Samira Aouidi
ABSTRACT
Social networks have arisen in the last years as powerful tools where people exchange knowledge and multimedia content. They help to share interests between groups of people with common features. Undoubtedly, there is an inherent social network in any e-learning system, where the main actors are teachers, learners and learning resources. New social environments for learning should appear to act as intelligent systems that better fit the needs of their users and especially students according to their interests, preferences, motivations, objectives and knowledge. Recently, there has been research work focused on Web Communities for learning and their formulation as Social Networks. Thus, social network analysis may be applied to infer group structures and to make intelligent recommendation systems and data mining.
In this paper, we propose an automatic learning environment based on the analysis of the social interactions that takes place between users-users and users-resources, the analysis is based on the history of interactions made by learners within the environment to deduce their interests in relation to a module, learners with similar interests will then be assigned to the same learning group in order to propose recommendations regarding their preferences, interests and needs. This system ensures that these recommendations will certainly improve the learning process by providing students with the best learning practices, the desirable collaborators, and the relevant resources that fit better their needs.
- Rahel Bekele : Computer-Assisted Learner Group Formation Based on Personality Traits. Thèse de doctorat, University Hamburg, Von-Melle-Park 3, 20146 Hamburg, 2005.Google Scholar
- Jane Brindley, Lisa Marie Blaschke et Christine Walti Creating effective collaborative learning groups in an online environment. The International Review of Research in Open and Distributed Learning, 10(3), 2009.Google Scholar
- Soane Amara, Joaquim Macedo, Fatima Bendella et et al. : Group formation in mobile computer supported collaborativelearningcontexts:A systematic literature review. Journal of Educational Technology & Society, 19(2):258273, 2016.Google Scholar
- Asma Ounnas, Hugh Davis et David Millard : A framework for semantic group formation. In Advanced Learning Technologies, 2008. ICALT'08. Eighth IEEE International Conference on, pages 3438. IEEE, 2008.Google Scholar
- DLV system. http://www.dlvsystem.com/dlv/, Dernière consultation en juin 2018.Google Scholar
- Maria-Iuliana Dascalu, Constanta-Nicoleta Bodea, Miltiadis Lytras, Patricia Ordoñez De Pablos et Alexandru Burlacu : Improving e-learning communities through optimal composition of multidisciplinary learning groups. Computers in Human Behavior, 30:362371, 2014.Google Scholar
- Abbas El Dor : Perfectionnement des algorithmes d'optimisation par essaim particulaire : applications en segmentation d'images et en électronique. Thèse de doctorat, UniversitéParis-Est, 2012.Google Scholar
- Ivan Srba et Maria Bielikova : Dynamic group formation as an approach to collaborative learning support. IEEE transactions on learning technologies, 8(2):173186, 2015.Google Scholar
- Nagdev Amruthnath et Tarun Gupta : Modied rank order clusteringalgorithm approach by including manufacturing data. IFAC-PapersOnLine, 49(5):138142, 2016.Google Scholar
- Yaqian Zheng, Chunrong Li, Shiyu Liu et Weigang Lu : An improved genetic approach for composing optimal collaborative learning groups. KnowledgeBased Systems, 139:214225, 2018.Google Scholar
- Liana Razmerita et Armelle Brun : Collaborative learning in heterogeneous classes. In The 3rd International Conference on Computer Supported Education, Noordwijkerhout, Netherlands : CSEDU, 2011, pages 189194, 2011.Google Scholar
- Howard Spoelstra, Peter Van Rosmalen, Tilly Houtmans et Peter Sloep : Team formation instruments to enhance learner interactions in open learning environments. Computers in Human Behavior, 45:1120, 2015.Google Scholar
- Yusof, N., Mansur, A. B. F., & Othman, M. S. (2011). Ontology of Moodle e-Learning System for Social Network Analysis. IEEE conference on open systems (ICOS2011)(pp.122--126).Google ScholarCross Ref
- Li, T., Yang, H., He, J., & Ai, Y. (2010). A Social Network Analysis methods based on ontology. Third international symposium on knowledge acquisition and modeling(pp.258--261).Google Scholar
- Halimi, K., & Seridi-Bouchelaghem, H. (2015). Semantic web based learning styles identification for social learning environments personalization. Web Intelligence, 3, 3--29.Google Scholar
- Obeid, C., Lahoud, I., El Khoury, H., & Champin, P.-A. (2018). Ontology-based recommender system in higher education. Companion proceedings of the the web conference 2018, WWW'18, international world wide web conferences steering committee(pp.1031--1034).Google ScholarDigital Library
Recommendations
Towards a Semantic Social Approach to enrich the Learner's Profile in Human Learning Environments
ICIST '19: Proceedings of the 9th International Conference on Information Systems and TechnologiesE-learning systems have benefited greatly from the concepts of the social web and the emerging technologies of the web semantic (2.0). In this context; the research questions that arise are as follows: How to effectively use the learner's social network ...
Link Analysis Application in Personalized E-learning Environment
CINC '09: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 01Personalized e-learning is a hot research topic. It can obviously improve the learning efficiency. A perfect e-learning system can recommend right learning content for right learner at right time. In order to find profit contents for different users, a ...
How to analyse a semantic social network of learners in a social learning environment?
Interactions among learners in e-learning systems enhance the learning process and cognitive level. To understand the learners' socialisation, the learners' social network analysis can provide more significant answers based on realistic models beyond ...
Comments