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IoTFLiP: IoT-based flipped learning platform for medical education


Ali, M and Bilal, HSM and Razzaq, MA and Khan, J and Lee, S and Idris, M and Aazam, M and Choi, T and Han, SC and Kang, BH, IoTFLiP: IoT-based flipped learning platform for medical education, Digital Communications and Networks, 3, (3) pp. 188-194. ISSN 2352-8648 (2017) [Refereed Article]


Copyright Statement

Copyright 2017 Chongqing University of Posts and Telecommunications. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

DOI: doi:10.1016/j.dcan.2017.03.002


Case-Based Learning (CBL) has become an effective pedagogy for student-centered learning in medical education, which is founded on persistent patient cases. Flippped learning and Internet of Things (IoTs) concepts have gained significant attention in recent years. Using these concepts in conjunction with CBL can improve learning ability by providing real evolutionary medical cases. It also enables students to build confidence in their decision making, and efficiently enhances teamwork in the learing environment. We propose an IoT-based Flip Learning Platform, called IoTFLiP, where an IoT infrastructure is exploited to support flipped case-based learning in a cloud environment with state of the art security and privacy measures for personalized medical data. It also provides support for application delivery in private, public, and hybrid approaches. The proposed platform is an extension of our Interactive Case-Based Flipped Learning Tool (ICBFLT), which has been developed based on current CBL practices. ICBFLT formulates summaries of CBL cases through synergy between students' and medical expert knowledge. The low cost and reduced size of sensor device, support of IoTs, and recent flipped learning advancements can enhance medical students' academic and practical experiences. In order to demonstrate a working scenario for the proposed IoTFLiP platform, real-time data from IoTs gadgets is collected to generate a real-world case for a medical student using ICBFLT.

Item Details

Item Type:Refereed Article
Keywords:internet of things, cloud environment, flipped learning, case-based learning, medical education
Research Division:Information and Computing Sciences
Research Group:Distributed computing and systems software
Research Field:Mobile computing
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Ali, M ( Maqbool Ali)
UTAS Author:Han, SC (Ms Caren Han)
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:117870
Year Published:2017
Web of Science® Times Cited:24
Deposited By:Engineering
Deposited On:2017-06-28
Last Modified:2018-06-15
Downloads:162 View Download Statistics

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