Visually Enhanced E-learning Environments Using Deep Cross-Medium Matching
Mozhdeh Dokhani, Babak Majidi, Ali Movaghar · 2019
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.