Detecting Students Learning Styles and Knowledge Level in E-Learning Environment

摘要

In the past years building an adaptive e-learning environment was an attractive point to any research, and Nowadays this point has been tremendously developed to include not only adaptive e-learning environment, but also it will be depending on the intellectual dexterity and learning style of the learner as the course presentation will no longer be static but it will be established on the LS (learning style) of the learner. Many types of research have presented solutions for building an adaptive e-learning environment based on the student LS using Felder Silverman model either through questionnaire or by automatic detection through literature-based method, neural networks, however the obstacle in using Questionnaire only was that the answers won’t be accurate as the student can just choose any answer to finish the questionnaire, and If it’s depending on the automatic detection only it will take time till the student interact with the provided system to be able to conclude the student behavior. In our paper we will be combining both techniques with the help of the Ontology to detect the accurate student LS and detect the student knowledge through the complexity measurement of each question using six measures: A) external domain references, B) explicitness, C) linguistic complexity, D) conceptual complexity, E) level of difficulty F) intellectual complexity (Bloom level) to detect the knowledge level of the student. Finally, our paper aims to eliminate the failure of students in any course as the student will either pass or drop the course and if the student passed, then he/she will have the required minimum knowledge from the course.