Enhancing e-learning effectiveness using an intelligent agent-supported personalized virtual learning environment: An empirical investigation

Dongming Xu, Wayne W. Huang, Huaiqing Wang, Jon Heales
2014 Information & Management  
Virtual Learning Environments (VLEs) developed under constructivism and embedded personalization learning functions have the potential to meet different requirements of different learners, and thus increase e-Learning effectiveness. We formulated internal personalized learning mechanisms by implementing intelligent agents in a VLE under a constructivist learning model, and further developed an e-Learning effectiveness framework by integrating educational and IS theories. An empirical field
more » ... iment involving 228 university students was conducted. The findings suggested that personalized e-learning facilities enhance online learning effectiveness in terms of examination, satisfaction, and self-efficacy criteria. A c c e p t e d M a n u s c r i p t 2 materials to match each learner's individual cognitive capability and style. Such a VLE should help online learners learn more and feel satisfied with the learning process. Recent research in the field of Intelligent Tutoring Systems (ITS) forms a major part of research into VLEs. With the growth of computing capabilities, more researchers have focused on VLEs to provide tailored learning material, instruction, and instant interaction to suit individual learners by using intelligent agent technology [12, 15, 21, 28] . Intelligent agents are autonomous and can engage in flexible, high-level interactions. A multi-agent system is a collection of autonomous agents that work together to solve problems that are beyond the capabilities of individual agents. They offer a new and appropriate way of developing complex systems, especially in open and dynamic environments. ITSs are intelligent learning systems whose components reflect the values of the particular view that they emphasise about the nature of that knowledge (the domain model), the learning model, and the teaching model. These emphasize the philosophy of learning under objectivist learning theory. However from the constructivist view, intelligent systems should provide a learning environment to meet the individual learner's needs. This type of ITS is flexible, giving rise to a Personalised VLE (PVLE), which supports e-learning by recognizing an online learner's learning stage and providing tailored instruction, including personalized learning materials, tests, instant interactions, etc. The literature suggests that instructional methods matching an individual's learning style are most effective for learning, and a computer-based education system with a personalizing component might be superior to a non-personalized one. We noted and decided to address the lack of investigation into personalization mechanisms implemented by intelligent agents.
doi:10.1016/j.im.2014.02.009 fatcat:5xvvxk3dfnaxdppvevrfhk6fzm