Fuzzy Student Modeling for Personalization of e-Learning Courses

In the context of e-learning courses, personalization is a more and more studied issue, being its advantage in terms of time and motivations widely proved. Course personalization basically means to understand student’s needs: to this aim several Artificial Intelligence methodologies have been used to model students for tailoring e-learning courses and to provide didactic strategies, such as planning, case based reasoning, or fuzzy logic, just to cite some of them. Moreover, in order to disseminate personalised e-learning courses, the use of known and available Learning Management System is mandatory.

[1]  Sabine Graf,et al.  Providing Adaptive Courses in Learning Management Systems with Respect to Learning Styles , 2007 .

[2]  Carla Limongelli,et al.  A Teacher Model to Speed Up the Process of Building Courses , 2013, HCI.

[3]  Jose L. Marzo,et al.  User Modeling, Adaption and Personalization - 19th International Conference, UMAP 2011, Girona, Spain, July 11-15, 2011. Proceedings , 2011, UMAP.

[4]  Vania Dimitrova,et al.  Using Fuzzy Techniques to Model Students in Web-Based Learning Environments , 2003, KES.

[5]  Carlo Strapparava,et al.  Adaptive Hypermedia and Adaptive Web-Based Systems, 5th International Conference, AH 2008, Hannover, Germany, July 29 - August 1, 2008. Proceedings , 2008, AH.

[6]  Carolyn Penstein Rosé,et al.  The Beginning of a Beautiful Friendship? Intelligent Tutoring Systems and MOOCs , 2015, AIED.

[7]  Carla Limongelli,et al.  An Ontology-Driven OLAP System to Help Teachers in the Analysis of Web Learning Object Repositories , 2010, Inf. Syst. Manag..

[8]  Marco Temperini,et al.  Selection and sequencing constraints for personalized courses , 2010, 2010 IEEE Frontiers in Education Conference (FIE).

[9]  Carla Limongelli,et al.  LS-Plan: An Effective Combination of Dynamic Courseware Generation and Learning Styles in Web-Based Education , 2008, AH.

[10]  Essam Kosba,et al.  Generating computer-based advice in web-based distance education environments , 2004 .

[11]  Marco Temperini,et al.  Average effort and average mastery in the identification of the Zone of Proximal Development , 2013, 2013 17th International Conference on System Theory, Control and Computing (ICSTCC).

[12]  Carla Limongelli,et al.  The Lecomps5 framework for personalized web-based learning: A teacher's satisfaction perspective , 2011, Comput. Hum. Behav..

[13]  Carla Limongelli,et al.  Virtual industrial training: Joining innovative interfaces with plant modeling , 2012, 2012 International Conference on Information Technology Based Higher Education and Training (ITHET).

[14]  Marco Temperini,et al.  Collaborative Projects and Self Evaluation within a Social Reputation-Based Exercise-Sharing System , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[15]  Kai Warendorf,et al.  Application of fuzzy logic techniques in the BSS1 tutoring system , 1997 .

[16]  A. Conchon,et al.  Applications and services , 2001 .

[17]  Kinshuk,et al.  Analysing the Behaviour of Students in Learning Management Systems with Respect to Learning Styles , 2008, Advances in Semantic Media Adaptation and Personalization.

[18]  Carla Limongelli,et al.  Personalized e-learning in Moodle: the Moodle_LS System , 2011 .

[19]  Konstantina Chrysafiadi,et al.  Dynamically Personalized E-Training in Computer Programming and the Language C , 2013, IEEE Transactions on Education.

[20]  Maria De Marsico,et al.  A Strategy to Join Adaptive and Reputation-Based Social-Collaborative E-Learning, Through the Zone of Proximal Development , 2013, Int. J. Distance Educ. Technol..

[21]  Carla Limongelli,et al.  Supporting Teachers to Retrieve and Select Learning Objects for Personalized Courses in the Moodle_LS Environment , 2012, 2012 IEEE 12th International Conference on Advanced Learning Technologies.

[22]  Maria De Marsico,et al.  The Definition of a Tunneling Strategy between Adaptive Learning and Reputation-based Group Activities , 2011, 2011 IEEE 11th International Conference on Advanced Learning Technologies.

[23]  Giuseppe Sansonetti,et al.  Enhancing traditional local search recommendations with context-awareness , 2011, UMAP'11.

[24]  Carla Limongelli,et al.  A Teaching-Style Based Social Network for Didactic Building and Sharing , 2013, AIED.

[25]  Nicola Capuano,et al.  ABITS: An Agent Based Intelligent Tutoring System for Distance Learning , 2014 .

[26]  Alenka Kavcic,et al.  Fuzzy user modeling for adaptation in educational hypermedia , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[27]  Giuseppe Sansonetti,et al.  Exploiting Web Browsing Activities for User Needs Identification , 2014, 2014 International Conference on Computational Science and Computational Intelligence.

[28]  Lakhmi C. Jain,et al.  Knowledge-Based Intelligent Information and Engineering Systems , 2004, Lecture Notes in Computer Science.

[29]  Alessandro Micarelli,et al.  Infoweb: An adaptive information filtering system for the cultural heritage domain , 2003, Appl. Artif. Intell..

[30]  Filippo Sciarrone.  An Extension of the Q Diversity Metric for Information Processing in Multiple Classifier Systems: a Field Evaluation , 2013, Int. J. Wavelets Multiresolution Inf. Process..