Case-Based Reasoning for E-Learning Systems: State of the Art

Author(s):  
Abroun Soundoss ◽  
Ghailani Mohamed ◽  
Fennan Abdelhadi
2016 ◽  
Vol 11 (02) ◽  
Author(s):  
Vandana Jindal ◽  
Vandana Jindal

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.


2017 ◽  
Vol 12 (01) ◽  
Author(s):  
Vandana Jindal ◽  
Pankaj Kumar Jain

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.


Author(s):  
Brahim Faqihi ◽  
Najima Daoudi ◽  
Rachida Ajhoun

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.


2018 ◽  
Vol 46 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Shahid Farid ◽  
Rodina Ahmad ◽  
Mujahid Alam ◽  
Atif Akbar ◽  
Victor Chang

Purpose The purpose of this study is to propose a sustainable quality assessment approach (model) for the e-learning systems keeping software perspective under consideration. E-learning is becoming mainstream due to its accessibility, state-of-the-art learning, training ease and cost effectiveness. However, the poor quality of e-learning systems is one of the major causes of several failures reported. Moreover, this arena lacks well-defined quality assessment measures. Hence, it is quite difficult to measure the overall quality of an e-learning system effectively. Design/methodology/approach A pragmatic mixed-model philosophy was adopted for this study. A systematic literature review was performed to identify existing e-learning quality models and frameworks. Semi-structured interviews were conducted with e-learning experts following empirical investigations to identify the crucial quality characteristics of e-learning systems. Various statistical tests like principal component analysis, logistic regression, chi-square and analysis of means were applied to analyze the empirical data. These led to an adequate set of quality indicators that can be used by higher education institutions to assure the quality of e-learning systems. Findings A sustainable quality assessment model for the information delivery in e-learning systems in software perspective has been proposed by exploring the state-of-the-art quality assessment/evaluation models and frameworks proposed for the e-learning systems. The proposed model can be used to assess and improve the process of information discovery and delivery of e-learning. Originality/value The results obtained led to conclude that very limited attention is given to the quality of e-learning tools despite the importance of quality and its effect on e-learning system adoption and promotion. Moreover, the identified models and frameworks do not adequately address quality of e-learning systems from a software perspective.


Author(s):  
Natalia Martínez Sánchez ◽  
María M. García Lorenzo ◽  
Zoila Zenaida García Valdivia ◽  
Gheisa Ferreira Lorenzo

Los Sistemas de Enseñanza-Aprendizaje Inteligentes son programas que portan conocimientos de cierto contenido mediante un proceso interactivo individualizado.En este trabajo se expone un modelo que integra el paradigma del Razonamiento Basado en Casos y los Sistemas de Enseñanza-Aprendizaje Inteligentes que favorece la concepción de estos sistemas a usuarios no expertos en informática, teniendo en cuenta las facilidades y naturalidad del enfoque basado en casos. AbstractThe Intelligent Teaching-Learning Systems are programs which carry knowledge about certain subject through an individualized interactive process.In the present work, a model which integrates the case-based reasoning paradigm and the Intelligent Teaching-Learning Systems is proposed, the model favors the design of these systems by users no necessarily experts in the informatics field, taking into account the facilities and naturalness of the case-based approach.


2005 ◽  
Vol 20 (3) ◽  
pp. 201-202 ◽  
Author(s):  
DAVID W. AHA ◽  
CINDY MARLING ◽  
IAN WATSON

We are delighted to present this special issue of The Knowledge Engineering Review, as it marks a significant accomplishment of the case-based reasoning (CBR) community. Its 19 commentaries, written by 41 authors, represent a compendium on the state-of-the-art in CBR. These evolved from a 2003 workshop that was held at Waiheke Island and Queenstown, New Zealand and chaired by Alec Holt and Ian Watson. The workshop's delegates identified the primary topics of CBR research and application, selected representative influential publications for each topic, and were encouraged to co-author commentaries on each topic with other CBR experts who were unable to attend. These collaborations produced the articles you now see. While several reviews exist on CBR (e.g. Marir & Watson, 1994; López de Mántaras & Plaza, 1997; Lenz et al., 1998), few have been published recently or have similar historical and subject breadth.


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