The Value of Social: Comparing Open Student Modeling and Open Social Student Modeling

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9146)


Open Student Modeling (OSM) is a popular technology that makes traditionally hidden student models available to the learners for exploration. OSM is known for its ability to increase student engagement, motivation, and knowledge reflection. A recent extension of OSM known as Open Social Student Modeling (OSSM) attempts to enhance cognitive aspects of OSM with social aspects by allowing students to explore models of peer students or the whole class. In this paper, we introduce MasteryGrids, a scalable OSSM interface and report the results of a large-scale classroom study that explored the value of adding social dimension to OSM. The results of the study reveal a remarkable engaging potential of OSSM as well as its impact on learning effectiveness and user attitude.


Open student modeling Open social student modeling Social visualization 


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  1. 1.
    Ahn, J.-w., Brusilovsky, P., Grady, J., He, D., Syn, S.Y.: Open user profiles for adaptive news systems: help or harm? In: Proc. of the 16th international conference on World Wide Web, WWW 2007. ACM, pp. 11–20 (2007)Google Scholar
  2. 2.
    Brusilovsky, P., Sosnovsky, S., Lee, D., Yudelson, M., Zadorozhny, V., Zhou, X.: Learning SQL programming with interactive tools: from integration to personalization. ACM Transactions on Computing Education 9(4), 1–15 (2010). Article No. 19CrossRefGoogle Scholar
  3. 3.
    Bull, S.: UMPTEEN: Named and Anonymous Learner Model Access for Instructors and Peers. International Journal of Artificial Intelligence in Education 17(3), 227–253 (2007)Google Scholar
  4. 4.
    Bull, S., Kay, J.: Student Models That Invite the Learner The SMILI:() Open Learner Modelling Framework. International Journal of AI in Education 17(2), 89–120 (2007)Google Scholar
  5. 5.
    Bull, S., Kay, J.: Open Learner Models as Drivers for Metacognitive Processes. In: Azevedo, R., Aleven, V. (eds.) International Handbook of Metacognition and Learning Technologies, pp. 349–365. Springer, Berlin (2013)CrossRefGoogle Scholar
  6. 6.
    Corbett, A.T., Anderson, J.R.: Knowledge tracing: Modelling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction 4(4), 253–278 (1995)CrossRefGoogle Scholar
  7. 7.
    Hsiao, I.-H., Brusilovsky, P.: Motivational Social Visualizations for Personalized E-Learning. In: Ravenscroft, A., Lindstaedt, S., Kloos, C.D., Hernández-Leo, D. (eds.) EC-TEL 2012. LNCS, vol. 7563, pp. 153–165. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Hsiao, I.-H., Sosnovsky, S., Brusilovsky, P.: Guiding students to the right questions: adaptive navigation support in an E-Learning system for Java programming. Journal of Computer Assisted Learning 26(4), 270–283 (2010)CrossRefGoogle Scholar
  9. 9.
    Hsiao, I.H., Bakalov, F., Brusilovsky, P., König-Ries, B.: Progressor: social navigation support through open social student modeling. New Review of Hypermedia and Multimedia 19(2), 112–131 (2013)CrossRefGoogle Scholar
  10. 10.
    Loboda, T.D., Guerra, J., Hosseini, R., Brusilovsky, P.: Mastery Grids: An Open Source Social Educational Progress Visualization. In: de Freitas, S., Rensing, C., Ley, T., Muñoz-Merino, P.J. (eds.) EC-TEL 2014. LNCS, vol. 8719, pp. 235–248. Springer, Heidelberg (2014)Google Scholar
  11. 11.
    Mitrovic, A., Martin, B.: Evaluating the Effect of Open Student Models on Self-Assessment. International Journal of AI in Education 17(2), 121–144 (2007)Google Scholar
  12. 12.
    O’Keeffe, I., Brady, A., Conlan, O., Wade, V.: Just-in-time Generation of Pedagogically Sound, Context Sensitive Personalized Learning Experiences. International Journal on E-Learning 5(1), 113–127 (2006)Google Scholar
  13. 13.
    Paas, F., van Merriënboer, J.J.G.: The efficiency of instructional conditions: An approach to combine mental effort and performance measures. Human Factors 35, 737–743 (1993)Google Scholar
  14. 14.
    Paas, F., van Merriënboer, J.J.G.: Instructional control of cognitive load in the training of complex cognitive tasks. Educational Psychology Review 35(6), 51–71 (1994)Google Scholar
  15. 15.
    Papanikolaou, K.A., Grigoriadou, M., Kornilakis, H., Magoulas, G.D.: Personalising the interaction in a Web-based Educational Hypermedia System: the case of INSPIRE. User Modeling and User Adapted Interaction 13(3), 213–267 (2003)CrossRefGoogle Scholar
  16. 16.
    Weber, G., Brusilovsky, P.: ELM-ART: An adaptive versatile system for Web-based instruction. International Journal of Artificial Intelligence in Education 12(4), 351–384 (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Information SciencesUniversity of PittsburghPittsburghUSA
  2. 2.Department of Computer Education and Instructional TechnologiesGazi UniversityAnkaraTurkey

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