Visually Enhanced E-learning Environments Using Deep Cross-Medium Matching
عنوان مقاله: Visually Enhanced E-learning Environments Using Deep Cross-Medium Matching
شناسه ملی مقاله: ICELEARNING13_019
منتشر شده در سیزدهمین کنفرانس سالانه یادگیری و یاددهی الکترونیک در سال 1397
شناسه ملی مقاله: ICELEARNING13_019
منتشر شده در سیزدهمین کنفرانس سالانه یادگیری و یاددهی الکترونیک در سال 1397
مشخصات نویسندگان مقاله:
Mozhdeh Dokhani - Department of Computer Engineering Khatam University Tehran, Iran
Babak Majidi - Department of Computer Engineering Khatam University Tehran, Iran
Ali Movaghar - Department of Computer Engineering Sharif University of Technology Tehran, Iran
خلاصه مقاله:
Mozhdeh Dokhani - Department of Computer Engineering Khatam University Tehran, Iran
Babak Majidi - Department of Computer Engineering Khatam University Tehran, Iran
Ali Movaghar - Department of Computer Engineering Sharif University of Technology Tehran, Iran
In the past few years, e-learning solutions are gradually replacing the traditional learning environments. The short attention span and lack of focus in many students is one of the factors which requires attention of e-learning course designers. Visually enhanced and dynamic e-learning courses proved to be more effective in keeping the attention of the students. In this paper, a framework for designing visually enhanced e-learning environments using deep cross-medium matching is proposed. The proposed framework uses deep neural networks for matching the textual and visual information together in order to suggest dynamic visual content for the textual e-learning materials. The proposed framework can improve the learning experience of students by providing dynamic visually enhanced e-learning environment.
کلمات کلیدی: deep neural networks; e-learning; test mining; personalization
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/867249/