Journal Article10.1016/J.ESWA.2006.05.019
Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach
TL;DR: A genetic-based curriculum sequencing approach that will generate a personalized curriculum sequencing and empirical research is used to indicate that the proposed approach can generate the appropriate course materials for learners, based on individual learner requirements, to help them to learn more effectively in a Web-based environment.
read more
Abstract: The Internet and the World Wide Web in particular provide a unique platform to connect learners with educational resources. Educational material in hypermedia form in a Web-based educational system makes learning a task-driven process. It motivates learners to explore alternative navigational paths through the domain knowledge and from different resources around the globe. Consequently, many researchers have focused on developing e-learning systems with personalized learning mechanisms to assist on-line Web-based learning and to adaptively provide learning paths. However, although most personalized systems consider learner preferences, interests and browsing behaviors when providing personalized curriculum sequencing services, these systems usually neglect to consider whether learner ability and the difficulty level of the recommended curriculums are matched to each other. Therefore, our proposed approach is based on the evolvement technique through computerized adaptive testing (CAT). Then the genetic algorithm (GA) and case-based reasoning (CBR) are employed to construct an optimal learning path for each learner. This paper makes three critical contributions: (1) it presents a genetic-based curriculum sequencing approach that will generate a personalized curriculum sequencing; (2) it illustrates the case-based reasoning to develop a summative examination or assessment analysis; and (3) it uses empirical research to indicate that the proposed approach can generate the appropriate course materials for learners, based on individual learner requirements, to help them to learn more effectively in a Web-based environment.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms
Khalid Colchester,Hani Hagras,Daniyal M. Alghazzawi,Ghadah Aldabbagh +3 more
- 01 Jan 2017
TL;DR: A survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e- learning environments.
Smart Education with artificial intelligence based determination of learning styles
Richa Bajaj,Vidushi Sharma +1 more
TL;DR: A framework of a tool is proposed here, which takes into consideration multiple learning models and artificial intelligence techniques for determining students’ learning styles, and is suggested that this tool be deployed in a cloud environment to provide a scalable solution that offers easy and rapid determination of learning styles.
245
Learning in a u-Museum: Developing a context-aware ubiquitous learning environment
Chia-Chen Chen,Tien-Chi Huang +1 more
TL;DR: A context-aware ubiquitous learning system based on radio-frequency identification, wireless network, embedded handheld device, and database technologies to detect and examine real-world learning behaviors of students is proposed and demonstrated that this innovative approach can enhance their learning intention.
238
Grand challenges
Raymond Lister
- 01 Jun 2005
TL;DR: This column will concentrate on some of the problems/solutions from a recent paper on the "Grand Challenges" for Computing Education that might be answered via education research.
198
Learning path recommendation based on modified variable length genetic algorithm
TL;DR: This paper presents an effective learning path recommendation system (LPRS) for e-learners through a variable length genetic algorithm (VLGA) by considering learners’ learning styles and knowledge levels and demonstrates the effectiveness of the proposed LPRS in e-learning environment.
138
References
•Book
Modern Information Retrieval
Ricardo Baeza-Yates,Berthier Ribeiro-Neto +1 more
- 15 May 1999
TL;DR: In this article, the authors present a rigorous and complete textbook for a first course on information retrieval from the computer science (as opposed to a user-centred) perspective, which provides an up-to-date student oriented treatment of the subject.
Term Weighting Approaches in Automatic Text Retrieval
Gerard Salton,Chris Buckley +1 more
TL;DR: This paper summarizes the insights gained in automatic term weighting, and provides baseline single term indexing models with which other more elaborate content analysis procedures can be compared.
•Book
Handbook of Genetic Algorithms
Lawrence Davis
- 01 Jan 1991
TL;DR: This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems, and introduces the fundamental genetic algorithm (GA), and shows how the basic technique may be applied to a very simple numerical optimisation problem.
8.2K
Lecture Notes in Artificial Intelligence
P. Brezillon,P. Bouquet +1 more
- 01 Jan 1999
TL;DR: The topics in LNAI include automated reasoning, automated programming, algorithms, knowledge representation, agent-based systems, intelligent systems, expert systems, machine learning, natural-language processing, machine vision, robotics, search systems, knowledge discovery, data mining, and related programming languages.
7.5K
Intelligent tutoring systems
TL;DR: Computer tutors based on a set of pedagogical principles derived from the ACT theory of cognition have been developed for teaching students to do proofs in geometry and to write computer programs in the language LISP.
3.2K