Adaptive E-Learning System

Adaptive e-learning system is widely accepted system in which learners get information as per their own preferences either hidden or expressed. Hence, adaptive learning process maximizes learning and helps learners to achieve the course targets successfully in a lesser time and in a very cost-effective manner. It has immense capability to diagnose complete learner’s behavior as well as provide suitable course recommendations, automated personalized contents, and due guidance for learning path as per requirements detected from the choices made by the learner. In this chapter, a brief introduction to adaptive and personalized e-learning system is presented. This chapter also discusses the detailed idea of educational data mining in e-learning domain, an adaptive e-learning system followed by the challenges and motivation for choosing such kind of research issue. This chapter clearly identifies what, why and how solution of the problem can be derived and given the detailed idea about the processes. The chapter highlights the requirement of changes from stereotyped e-learning to adaptive personalized e-learning system. Finally, the chapter provides an introduction to all about the fundamentals concepts and related work of adaptive e-learning applications and logical structure for the processing of adapting learning contents.

[1]  Zorica Bogdanovic,et al.  Providing Adaptivity in Moodle LMS Courses , 2012, J. Educ. Technol. Soc..

[2]  Swagatam Das,et al.  Proceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015) , 2015, Advances in Intelligent Systems and Computing.

[3]  T. V. Geetha,et al.  Learning content design and learner adaptation for adaptive e-learning environment: a survey , 2015, Artificial Intelligence Review.

[4]  Michel C. Desmarais,et al.  A review of recent advances in learner and skill modeling in intelligent learning environments , 2012, User Modeling and User-Adapted Interaction.

[5]  Jay F. Nunamaker,et al.  Powering E-Learning In the New Millennium: An Overview of E-Learning and Enabling Technology , 2003, Inf. Syst. Frontiers.

[6]  Antonio Garrido,et al.  Student-oriented planning of e-learning contents for Moodle , 2015, J. Netw. Comput. Appl..

[7]  Miguel Ángel Conde González,et al.  Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning , 2014, Comput. Hum. Behav..

[8]  Chieu Vu Minh,et al.  Constructivist learning : an operational approach for designing adaptive learning environments supporting cognitive flexibility/ , 2005 .

[9]  Miltiadis D. Lytras,et al.  A recommender agent based on learning styles for better virtual collaborative learning experiences , 2015, Comput. Hum. Behav..

[10]  Sofia B. Dias,et al.  Fuzzy cognitive mapping of LMS users' Quality of Interaction within higher education blended-learning environment , 2015, Expert Syst. Appl..

[11]  Yoshitaka Sakurai,et al.  A Case Study on Using Data Mining for University Curricula , 2012, 2012 IEEE 12th International Conference on Advanced Learning Technologies.

[12]  Adnan Baki,et al.  Design and development of an innovative individualized adaptive and intelligent e-learning system for teaching-learning of probability unit: Details of UZWEBMAT , 2013, Expert Syst. Appl..

[13]  Peter Brusilovsky,et al.  User Models for Adaptive Hypermedia and Adaptive Educational Systems , 2007, The Adaptive Web.

[14]  Maria Satratzemi,et al.  Comparing LMS and AEHS: Challenges for Improvement with Exploitation of Data Mining , 2014, 2014 IEEE 14th International Conference on Advanced Learning Technologies.

[15]  Birgitta König-Ries,et al.  Progressor: social navigation support through open social student modeling , 2013, New Rev. Hypermedia Multim..

[16]  Mirjam Köck Computational Intelligence for Communication and Cooperation Guidance in Adaptive E-Learning Systems , 2008, LWA.

[17]  Maria Dominic,et al.  A Framework to Formulate Adaptivity for Adaptive e-Learning System Using User Response Theory , 2015, International Journal of Modern Education and Computer Science.

[18]  Àngela Nebot,et al.  Applying Data Mining Techniques to e-Learning Problems , 2007 .

[19]  Mehri Mohammad Bagheri Intelligent and Adaptive Tutoring Systems: How to Integrate Learners , 2015 .

[20]  Chih-Ping Chu,et al.  A learning style classification mechanism for e-learning , 2009, Comput. Educ..

[21]  Sebastián Ventura,et al.  Educational Data Mining: A Review of the State of the Art , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[22]  M. Goyal,et al.  Emerging information technology and contemporary challenging R & D problems in the area of learning: An artificial intelligence approach , 2012, 2012 IEEE International Conference on Engineering Education: Innovative Practices and Future Trends (AICERA).

[23]  Ray-I Chang,et al.  Data mining for providing a personalized learning path in creativity: An application of decision trees , 2013, Comput. Educ..