB-H mobile broadcast. IEEE Wirel. Commun. 17, 15–21 (2010)CrossRefGoogle Scholar
  9. 9.
    Carroll, J.M., Neale, D.C., Isenhour, P.L., et al.: Notification and awareness: synchronizing task-oriented collaborative activity. Int. J. Hum. Comput. Stud. 58, 605–632 (2003)CrossRefGoogle Scholar
  10. 10.
    Latif, N.A., Hassan, M.F., Hasan, M.H.: Automated notification and document downloading in e-learning - development of an agent-based framework utilizing the push-pull technology interaction policy. In: ITSim 2008, vol. 1, pp. 1−7 (2008)Google Scholar
  11. 11.
    Bawden, D., Robinson, L.: The dark side of information: overload, anxiety and other paradoxes and pathologies. J. Inf. Sci. 35, 180–191 (2009)CrossRefGoogle Scholar
  12. 12.
    Eppler, M.J., Mengis, J.: The concept of information overload: a review of literature from organization science, accounting, marketing, MIS, and related disciplines. Inf. Soc. 20, 325–344 (2004)CrossRefGoogle Scholar
  13. 13.
    Abel, F., Bittencourt, I.I., Costa, E., et al.: Recommendations in online discussion forums for e-learning systems. TLT 3, 165–176 (2010)Google Scholar
  14. 14.
    Buder, J., Schwind, C.: Learning with personalized recommender systems: a psychological view. Comput. Hum. Behav. 28, 207–216 (2012)CrossRefGoogle Scholar
  15. 15.
    Petersen, H., Poon, J.: Enhancing short text clustering with small external repositories. In: AusDM, pp. 79–90 (2011)Google Scholar
  16. 16.
    Mehrotra, R., Sanner, S., Buntine, W. et al.: Improving LDA topic models for microblogs via tweet pooling and automatic labeling. In: SIGIR, vol. 36, pp. 889-892 (2013)Google Scholar
  17. 17.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  18. 18.
    Owen, S., Anil, R., Dunning, T., et al.: Mahout in Action. Manning Publications, New York (2011)Google Scholar
  19. 19.
    Hu, X., Sun, N., Zhang, C. et al.: Exploiting internal and external semantics for the clustering of short texts using world knowledge. In: CIKM, vol. 18, pp. 919-928 (2009)Google Scholar
  20. 20.
    Karampiperis, P., Sampson, D.: Adaptive learning resources sequencing in educational hypermedia systems. Educ. Technol. Soc. 8, 128–147 (2005)Google Scholar
  21. 21.
    Manouselis, N., Drachsler, H., Vuorikari, R., et al.: Recommender Systems in Technology Enhanced Learning, pp. 387–415. Springer, New York (2011)Google Scholar
  22. 22.
    Brusilovsky, P.: Developing Adaptive Educational Hypermedia Systems: From Design Models to Authoring Tools, pp. 377–409. Springer, Netherlands (2003)Google Scholar
  23. 23.
    Dourish, P., Bellotti, V.: Awareness and coordination in shared workspaces. In: CSCW, pp. 107–114 (1992)Google Scholar
  24. 24.
    Ogata, H., Yano, Y.: Combining knowledge awareness and information filtering in an open-ended collaborative learning environment. Int. J. Artif. Intell. Educ. (IJAIED) 11, 33–46 (2000)Google Scholar
  25. 25.
    Wang, H., Wang, C., Zhai, C. et al.: Learning online discussion structures by conditional random fields. In: SIGIR, vol. 34, pp. 435−444 (2011)Google Scholar
  26. 26.
    Banerjee, S., Ramanathan, K., Gupta, A.: Clustering short texts using Wikipedia. In: SIGIR, vol. 30, pp. 787−788 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Kittisak Sirisaengtaksin
    • 1
    Email author
  • Lorne Olfman
    • 1
  • Nimer Alrushiedat
    • 2
  1. 1.Claremont Graduate UniversityClaremontUSA
  2. 2.California State University, FullertonFullertonUSA

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