Big data visualization using multimodal feedback in education. (December 2021)

• Record Type: Journal Article
• Authors: Cui, Yong; Song, Xiao; Hu, Qinglei; Li, Ying; Shanthini, A.; Vadivel, Thanjai
• Is Part Of: Computers & electrical engineering. Volume 96:Part A(2021)
• Publication Date: 2021-12
• Subjects: Big data visualization -- Multimodal feedback -- Online learning -- Student -- Grade Analysis -- Behaviour; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107544
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Currently, Big Data Analytics help educators better analyze students and monitor their progress and likelihood of success. The online learning experience applies to the experiences in the online computer-mediated digital system to collect information. An essential part of education training programs is challenging and effectively implemented between teachers and students in the online learning feedback system. Hence, a Big Data Visualization assisted Multimodal Feedback Framework (BDVMFF) is proposed to boost students' confidence, self-consciousness, and motivation in the online learning platform. The BDVMFF offers the teacher a digital workflow to effectively exchange both the writing and the input to use multimodal feedback effectively. This system provides teachers and students with an effective and straightforward digital learning environment. The simulation results of BDVMFF show the highest performance ratio (97.9%), the efficiency ratio (96.1%), the grade analysis ratio (93.5%), the computation ratio (95.3%), and the lowest response time compared to existing methods.