Case-based reasoning for adaptive content delivery in e-learning systems using mobile agents

2011 ◽  
pp. 48-53
Author(s):  
S. R. Mangalwede ◽  
D. H. Rao
Keyword(s):  
Mobile Agents ◽  
Content Delivery ◽  
Learning Systems ◽  
Case Based Reasoning ◽  
E Learning ◽  
Adaptive Content Delivery ◽  
Case Based ◽  
Adaptive Content

Adaptive Content Delivery in E-Learning Systems using Mobile Agents

2012 ◽  
pp. 148-166 ◽  
Author(s):  
S. R. Mangalwede ◽  
D. H. Rao
Keyword(s):  
Mobile Agents ◽  
Educational Background ◽  
Content Delivery ◽  
Case Based Reasoning ◽  
Distributed Computing Systems ◽  
Learning Content ◽  
E Learning ◽  
Networking Technologies ◽  
Adaptive Content Delivery ◽  
Adaptive Content

The e-Learning refers to the use of networking technologies to create, foster, deliver and facilitate learning anytime, anywhere. This chapter discusses our research on personalization of e-Learning content based on the learner’s profile. After justifying the feasibility of using mobile agents in distributed computing systems for information retrieval, processing and mining, the authors deal with the relevance of mobile agents in e-Learning domain. The chapter discusses the proposed Case-Based Reasoning (CBR) as an approach to context-aware adaptive content delivery. Different parameters like technological, cultural and educational background of a learner are taken as the basis for forming the case-base that determines the type of content to be delivered. Along with the CBR, a diagnostic assessment to gauge an insight into the student’s current skills is done to determine the type of content to deliver. The implementation observations of such implementation vis-à-vis traditional e-Learning are also documented.

Case-Based Reasoning and Learning Systems

2016 ◽  
Vol 11 (02) ◽  
Author(s):  
Vandana Jindal ◽  
Vandana Jindal
Keyword(s):  
Intelligent Systems ◽  
New Technology ◽  
Learning Systems ◽  
Case Based Reasoning ◽  
Research Issues ◽  
Technical Aspects ◽  
E Learning ◽  
Teaching Learning ◽  
And Training ◽  
Case Based

Case based reasoning (CBR) technology presents a foundation for a new technology of building intelligent systems for teaching, learning and training. This Technology directly addresses the problems found in the traditional artificial intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. This paper discusses the CBR methodology, the research issues and technical aspects of implementing effective intelligent e-learning systems. Some examples of successful applications in different domain are also given in the paper.

Case-Based Reasoning and Learning Systems

2017 ◽  
Vol 12 (01) ◽  
Author(s):  
Vandana Jindal ◽  
Pankaj Kumar Jain
Keyword(s):  
Intelligent Systems ◽  
New Technology ◽  
Learning Systems ◽  
Case Based Reasoning ◽  
Research Issues ◽  
Technical Aspects ◽  
E Learning ◽  
Teaching Learning ◽  
And Training ◽  
Case Based

Case based reasoning (CBR) technology presents a foundation for a new technology of building intelligent systems for teaching, learning and training. This Technology directly addresses the problems found in the traditional artificial intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. This paper discusses the CBR methodology, the research issues and technical aspects of implementing effective intelligent e-learning systems. Some examples of successful applications in different domain are also given in the paper.

scholarly journals Design of an Intelligent Educational Resource Production System

2018 ◽  
Vol 13 (12) ◽  
pp. 4 ◽  
Author(s):  
Brahim Faqihi ◽  
Najima Daoudi ◽  
Rachida Ajhoun
Keyword(s):  
Production System ◽  
Target System ◽  
Open Educational Resource ◽  
Case Based Reasoning ◽  
Educational Resource ◽  
Face To Face ◽  
Massive Online Open Course ◽  
E Learning ◽  
Face To Face Learning ◽  
Case Based

In the field of learning, we are witnessing more and more the introduction of new environments in order to better meet the specific needs of the main actors of the process. The shift from face-to-face learning to distance learning or e-learning has overcome some of the challenges of availability, location, prerequisites, but has been rapidly impacted by the development of mobile technology. As a result, m-learning appeared and quickly evolved into p-learning. The arrival of the "Open Software" concept has given birth to several "open-something" initiatives, among which are the Open Educational Resource (OER) and the Massive Online Open Course (MOOC). These learning resources have also made progress, although they are fairly recent. Admittedly, this diversity of environments offers a wealth and a multitude of pedagogical resources. However, the question of the capitalization of contents, knowledge and know-how of each of these environments is necessary. How can the exchange and reuse of pedagogical resources be guaranteed between these different learning environ-ments? otherwise-said how to guarantee the interoperability of these resources? In order to contribute to the creation of an pedagogical heritage, we propose to design a case-based system allowing the author, when creating a course in a particular context and environment, to exploit the resources that are already available. The goal is to put in place an intelligent production system based on case-based reasoning. It is based on four phases ranging from indexing to reuse, through the similarity measurement and the evaluation. In the first part, we will detail the evolution of learning environments. In the second part, we will review the existing course production platforms, their prin-ciples and their challenges. In the third part, we will present case-based reasoning systems, and then we will introduce our target system.

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