Document Details

Document Type : Project 
Document Title :
Inferring Learning Styles for Adaptive E-Learning Environments (To enhance learning with low cost)
التعرف على طريقة التعلم في بيئة التعليم عن بعد
 
Subject : Inferring Learning Styles for Adaptive E-Learning Environments (To enhance learning with low cost) 
Document Language : Arabic 
Abstract : Computer-aided instruction has been playing a crucial role in supporting learning. Early computer-aided instruction delivered a single style of content to all learners without any consideration of their learning styles. Recently, systems have been developed to adapt content based on the learners’ learning styles. These systems use instruments, such as questionnaires and interview, to infer the learning styles. Using such instruments costs learners extra time, and they have to be done explicitly. In addition, these systems do not adapt the learning styles of learners over time. These drawbacks are the problem addressed in this study. The purpose of this research is to infer the learning styles of students while they are browsing online instruction. This indicates that the inferred process can be done implicitly, in less time, and repeated over time. The focus of this study is on the three cognitive learning styles: holist, serialist, and versatile. In order to achieve this goal, a classification system will be developed, which contains three online lessons and uses two mechanisms (Tracking and Questions) to extract useful information about the users’ behaviors. The extracted features will be used by a collection of classifiers to infer the users’ learning styles. These results will be compared with those of the Study Preference Questionnaire by calculating the Pearson correlation between them. 
Publishing Year : 1428 AH
2007 AD 
Sponsor Name : King Abdulaziz University 
Sponsorship Year : 1428 AH
2007 AD 
Added Date : Saturday, October 16, 2010 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
دانيال محمد الغزاويAlghazzawi, Daniyal MohammedInvestigatorDoctorate 

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