Personalized Learning in a Virtual Learning Environment Using Modification of Objective Distance

For a virtual learning environment (VLE), assessing student's competency is important because the proper feedback for students to achieve their goals is re-quired differently. Therefore, it is necessary to have an appropriate measurement for evaluating student's competency so that personalized support can be provided. This study proposes the attribute to determine an individual's competency by modifying the Objective Distance, which is the distance from the current status of a student's competency towards a satisfied competency. In this study, the Objective Distance is modified based on the assumption that the entire course's achievement is obtained from different learning objects' priorities. The proposed attribute was studied to determine to what extent it would represent diverse learners' competencies. The study was done with 55 students in an online learning course through the VLE. The students could choose learning objects freely. The learning enhancement was done through the analysis of pre-test and post-test for all learning objects. The classification results with K-Nearest Neighbor and Artificial Neural Network show that both the original and the modified Objective Distance are effectively used to assess the individual's competency. Overall, the modified Objective Distance performs better than the original one.