Learning Object Retrieval and Aggregation Based on Learning Styles

Abstract

The main goal of this article is to develop a Management System for Merging Learning Objects (msMLO), which offers an approach that retrieves learning objects (LOs) based on students’ learning styles and term-based queries, which produces a new outcome with a better score. The msMLO faces the task of retrieving LOs via two steps: The first step ranks LOs using a unified learning style model and creates better LOs by merging the top-ranked LOs. The second step maps LOs onto a hierarchy of concepts to avoid duplicated topics. An experiment was conducted to evaluate this approach in an applied computing course. A total of 84 students were randomly split into four groups. The experimental results demonstrated that the msMLO is a promising approach that provides useful LOs based on students’ learning styles and the merging process for reusing stored LOs. Furthermore, this approach improves overall student learning performance and reduces the number of LOs reviewed.