Journal Article10.1504/IJTEL.2015.072810
Knowledge and intelligent computing methods in e-learning
Aditya Khamparia,Babita Pandey +1 more
53
TL;DR: A study of different individual KBM and ICM methods; and integrated KBS-ICM methods applicable to e-learning domain right from the mid 1990s to 2014, observing that a single KBM is not deployed to solve any e- learning problem.
read more
Abstract: E-learning is the use of technology that enables people to learn at anytime from anywhere. Various single knowledge-based methods KBM such as rule-base reasoning RBR and case-base reasoning CBR; and intelligent computing methods ICM such as genetic algorithm GA, particle swarm optimisation PSO, artificial neural network ANN, multi-agent systems MAS, ant colony optimisation ACO, fuzzy logic FL etc. Integrated KBM-ICM methods such as GA-CBR, ANN-RBR, GA-Ontology and ANN-Mining have been used in various e-learning contexts such as: the learning path generation, adaptive course sequencing and personalisation of recommended learning object etc. We have made a study of different individual KBM and ICM methods; and integrated KBS-ICM methods applicable to e-learning domain right from the mid 1990s to 2014. The study is presented in a tabular form, showing the KBM-ICM methods, e-learning problems to be addressed, specific features and the implementation in the e-learning domain. From the results, it is observed that a single KBM is not deployed to solve any e-learning problem. A single ICM and integrated KBM-ICM methods are used to solve various e-learning problems. The study and its presentation in the context help the novice researchers to resume their work in the area of e-learning systems.
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
Association of learning styles with different e-learning problems: a systematic review and classification
Aditya Khamparia,Babita Pandey +1 more
TL;DR: This study supports researchers, academicians and practitioners in effectively adopting learning styles and method correspond to learning problems and provides a deep insight into its state of art.
68
Metaheuristic-based adaptive curriculum sequencing approaches: a systematic review and mapping of the literature
Marcelo de Oliveira Costa Machado,Natalie Ferraz Silva Bravo,André Ferreira Martins,Heder S. Bernardino,Eduardo Barrére,Jairo Francisco de Souza +5 more
TL;DR: A systematic review and mapping of the literature to identify, analyze, and classify the published solutions related to the ACS problem addressed by metaheuristics and emphasizes the use of Swarm Intelligence and Genetic Algorithm.
30
Pedagogical Agents in an Adaptive E-learning System
Aqeel M. Ali. Hussein,Humam K. Majeed Al-Chalabi +1 more
- 01 Mar 2020
TL;DR: The main aim of this study is to discuss the importance of pedagogical agents in adaptive e-learning systems and to discuss different approaches of pedgeogy including the constructivist approach, collaborative approach, inquiry-based approach, integrative approach, reflective approach, objectivism approach, and competency-b ased approach.
A novel method of case representation and retrieval in CBR for e-learning
Aditya Khamparia,Babita Pandey +1 more
TL;DR: A novel method for representation and retrieval of cases in case based reasoning (CBR) as a part of e-learning system which is based on various student features is discussed, which integrated Artificial Neural Network with Data mining (DM) and CBR.
20
A comparative analysis of metaheuristics applied to adaptive curriculum sequencing
André Ferreira Martins,Marcelo de Oliveira Costa Machado,Heder S. Bernardino,Jairo Francisco de Souza +3 more
- 06 May 2021
TL;DR: In this paper, a procedure to create synthetic dataset to evaluate adaptive curriculum sequencing (ACS) approaches is presented and as a concept proof, analyzes metaheuristics usually used in ACS approaches: Genetic Algorithm, Particle Swarm Optimization (PSO), and Prey-Predator Algorithm using student's learning goals and their extrinsic and intrinsic information.
References
Personalized e-learning system using Item Response Theory
TL;DR: This study proposes a personalized e-learning system based on Item Response Theory (PEL-IRT) which considers both course material difficulty and learner ability to provide individual learning paths for learners and shows that applying Item Response theory to Web-based learning can achieve personalized learning and help learners to learn more effectively and efficiently.
541
Building a recommender agent for e-learning systems
Osmar R. Zaïane
- 03 Dec 2002
TL;DR: The use of web mining techniques are suggested to build such an agent that could recommend on-line learning activities or shortcuts in a course web site based on learners' access history to improve course material navigation as well as assist the online learning process.
456
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.
247
Knowledge Discovery with Genetic Programming for Providing Feedback to Courseware Authors
TL;DR: A specific data mining tool is presented that can help non-experts in data mining carry out the complete rule discovery process, and its utility is demonstrated by applying it to an adaptive Linux course that was developed.
A learning style classification mechanism for e-learning
TL;DR: Experimental results indicate that the proposed classification mechanism can effectively classify and identify students' learning styles and is implemented on an open-learning management system.