Social Context-Aware Recommendation for Personalized Online Learning
The integration of ICT in teaching and learning enables the paradigm shift for education system by creating a possibility for learner to learn anywhere and anytime through variety of communication system. To enhance effective learning for a large number of learners, online learning requires effective personalized learning method. For decades, recommendation system is responsible for providing personalized learning to the learners by considering several related learners information such as individual characteristic, learning style, and knowledge background. With context aware computing perspective, this paper thus proposes the context-aware recommendation system to promote effective personalized online learning for each learner individually. Instead of employing ordinary individual context, this paper focuses also on the social context which is the interaction between learning objects and the learners. The gathered social context is classified with K-nearest neighbor and decision tree for classifying appropriate types of learners. Consequently, the appropriate learning paths are recommended by using association rule. The empirical study is conducted with the learners having scientific and non-scientific backgrounds studying in two different content modules of basic computer skill course. The results show that the proposed social context-aware recommendation system is able to provide acceptable classification accuracies from both classifiers. Additionally, the proposed system is potentially able to recommend appropriate learning path to different group of learners.
KeywordsContext-aware recommendation Personalized learning Online learning Social context Context-aware computing
- 1.Majumdar, S. (2015). Emerging trends in ICT for education & training. In General Asia and Pacific Region IVETA. Resource document. United Nations Education. http://www.unevoc.unesco.org/fileadmin/up/emergingtrendsinictforeducationandtraining.pdf. Accessed October 2016.
- 2.Attwell, G. (2007). Personal learning environments—The future of eLearning? Elearning Papers, 2(1), 1–8.Google Scholar
- 3.Nichols, M. (2003). A theory for eLearning. Educational Technology & Society, 6(2), 1–10.Google Scholar
- 4.Stahl, G., Koschmann, T., & Suthers, D. (2006). Computer-supported collaborative learning: An historical perspective. In Cambridge handbook of the learning sciences, 2006 (pp. 409–426).Google Scholar
- 6.Brusilovsky, P., & Nijhawan, H. (2002). A framework for adaptive e-learning based on distributed re-usable learning activities. In Proceedings of world conference on e-learning, e-learn.Google Scholar
- 7.Das, M. M., Bhaskar, M., & Chithralekha, T. (2010). Three layered adaptation model for context aware E-learning. In V. V. Das & R. Vijaykumar (Eds.), International conference on advances in information and communication technologies (pp. 243–248). Berlin: Springer.Google Scholar
- 9.Brusilovsky, P., & Peylo, C. (2003). Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education (IJAIED), 13, 159–172.Google Scholar
- 10.Paramythis, A., & Loidl-Reisinger, S. (2003). Adaptive learning environments and e-learning standards. In Second European conference on e-learning (Vol. 1, pp. 369–379).Google Scholar
- 11.Baker, F. B. (2001). The basics of item response theory. ERIC Clearinghouse on Assessment and Evaluation. College park, MD: University of Maryland. Resource document. Clearinghouse on Assessment and Evaluation. http://ericae.net/irt/baker. Accessed October 2016.
- 16.Simon, B., Mikls, Z., Nejdl, W., Sintek, M., & Salvachua, J. (2003). Smart space for learning: A mediation infrastructure for learning services. In Proceedings of the twelfth international conference on world wide web (pp. 20–24).Google Scholar
- 18.Abowd, G. D., Dey, A. K., Brown, P. J., Davies, N., Smith, M., & Steggles, P. (1999). Towards a better understanding of context and context-awareness. In H.-W. Gellersen (Ed.), International symposium on handheld and ubiquitous computing (pp. 304–307). Berlin: Springer.Google Scholar
- 21.Beale, R., & Lonsdale, P. (2004). Mobile context aware systems: The intelligence to support tasks and effectively utilise resources. In B. Stephen & D. Mark (Eds.), International conference on mobile human–computer interaction (pp. 240–251). Berlin: Springer.Google Scholar
- 22.Berri, J., Benlamri, R., & Atif, Y. (2006). Ontology-based framework for context-aware mobile learning. In Proceedings of the 2006 international conference on wireless communications and mobile computing (pp. 1307–1310). New York: ACM.Google Scholar
- 23.Derntl, M., & Hummel, K. A. (2005). Modeling context-aware e-learning scenarios. In Third IEEE international conference on pervasive computing and communications workshops, 2005. PerCom 2005 Workshops (pp. 337–342).Google Scholar
- 25.Marquez, J. M., Ortega, J. A., Gonzalez-Abril, L., & Velasco, F. (2008). Creating adaptive learning paths using ant colony optimization and bayesian networks. In IEEE international joint conference on neural networks, 2008. IJCNN 2008. (IEEE world congress on computational intelligence). (pp. 3834–3839).Google Scholar
- 27.Rath, A. S., Devaurs, D., & Lindstaedt, S. N. (2009). UICO: An ontology-based user interaction context model for automatic task detection on the computer desktop. In Proceedings of the 1st workshop on context, information and ontologies. New York: ACM.Google Scholar
- 30.Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., & Verkamo, A. I. (1996). Fast discovery of association rules. Advances in Knowledge Discovery and Data Mining, 12(1), 307–328.Google Scholar
- 31.Cooley, R., Mobasher, B., & Srivastava, J. (1997). Web mining: Information and pattern discovery on the world wide web. In Proceedings ninth IEEE international conference on tools with artificial intelligence, 1997 (pp. 558–567).Google Scholar
- 32.Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (1996). Advances in knowledge discovery and data mining (Vol. 21). Menlo Park: AAAI Press.Google Scholar
- 33.Yadav, A., & Jain, S. (2011). Analyses of web usage mining techniques to enhance the capabilities of E-learning environment. In 2011 international conference on emerging trends in networks and computer communications (ETNCC) (pp. 223–225).Google Scholar