Selection and Composition of Personalization Parameters in Cloud

Integrating e-learning personalization systems in cloud computing environment is a relatively recent trend in technology enhanced learning. Cloud computing is a new model that allows users to access applications through Internet. It is characterized by several advantages such as reducing the cost of development, high availability of information, scalability and so on. In literature, several works have taken into consideration these advantages for applying this model in learning systems. However, very little research is available that focuses on selecting and assembling the best personalization parameters according to the teachers' preferences. This paper proposes a solution based on the cloud for the composition of personalization parameters with minimal cost. Through this solution, teachers can easily access these parameters from anywhere and anytime.

[1]  Mohamed Jemni,et al.  Metric-Based Approach for Selecting the Game Genre to Model Personality , 2016 .

[2]  Mohamed Jemni,et al.  Generalized metrics for the analysis of E-learning personalization strategies , 2015, Comput. Hum. Behav..

[3]  Mohamed Jemni,et al.  Enhanced Federation and Reuse of E-Learning Components Using Cloud Computing , 2014, ICSLE.

[4]  Costin Badica,et al.  Accommodating Learning Styles in an Adaptive Educational System , 2010, Informatica.

[5]  Jing Zhao,et al.  An Interactive and Personalized Cloud-Based Virtual Learning System to Teach Computer Science , 2013, ICWL.

[6]  K. Palanivel,et al.  Architecture Solutions to E-Learning Systems Using Service-Oriented Cloud Computing Reference Architecture , 2014 .

[7]  Jun Yan,et al.  Learner Profile Design for Personalized E-Learning Systems , 2009, 2009 International Conference on Computational Intelligence and Software Engineering.

[8]  Mohamed Jemni,et al.  Toward the reuse of E-Learning personalization systems , 2013, Fourth International Conference on Information and Communication Technology and Accessibility (ICTA).

[9]  Manju Bhaskar,et al.  Genetic Algorithm Based Adaptive Learning Scheme Generation For Context Aware E-Learning , 2010 .

[10]  Francisco Herrera,et al.  An Overview of E-Learning in Cloud Computing , 2012 .

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

[12]  Luisa M. Regueras,et al.  A Diversity-Enhanced Genetic Algorithm to Characterize the Questions of a Competitive e-Learning System , 2010, 2010 10th IEEE International Conference on Advanced Learning Technologies.

[13]  Mohamed Jemni,et al.  Learners' Working Memory Capacity Modeling Based on Fuzzy Logic , 2014, 2014 IEEE 14th International Conference on Advanced Learning Technologies.

[14]  Jiann-Min Yang,et al.  Virtual Personalized Learning Environment (VPLE) on the Cloud , 2011, WISM.

[15]  Mohamed Jemni,et al.  Optimal composition of e-leaming personalization parameters , 2015, 2015 5th International Conference on Information & Communication Technology and Accessibility (ICTA).

[16]  Ricardo Conejo,et al.  SIETTE: A Web-Based Tool for Adaptive Testing , 2004, Int. J. Artif. Intell. Educ..

[17]  Giuseppe M. L. Sarnè,et al.  EFFICIENT PERSONALIZATION OF E‐LEARNING ACTIVITIES USING A MULTI‐DEVICE DECENTRALIZED RECOMMENDER SYSTEM , 2010, Comput. Intell..

[18]  Mohamed Jemni,et al.  Optimal Composition of e-Learning Personalization Resources , 2016 .