Adaptive Virtual Learning Environment based on Learning Styles for Personalizing E-learning System: Design and Implementation
Renato R. Maaliw III

Renato R. Maaliw III, DIT , Southern Luzon State University, Lucban, Quezon, Philippines .

Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3398-3306 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8901038620/2020©BEIESP | DOI: 10.35940/ijrte.F8901.038620
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: Most virtual learning environment fails to recognize that students have different needs when it comes to learning. With the evolving characteristics and tendencies of students, these learning environments must provide adaptation and personalization features for adaptive learning materials, course content and navigational designs to support student’s learning styles. Based from the data mining results of learner behavioral features of five hundred seven (507) tertiary students, an accurate model for classification of student’s learning styles were derived using J48 decision tree algorithm. The model was implemented in a prototype using a framework and a proposed system architectural design of an adaptive virtual learning environment. The study resulted in the development of an adaptive virtual learning environment prototype where learner’s preferences are dynamically diagnosed to intelligently personalize the course content design and user interfaces for them.
Keywords: Adaptive E-Learning, Personalization, Data Mining, Prototype
Scope of the Article: Data Mining.