IoTFLiP: IoT-based flipped learning platform for medical education.

Maqbool Ali, Hafiz Syed Muhammad Bilal, Muhammad Asif Razzaq, Jawad Khan, Sungyoung Lee, Muhammad Idris, Mohammad Aazam, Taebong Choi, Soyeon Caren Han, Byeong Ho Kang

Research output: Contribution to journalArticlepeer-review

52 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)188-194
Number of pages7
JournalDigital Communications and Networks
Volume3
Issue number3
DOIs
Publication statusPublished - Aug 2017
Externally publishedYes

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