Personalized e-learning environment for bioinformatics

Abstract

In recent years, the pervasive use of computers and the Internet has created an unprecedented environment for e-learning. However, the rapid expansion in the number of disparate information sources and variety of data available affects e-learning significantly. Nonetheless, there has been a growing awareness that courseware should automatically adjust to the profiles of individual learners. Over the past few years, much effort has been expended to enable personalization for e-learning by semantic web techniques. Although the semantic web offers a theoretical framework for flexibility and interoperability in e-learning resources, there is no consensus ontology that can be used to describe learning profiles directly for personal e-learning environments. This means that their actual applications are as yet unknown. Positing that ontologies actually provide viable solutions for knowledge management, in this article, we present a three-module architecture for a personalized e-learning environment for bioinformatics. The architecture facilitates a personalized e-material recommender that does item-based collaborative filtering (CF) + adapted vector space model (VSM), explicit and implicit scoring, and a concept of tasks focused on rating literature for the e-learner. Meanwhile, the knowledge discovery process can be tailored to acquiring knowledge for professional requirements. Validation for our architecture is provided by a case study for biological institutions. The experimental results show that our architecture is helpful for professional requirements, improving recommendation quality, and satisfying users.