An Automatic Approach for Identifying Triple-Factor in e-Learning Process

Abstract

Developing the personalization in an e-Learning environment is a way to provide learning material which matches the students' characteristics. In order to develop an e-Learning which supports personalization should be designed to facilitate an factors influence the success of students' learning, such as learning style, motivation, knowledge ability, etc. Considering the existence of those factors for the personalization in e-Learning system can affect students' performance and makes learning easier for them. Therefore, in this study, we propose a parameter (learning behavior patterns) based on learning activities (learning behavior) students for identifying learning styles, motivation, and knowledge ability (triple-factor) in e-Learning system. Our approach is to identify the triple-factor by simply observing his/her learning behavior recorded in data log, without asking the student to answer any questions of questionnaire. Each data log gives an indication related to identification the triple-factor in e-Learning system. The identification of those factor aims at inferring the learning styles, motivation and knowledge ability states. Then, it uses as the basis for dynamic personalization functionality.