Knowledge and intelligent computing methods in e-learning
Aditya Khamparia, Babita Pandey · International Journal of Technology Enhanced Learning · 2015
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.