مدل گنجاندن الکترونیکی برای دوره یادگیری الکترونیکی آمیخته
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|41012||2015||10 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Procedia Computer Science, Volume 65, 2015, Pages 744–753
This study addresses the e-inclusion problem that relates to the inclusion of as many individuals as possible to enjoy benefits of information and communication technology. Despite the fact that European Union accepted e-inclusion declaration in 2006 which aims to reduce disparities that exist among individuals and to improve the level of e-skills among people, nowadays e-inclusion problem still exists. Therefore it is necessary to find out new approach to promote e-inclusion in society. We propose a more nuanced design approach that takes into account student's satisfaction with e-learning environment and e-materials, student's ability to learn, instructor willingness to share knowledge and others factors. Moreover we believe that e-inclusion means not only high level of digital skills but also the usage of these digital skills to benefit from technologies. To obtain predictors for algorithms we did e-inclusion data domain study based on knowledge management theory. The aim of proposed work is to present e-inclusion theoretical model which is based on integration of several algorithms as multiply linear regression and cluster analysis. These algorithms were calculated based on statistical data obtained on evaluating a group of five hundred blended e-course learners. In this paper we propose architecture designed to predict e-inclusion degree of student based on machine learning and intelligent agent approach. We identified two main processes in the e-inclusion prediction system. The first process consists of agent learning activities. Intelligent agents learn the most appropriate algorithm to predict e-inclusion degree of student based on linear regression or cluster analysis. The second process includes activities to predict e-inclusion degree of student. This process covers analysis of e-inclusion risks and communication between student and instructor also. Proposed e-inclusion model consists of goal diagram, use cases diagrams and main algorithms of the system. As the result of the e-inclusion model is prediction of e-inclusion degree of person as well as e-inclusion risk factors for person, for instance inappropriate e-learning materials or no interest to learn, or dissatisfaction with e-learning environment, or others factors.