Artículos regulares

 

Enrichment of Learner Profile with Ubiquitous User Model Interoperability

 

Enriquecimiento del perfil del estudiante mediante la interoperabilidad de modelos de usuario ubicuos

 

María de Lourdes Martínez-Villaseñor¹, Miguel González-Mendoza², and Ignacio Danvila Del Valle³

 

¹ Universidad Panamericana, Campus México, D.F., Mexico. lmartine@up.edu.mx

² Tecnológico de Monterrey, Campus Estado de México, Edo. de México, Mexico. mgonza@itesm.mx

³ Universidad Complutense de Madrid, Madrid, Spain. idanvila@ccee.ucm.es

 

Abstract

Nowadays, there is a constant need of acquiring new knowledge and skills to keep up with the demands of changing environment. The design and development of training and educational systems that enable effective personalized learning help obtaining changing skills and fill competence gaps. The computational effort to create a user model that represents user's knowledge, characteristics, interests, goals, background and preferences is repeatedly done by many systems and applications in several domains. Each system ends up with a partial view of the user. Researchers in user modeling foresee the need of sharing and reusing user model information in order to obtain a better understanding of the user and be able to provide personalized and proactive services. In this paper we present an application scenario of sharing and reusing information scattered in most commonly used applications to enhance learner profiles.

Keywords: User modeling interoperability, learner profile enrichment.

 

Resumen

En la actualidad hay una necesidad constante de adquirir nuevo conocimiento y habilidades para cubrir las demandas de un ambiente cambiante. El diseño y desarrollo de sistemas educacionales y de entrenamiento que permitan un aprendizaje efectivo y personalizado, ayuda a obtener habilidades cambiantes y llenar las brechas de competencia. El esfuerzo computacional para crear un modelo de usuario que represente el conocimiento, características, intereses, metas, antecedentes y preferencias del usuario es realizado repetidamente por varios sistemas y aplicaciones de diferentes dominios. Cada sistema termina con un conocimiento parcial del usuario. Investigadores del área de modelado de usuario visualizan la necesidad de compartir y reusar la información de los modelos de usuario para obtener un mejor entendimiento del usuario y proveer servicios de manera personalizada y proactiva. En este artículo presentamos un escenario de aplicación para compartir y reusar información esparcida en las aplicaciones más comúnmente usadas con el fin de enriquecer perfiles del estudiante.

Palabras clave: Interoperabilidad de modelos de usuario, enriquecimiento del perfil de estudiante.

 

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References

1. Brusilovsky, P., Kobsa, A., & Nejdl, W. (2007). The Adaptive Web. Methods and Strategies of Web Personalization. Lecture Notes in Computer Science, 4321. doi: 10.1007/978-3-540-72079-9.         [ Links ]

2. Brusilovsky, P. & Millán, E. (2007). User models for adaptive hypermedia and adaptive educational systems. The Adaptive web. Lecture Notes in Computer Science, 4321, 3-53.         [ Links ]

3. Paulsson, F. (2005). Achieving Learner Information interoperability: Position paper for the LIFE expert meeting on Learner Information. Cambridge, UK.         [ Links ]

4. Brusilovsky, P. (2001). Adaptive Hypermedia. User Model User-Adapted Interaction, 11(1-2), 87-110.         [ Links ]

5. Berkovsky, S., Heckmann, D., & Kuflik, T. (2009). Addressing Challenges of Ubiquitous User Modeling: Between mediation and semantic integration. Advances in Ubiquitous User Modelling. Lecture Notes in Computer Science, 5830, 1-19. doi: 10.1007/978-3-642-05039-8_1.         [ Links ]

6. Carmagnola, F., Cena, F., & Gena, C. (2011). User model interoperability: a survey. User Model and User-Adapted Interaction, 21(3), 285-331. doi: 10.1007/s11257-011-9097-5.         [ Links ]

7. Aroyo, L., Dolog, P., Houben, G.J., Kravcik, M., Naeve, A., Nilsson, M., & Wild, F. (2006). Interoperability in Personalized Adaptive Learning. Educational Technology and Society, 9(2), 4-18.         [ Links ]

8. Bakaev, M., Avdeenko, T. (2013). Indexing and Comparison of Multi-Dimensional Entities in a Recommender System based on Ontological Approach. Computación y Sistemas, 17(1), 5-13.         [ Links ]

9. Carmagnola, F. (2009). Handling Semantic Heterogeneity in Interoperable Distributed User Models. Advances in Ubiquitous User Modelling. Lecture Notes in Computer Science, 5830, 20-36. doi: 10.1007/978-3-642-05039-8_2.         [ Links ]

10. IMS Global Learning Consortium Inc. (2001). IMS Learner Information Package Specification. Retrieved from http://www.imsglobal.org/profiles/.         [ Links ]

11. Ramandalahy, T., Vidal, P., & Broisin, J. (2009). Opening Learner Profiles across Heterogeneous Applications. Ninth IEEE International Conference on Advanced Learning Technologies (ICALT 2009), Riga, Latvia, 504-508.         [ Links ]

12. Martinez-Villaseñor, M. de L., Gonzalez-Mendoza, M., & Hernandez-Gress, N. (2012). Towards a Ubiquitous User Model for Profile Sharing and Reuse. Sensors, 12(10), 1324913283. doi: 10.3390/s121013249.         [ Links ]

13. Martinez-Villaseñor, M. de L. (2013). Design and Implementation of a Framework for Ubiquitous User Model Interoperability. PhD Thesis, Instituto Tecnológico y de Estudios Superiores de Monterrey, Estado de México, México.         [ Links ]

14. Dolog, P. & Schäfer, M. (2005). A framework for browsing, manipulating and maintaining interoperable learner profiles. User Modelling 2005. Lecture Notes in Computer Science, 3538, 397-401.         [ Links ]

15. Musa, D.L. & De Oliveira, J.P.M. (2005). Sharing learner information through a web services-based learning architecture. Journal of Web Engineering, 4(3), 263-278.         [ Links ]

16. Brusilovsky, P. (2004). KnowledgeTree: A Distributed Architecture for Adaptive E-Learning. 13^th International World Wide Web Conference on Alternate track papers & posters. New York, USA, 104-113.         [ Links ]

17. Brusilovsky, P., Sosnovsky, S., & Yudelson, M. (2005). Ontology-based Framework for User Model Interoperability in Distributed Learning Environments. World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education. Chesapeake, VA, 2851-2855.         [ Links ]

18. Assad, M., Carmichael, D.J., Kay, J., & Kummerfeld, B. (2007). PersonisAD: Distributed, active, scrutable model framework for context-aware services. Pervasive Computing. Lecture Notes in Computer Science, 4480, 55-72.         [ Links ]

19. Kay, J. (1994). The um toolkit for cooperative user modelling. User Model User-adapted Interaction, 4(3), 149-196.         [ Links ]

20. Kay, J. (1999). A scrutable user modelling shell for user-adapted interaction. PhD Thesis, University of Sydney, Australia.         [ Links ]

21. Kay, J. (2000). Accretion Representation for Scrutable Student Modelling. Intelligent Tutoring Systems. Lecture Notes in Computer Science, 1839, 514-523.         [ Links ]

22. Kay, J., Kummerfeld, B., & Lauder, P. (2002). Personis: a server for user models. Adaptive Hypermedia and Adaptive Web-Based Systems. Lecture Notes in Computer Science, 2347, 203212.         [ Links ]

23. Distributed Management Task Force. (2003). CIM User and Security Model White Paper.         [ Links ]

24. McCalla, G., Vassileva, J., Greer, J., & Bull, S. (2000). Active Learner Modelling. Intelligent Tutoring Systems. Lecture Notes in Computer Science, 1839, 53-62.         [ Links ]

25. Vassileva, J. (2001). Distributed user modelling for universal information access. 9^th International Conference on Human-Computer Interaction. New Orleans, USA, 122-126.         [ Links ]

26. Niu, X., Mccalla, G., Vassileva, J. (2003). Purpose-based User Modelling in a Multi-agent Portfolio Management System. User Modeling 2003. Lecture Notes in Computer Science, 2702, 398-402.         [ Links ]

27. Vassileva, J., Mccalla, G., Greer, J. (2003). Multi-agent multi-user modeling in I-Help. User Model and User-adapted Interaction, 13(1-2), 179-210.         [ Links ]

28. Stewart, C., Cristea, A., Celik, I., Ashman, H. (2006). Interoperability between AEH user models. Proceedings of the joint international workshop on Adaptivity, personalization & the semantic web. Odense, Denmark, 21 -30.         [ Links ]

29. Trella, M., Conejo, R., Guzman, E., & Bueno, D. (2003). An Educational Component Based Framework for Web ITS Development. Web Engineering. Lecture Notes in Computer Science, 2722, 134-143.         [ Links ]

30. Van der Sluijs, K. & Houben, G.J. (2006). A generic component for exchanging user models between web-based systems. Learn International Journal of Continuing Engineering Education and Lifelong Learning, 16(1-2), 64-76.         [ Links ]

31. González, G., López, B., & de la Rosa, J.L. (2005) . A Multi-agent Smart User Model for Cross-domain Recommender Systems. Workshop beyond Personalization IUI'05, San Diego, Ca.         [ Links ]

32. Pazzani, M.J. (1999). A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review, 13(5-6), 393-408. doi: 10.1023/A:1006544522159.         [ Links ]

33. Berkovsky, S. (2006). Decentralized Mediation of User Models for a Better Personalization. Adaptive Hypermedia and Adaptive Web-Based Systems. Lecture Notes in Computer Science, 4018, 404-408.         [ Links ]

34. Berkovsky, S., Kuflik, T., Ricci, F. (2008). Mediation of user models for enhanced personalization in recommender systems. User Modeling and User-adapted Interaction, 18(3), 245-286. doi: 10.1007/s11257-007-9042-9.         [ Links ]

35. Berkovsky, S., Kuflik, T., & Ricci, F. (2007). Cross-domain mediation in collaborative filtering. User Modeling 2007. Lecture Notes in Computer Science, 4511, 355-359.         [ Links ]

36. Berkovsky, S., Kuflik, T., & Ricci, F. (2009). Cross-representation mediation of user models. User Model User-adapted Interaction, 19(1-2), 35-63. doi: 10.1007/s11257-008-9055-z.         [ Links ]

37. Berkovsky S, Gorfinkel A, Kuflik T, Manevitz L (2006) Case-based to content-based user model mediation. Work. Ubiquitous User Model. Riva de Garda, Italy, pp 1-4.         [ Links ]

38. Sosnovsky, S., Brusilovsky, P., Yudelson, M., Mitrovic, A., Mathews, M., & Kumar, A. (2009). Semantic Integration of Adaptive Educational Systems. Advances in Ubiquitous User Modelling. Lecture Notes in Computer Science, 5830, 134158.         [ Links ]

39. Bouzeghoub, A. & Gancarski, A.L. (2011). An Agent-Based Service Architecture for User Profiles Dynamic Share. Seventh International Conference on Semantics Knowledge and Grid (SKG), Beijing, China, 137-143.         [ Links ]

40. Walsh, E., O'Connor, A., & Wade, V. (2011). Supporting Learner Model Exchange in Educational Web Systems. 7^th International Conference on Web Information Systems and Technologies, Noordwijkerhout, The Netherlands.         [ Links ]

41. Shvaiko, P., Giunchiglia, F., Schorlemmer, M., McNeill, F., Bundy, A., Marchese, M., Yatskevich, M., Zaihrayeu, I., Ho, B., Lopez, V., Sabou, M., Abian, J., Siebes, R., & Kotoulas, S. (2006). Openknowledge Deliverable 3.1.: Dynamic Ontology Matching: A Survey (DIT-06-046). Trento, Italy: University of Trento.         [ Links ]

42. Miles, A. & Pérez-Agüera, J.R. (2007). SKOS: Simple Knowledge Organization for the Web. Cataloging & Classification Quarterly, 43, (3-4), 69-83.         [ Links ]

43. Dice, L.R. (1945). Measures of the amount of ecologic association between species. Ecology, 26(3), 297-302.         [ Links ]

44. Fellbaum, C. (2005). WordNet and Wordnets. In: Brown, K. E (ed). Encyclopedia of Language and Linguistics (665-670), Oxford, UK: Elsevier.         [ Links ]

45. Kravcík, M. & Gasevic, D. (2007). Leveraging the Semantic Web for Adaptive Education. Journal of Interactive Media in Education, Special Issue: Adaptation and IMS Learning Design.         [ Links ]

46. Chatti, M.A., Klamma, R., Quix, C., & Kensche, D. (2005). LM-DTM: An Environment for XML-Based, LIP/PAPI-Compliant Deployment, Transformation and Matching of Learner Models. Fifth IEEE International Conference on Advanced Learning Technologies (ICALT 2005). Kaohsiung, Taiwan, 567-569.         [ Links ]

47. The Nielsen Company. (2012). August 2012: Top U.S. Web Brands and Technology News Websites. Retrieved from http://blog.nielsen.com/nielsenwire/online_mobile/august-2012-top-u-s-web-brands-and-technology-news-websites/.         [ Links ]

48. Russell, M.A. (2011). Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn and Other Social Media Sites. Sebastopol, CA: O'Reilly.         [ Links ]

49. Ding, L., Zhou, L., Finin, T., & Joshi, A. (2005). How the Semantic Web is Being Used: An Analysis of FOAF Documents. 38^th Annual Hawaii International Conference on System Sciences. Big Island, HI, USA, 4, 113.3.         [ Links ]

50. Bellahsene, Z., Bonifati, A., Duchateau, F., & Velegrakis, Y. (2011). On Evaluating Schema Matching and Mapping. Schema Matching and Mapping (253-291), New York: Springer.         [ Links ]