Personalised learning object based on multi-agent model and learners’ learning styles
Year: 2011
Type: article
Abstract: A multi-agent model is proposed in which learning styles and a word analysis technique to create a learning object recommendation system are used. On the basis of a learning style-based design, a concept map combination model is proposed to filter out unsuitable learning concepts from a given course. Our learner model classifies learners into eight styles and implements compatible computational methods consisting of three recommendations: i) non-personalised, ii) preferred feature-based, and iii) neighbour-based collaborative filtering. The analysis of preference error (PE) was performed by comparing the actual preferred learning object with the predicted one. In our experiments, the feature-based recommendation algorithm has the fewest PE.
Author: Noppamas Pukkhem
Institution: Chulalongkorn University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W2164469481