Title: Enhance e-learning system performance with a cloud and crowd-oriented approach

Authors: Chun-Hsiung Tseng; Ching-Lien Huang; Yung-Hui Chen; Chu-Chun Chuang; Han-Ci Syu; Yan-Ru Jiang; Fang-Chi Tsai; Pin-Yu Su; Jun-Yan Chen

Addresses: Department of Communications Engineering, Yuan Ze University, Taoyuan City, Taiwan ' Department of Industrial Management, LungHwa University of Science and Technology, Taoyuan 33306, Taiwan ' Department of Computer Information and Network of LungHwa University of Science and Technology, Guishan District, Taoyuan City, 33306, Taiwan ' Department of Information Management, Nanhua University, Taiwan ' Department of Information Management, Nanhua University, Taiwan ' Department of Information Management, Nanhua University, Taiwan ' Department of Information Management, Nanhua University, Taiwan ' Department of Information Management, Nanhua University, Taiwan ' Department of Information Management, Nanhua University, Taiwan

Abstract: There are several e-learning systems focusing on the flipped classroom concept. The basic concept of flipped classroom is to have students learn by themselves. Once the background learning stage is performed outside of the class time, tutors have free time to lead students to participate in higher-order thinking. However, as shown in the report of Ash (2012), the performance of the flipped classroom method is arguable. Our survey shows that the contents offered by most modern e-learning systems are relatively static. Considering how fast new information appears on the web, creating content requires effort. The work load of our teachers is already heavy, so expecting teachers to update contents very frequently is not practical. Besides, crowd intelligence is usually considered helpful in enhancing learning performance. In this research, a system to utilise both cloud materials and crowd intelligence is proposed and an experiment to validate the system is included.

Keywords: e-learning; crowd-sourcing.

DOI: 10.1504/IJHPCN.2018.093844

International Journal of High Performance Computing and Networking, 2018 Vol.12 No.1, pp.84 - 93

Received: 11 Feb 2016
Accepted: 10 Jul 2016
Published online: 07 Aug 2018 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article