Two Recommending Strategies to Enhance Online Presence in Personal Learning Environments

  • Samuel Nowakowski
  • Ivana Ognjanović
  • Monique Grandbastien
  • Jelena Jovanovic
  • Ramo Šendelj
Chapter

Abstract

Aiming to facilitate and support online learning practices, TEL researchers and practitioners have been increasingly focused on the design and use of Web-based Personal Learning Environments (PLE). A PLE is a set of services selected and customized by students. Among these services, resource (either digital or human) recommendation is a crucial one. Accordingly, this chapter describes a novel approach to supporting PLEs through recommendation services. The proposed approach makes extensive use of ontologies to formally represent learning context that, among other components, includes students’ presence in the online world, i.e., their online presence. This approach has been implemented in and evaluated with the OP4L (Online Presence for Learning) prototype. In this chapter, we expose recommendation strategies devised for OP4L. One is already implemented in OP4L, it is based on the well-known Analytical Hierarchical Process (AHP) method. The other one which has been tested on data coming from the prototype is based on the active user’s navigation stream and used a Kalman filter approach.

Keywords

Web-based learning Social presence Online presence Ontology based resource recommendation Kalman filter Learning trajectories AHP CS-AHP 

Notes

Acknowledgements

 This work was supported by the SEE-ERA Net Plus program, contract no 115, from the European Union.

References

  1. 1.
    Atwell G (2007) Personal learning environments – The future of eLearning? eLearning papers 2(1), ISSN: 1887-1542, www.elearningpapers.eu
  2. 2.
    Vuokari R, Manouselis N, Duval E (eds) (2009) Special issue on social information retrieval for technology enhanced learning, J Dig Inform 10(2), ISSN: 1368-7506Google Scholar
  3. 3.
    Manouselis N, Drachsler H, Vuorikari R, Hummel H, Koper R (2010) Recommender systems in technology enhanced learning. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Handbook of recommender systems. Springer, Secaucus, NJ, pp 387–415Google Scholar
  4. 4.
    Santos OC, Boticario JG (2012) Educational recommender systems and technologies. Practices and challenges. IGI Global, Hershey, PAGoogle Scholar
  5. 5.
    OP4L project’s website: http://op4l.fon.bg.ac.rs/
  6. 6.
    Vassileva J (2008) Towards social learning environments. IEEE TLT 1(4):199–214Google Scholar
  7. 7.
    Aragon SR (2003) Creating social presence in online environments. New Dir Adult Contin Educ 100:57–68CrossRefGoogle Scholar
  8. 8.
    Cob SC (2009) Social presence and online learning: a current view from a research perspective. J Interact Online Learn 8(3):241–254Google Scholar
  9. 9.
    Lowenthal PR (2010) Social presence. In: Dasgupta S (ed) Social computing: concepts, methodologies, tools, and applications. IGI Global, Hershey, PA, pp 129–136Google Scholar
  10. 10.
    Beham G, Kump B, Ley T, Lindstaed SN (2010) Recommending knowledgeable people in a work-integrated learning system, 1st RecSysTEL workshop. Proc Comput Sci 1(2):2783–2792, ElsevierCrossRefGoogle Scholar
  11. 11.
    Jovanovic J, Knight C, Gasevic D, Richards G (2007) Ontologies for effective use of context in e-learning settings. Educ Tech Soc 10(3):47–59Google Scholar
  12. 12.
    Jeremic Z, Milikic N, Jovanovic J, Radulovic R, Brkovic M, Devedzic V (2011) OP4L: online presence enabled personal learning environments, IEEE – ERK’2011 conference, Portoroz, SloveniaGoogle Scholar
  13. 13.
    Milikic N, Radulovic R, Devedzic V (2011) Infrastructure for exchanging online presence data in learning applications, IEEE – ERK’2011 conference, Portoroz, SloveniaGoogle Scholar
  14. 14.
  15. 15.
    Jovanović J, Gašević D, Stanković M, Jeremić Z, Siadaty M (2009) Online presence in adaptive learning on the social semantic web. In: Proceedings of the 1st IEEE international conference on social computing - workshops (Workshop on social computing in education), Vancouver, BC, Canada. IEEE, Washington, DC, pp 891–896Google Scholar
  16. 16.
    Stankevic M (2008) Modeling online presence, In: Proceedings of the first social data on the web workshop, Karlsruhe, Germany, October 27, 2008, CEUR workshop proceedings, ISSN 1613-0073, online CEUR-WS.org/Vol-405/paper1.pdfGoogle Scholar
  17. 17.
    Dagger D, Wade V, Conlan O (2005) Personalisation for all: making adaptive course composition easy. Educ Tech Soc 8(3):9–25Google Scholar
  18. 18.
    Popescu E, Trigano P, Badica C (2007) Adaptive educational hypermedia systems: a focus on learning styles. In: Proc of the international conference on computer as a tool (EUROCON), Warsaw, Poland. IEEE Computer Society, Washington, DCGoogle Scholar
  19. 19.
    Stash N De Bra P (2004) Incorporating cognitive styles in AHA! The adaptive hypermedia architecture. In: Proceedings of the international conference web-based education (IASTED), Innsbruck, Austria, pp 378–383Google Scholar
  20. 20.
    Brusilovsky P (2001) Adaptive hypermedia User modeling and user adapted interaction. In: Alfred Kobsa (ed.), Tenth year anniversary issue 11(1/2): 87–110Google Scholar
  21. 21.
    Mustafa A, Sharif S (2011) An approach to adaptive e-learning hypermedia system based on learning styles (AEHS-LS): implementation and evaluation. Int J Lib Inform Sci 3(1):15–28Google Scholar
  22. 22.
    Ognjanović I, Šendelj R (2012) Teachers’ requirements in dynamically adaptive e-learning systems. In: Proceedings of 4th international conference on education and new learning technologies (EDULEARN12), Barcelona, SpainGoogle Scholar
  23. 23.
    Ognjanović I, Gašević D, Bagheri E, Asadi M (2011) Conditional preferences in software stakeholders’ judgments. In: Proceedings of the 26th annual ACM symposium on applied computing, Taichang, Taiwan. ACM, New York, NY, pp 683–690Google Scholar
  24. 24.
    Yu Z, Yu Z, Zhou X, Nakamu Y (2009) Toward an understanding of user-defined conditional preferences. In: Proceedings of the 8th IEEE international conference on dependable, autonomic and secure computing. IEEE, Washington, DC, pp 203–208Google Scholar
  25. 25.
    Ognjanović I, Gašević D, Bagheri E (2013) A stratified framework for handling conditional preferences: an extension of the analytic hierarchy process. Expert Syst Appl 40(4):1094–1115CrossRefGoogle Scholar
  26. 26.
    Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York, NYMATHGoogle Scholar
  27. 27.
    Ognjanović I, Šendelj R (2011) Making judgments and decisions about relevant learning resources. In: Proceedings of the 20th international electrotechnical and computer science conference, Portoroz, Slovenia (ERK 2011), pp 409–412Google Scholar
  28. 28.
    Boutilier C, Brafman RI, Domshlak C, Hoos HH, Poole D (2004) CP-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements. J AI Res 21(1):135–191MATHMathSciNetGoogle Scholar
  29. 29.
    Brafman RI, Domshlak C (2002) Introducing variable importance tradeoffs into CP-nets. In: The proceedings of the eighteenth conference on uncertainty in AI, Canada. AAAI, Menlo Park, CA, pp 69–76Google Scholar
  30. 30.
    Wilson N (2011) Computational techniques for a simple theory of conditional preferences. Artif Intell 175(7–8):1053–1091CrossRefMATHGoogle Scholar
  31. 31.
    Zavadskas EK, Kaklauskas A, Peldschus F, Turskis Z (2007) Multi-attribute assessment of road design solutions by using the COPRAS Method. Baltic J Road Bridge Eng 2(4): 195–203Google Scholar
  32. 32.
    Chen S, Buffett S, Fleming MW (2007) Reasoning with conditional preferences across attributes. In: Proceedings of the 20th conference of the Canadian society for computational studies of intelligence on advances in AI, Montreal, Canada. Springer, Berlin, pp 369–380Google Scholar
  33. 33.
    Berander P, Andrews A (2006) Requirements prioritization. Engineering and managing software requirements. Springer, Secaucus, NJ, pp 69–94Google Scholar
  34. 34.
    Hanna M (2004) Data mining in the e-learning domain. Campus Wide Inf Syst 21(1):29–34CrossRefMathSciNetGoogle Scholar
  35. 35.
    Merceron A, Yacef K (2005) Educational data mining: a case study. In: Proc Int Conf Artif Intell Educ, Pittsburgh, PA, 2005Google Scholar
  36. 36.
    Baker R, Yacef K (2009) The state of educational data mining in 2009: a review and future visions. J Educ Data Mining 1(1):3–17Google Scholar
  37. 37.
    Romero C, Ventura S (2010) Educational data mining: a review of the state of the art. IEEE Trans Syst Man Cybern Part C Appl Rev 40(6):601–618CrossRefGoogle Scholar
  38. 38.
    Schafer JB (2005) The application of data-mining to recommender systems. In: Wang J (ed) Encyclopedia of data warehousing and mining. Hershey, PA, Idea Group, pp 44–48CrossRefGoogle Scholar
  39. 39.
    Lazcorreta E, Botella F, Fernández-Caballero A (2008) Towards personalized recommendation by two-step modified apriori data mining algorithm. Expert Syst Appl 35(3):1422–1429CrossRefGoogle Scholar
  40. 40.
    Büyüközkan G, Çifçi G, Güleryüz S (2011) Strategic analysis of healthcare service quality using fuzzy AHP methodology. Expert Syst Appl 38(8):9407–9424CrossRefGoogle Scholar
  41. 41.
    Chen MK, Wang S (2010) The critical factors of success for information service industry in developing international market: using analytic hierarchy process (AHP) approach. Expert Syst Appl 37(1):694–704CrossRefGoogle Scholar
  42. 42.
    Ognjanović I, Gašević D, Bagheri E, Asadi M (2011) Conditional preferences in software stakeholders’ judgments. In: Proceedings of the 26th annual ACM symposium on applied computing (SAC 2011), Tunghai University, Taichang, Taiwan. ACM, New York, NYGoogle Scholar
  43. 43.
    Padmanabhan V, Mogul J (1996) Using predictive prefetching to improve World Wide Web Latency. Comput Commun Rev 28(4):22–36CrossRefGoogle Scholar
  44. 44.
    Box GEP, Jenkins GM (1970) Time series analysis: forecasting and control. Holden Day, San Francisco, CAMATHGoogle Scholar
  45. 45.
    Despande M, Karypis G (2004) Selective Markov models for predicting web pages accesses. ACM Trans Internet Technol 4:163–184CrossRefGoogle Scholar
  46. 46.
    Pirolli P, Pitkow J (1999) Distribution of surfer’s paths through the World Wide Web: empirical characterizations. WWW J 2(1–2):29–45Google Scholar
  47. 47.
    Pitkow J, Pirolli P (1999) Mining longest repeating subsequences to predict World Wide Web surfing. In: Proceedings of the 2nd conference of USENIX symposium on internet technologies and systems. USENIX Association, Berkeley, CA, pp 139–150Google Scholar
  48. 48.
    Nakagawa N, Mobasher B (2003) Impact of site characteristics on recommendation models based on association rules and sequential patterns. In: Proceedings of the IJCAI’03 workshop on intelligent techniques for web personalization, August 9–10, 2003, Acapulco, MexicoGoogle Scholar
  49. 49.
    Anderson B, Moore JB (1977) Optimal filtering. Prentice Hall – Information and system sciences series. Prentice Hall, Englewood Cliffs, NJGoogle Scholar
  50. 50.
    Gevers M, Vandendorpe L (2011) Processus stochastiques, estimation et prediction, http://www.tele.ucl.ac.be/EDU/INMA2731/
  51. 51.
    Nowakowski S, Boyer A, Bernier C (2011) Automatic tracking and control for web recommendation. New approaches for web recommendation. Conference SOTICS 2011, October 2011, Barcelona, SpainGoogle Scholar
  52. 52.
    Grandbastien M, Loskovska S, Nowakowski S, Jovanovic J (2012) Using online presence data for recommending human resources in the OP4L project. Conference RecSysTel, September 2012, Sarrebrück, GermanyGoogle Scholar
  53. 53.
    Gibson W (1988) Neuromancien. Collection J’ai Lu, ParisGoogle Scholar
  54. 54.
    Söderström T (1994) Discrete-time stochastic systems estimation and control. Springer, Secaucus, NJMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Samuel Nowakowski
    • 1
  • Ivana Ognjanović
    • 2
    • 3
  • Monique Grandbastien
    • 1
  • Jelena Jovanovic
    • 4
  • Ramo Šendelj
    • 2
    • 3
  1. 1.LORIAUniversité de LorraineVandoeuvre les Nancy CedexFrance
  2. 2.Faculty of Information technologyMediterranean UniversityPodgoricaMontenegro
  3. 3.Institute of Modern technologyPodgoricaMontenegro
  4. 4.FOS-Faculty of Organizational SciencesUniversity of BelgradeBelgradeSerbia

Personalised recommendations