L.I.M.E. A recommendation model for informal and formal learning, engaged
DOI:
https://doi.org/10.9781/ijimai.2013.2211Keywords:
e-learning, Education, Personalization, Technology EnhancedAbstract
In current eLearning models and implementations (e.g. Learning Management Systems-LMS) there is a lack of engagement between formal and informal activities. Furthermore, the online methodology focuses on a standard set of units of learning and learning objects, along with pre-defined tests, and collateral resources like, i.e. discussion fora and message wall. They miss the huge potential of learning via the interlacement of social networks, LMS and external sources. Thanks to user behaviour, user interaction, and personalised counselling by a tutor, learning performance can be improved. We design and develop an adaptation eLearning model for restricted social networks, which supports this approach. In addition, we build an eLearning module that implements this conceptual model in a real application case, and present the preliminary analysis and positive results.Downloads
References
[1] R. A. Bjork, "Assessing our own competence: Heuristics and illusions," in Attention and performance XVII. Cognitive regulation of performance: Interaction of theory and application, D. Gopher and A. Koriat, Eds. Cambridge, MA: MIT Press, 1999, pp. 435-459.
[2] V. Romero and D. Burgos, "Meta-Mender: A meta-rule based recommendation system for educational applications," presented at Proceedings of the Workshop on Recommender Systems for Technology Enhanced Learning, RecsysTEL-2010, Barcelona, Spain, 2010.
[3] V. Romero, D. Burgos, and A. Pardo, "Meta-rule based Recommender Systems for Educational Applications," in Educational Recommender Systems and Technologies: Practices and Challenges, O. Santos and J. Boticario, Eds.: Information Science-Idea Group, 2011.
[4] B. White and J. Frederiksen, "A theoretical framework and approach for fostering metacognitive development," Educational Psychologist, vol. 40, pp. 211–223, 2005.
[5] G. Linden, B. Smith, and Y. J., "Amazon.com recommendations: Itemto-item collaborative filtering," Internet Computing IEEE, vol. 7, pp. 76-80, 2003.
[6] B. Marlin, "Modeling user rating profiles for collaborative filtering," in Advances in neural information processing systems, S. Thrun, L. K. Saul, and B. Schölkopf, Eds. Cambridge, MA: MIT Press, 2003, pp. 627-634.
[7] S. Y. Chen and G. D. Magoulas, Adaptable and Adaptive Hypermedia Systems. Hershey, PA: IRM Press, 2005.
[8] K. I. Ghauth and N. A. Abdullah, "Learning materials recommendation using good learners’ ratings and content-based filtering," Education Technology Research and Development, In press.
[9] T. Kerkiri, A. Manitsaris, and A. Mavridou, Reputation metadata for recommending personalized e-learning resources. Uxbridge: IEEE Computer Society, 2007.
[10] C. Romero, S. Ventura, P. D. De Bra, and C. D. Castro, "Discovering prediction rules in AHA! courses.," presented at 9th International User Modeling Conference, 2003.
[11] D. Burgos, C. Tattersall, and R. Koper, "How to represent adaptation in eLearning with IMS Learning Design," Interactive Learning Environments, vol. 15, pp. 161-170, 2007.
[12] J. J. Rocchio, Relevance feedback in information retrieval, in the SMART Retrieval System. Experiments in Automatic Document Processing. Englewood Cliffs, NJ: Prentice Hall, Inc., 1971.
[13] P. Sánchez-Gonzáleza, I. Oropesaa, V. Romeroc, A. Fernándeza, A. Albaceted, E. Asenjoe, J. Nogueraf, F. Sánchez-Margallog, D. Burgosc, and E. J. Gómez, "TELMA: technology enhanced learning environment for Minimally Invasive Surgery," Procedia Computer Science, vol. 00, 2010.
[14] Romero L, Gutiérrez M, Caliusco ML. Conceptualizing the e-Learning Assessment Domain using an Ontology Network. International Journal of Interactive Multimedia and Artificial Intelligence. 2012;1 (Special Issue on Intelligent Systems and Applications):20-8
Downloads
Published
-
Abstract62
-
PDF21






