| کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
|---|---|---|---|---|
| 484519 | 703275 | 2015 | 10 صفحه PDF | دانلود رایگان |
کلمات کلیدی
1. پیشگفتار
2. تکنولوژی ها و روش های پیش بینی دسترسی الکترونیکی
2.1 تاریخچه تحقیق
شکل 1. فرایندهای اصلی سیستم پیش بینی براساس فراگیری ماشین
2.2 دامنه آنالیز دسترسی الکترونیکی و انتخاب الگوریتم ها برای پیش بینی
2.3 مقایسه با روش های دیگر
3. طراحی مدل دسترسی الکترونیکی
3.1 روش
3.2 مدل اهداف در سیستم پیش بینی دسترسی الکترونیکی
شکل 2. مدل اهداف برای سیستم پیش بینی دسترسی الکترونیکی
3.3 بیشترین موارد استفاده از مدل پیش بینی دسترسی الکترونیکی
شکل 4. مورد استفاده مدرس از مدل دسترسی الکترونیکی
4. الگوریتم های مدل پیش بینی دسترسی الکترونیکی
شکل 5. الگوریتم یادگیری مدل دسترسی الکترونیکی
شکل 6. الگوریتم پیش‌بینی در مدل دسترسی الکترونیکی
5. نتیجه
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.
Journal: Procedia Computer Science - Volume 65, 2015, Pages 744-753