International Journal of Computer Science Trends and Technology (IJCST) – Volume 6 Issue 2, Mar - Apr 2018 ISSN: 2347-8578 www.ijcstjournal.org Page 154 A Review of IOT-Based Flipped Learning Platform for Medical Education Sharda Tiwari [1] , Dr Manuj Darbari [2] Department of Computer science Babu Banarasi Das University Lucknow - India ABSTRACT A survey of Case-Based Learning (CBL) has turned into a viable instructional method for understudy focused learning in therapeutic training, which is established on constant patient cases. Flippped learning and Internet of Things (IoTs) ideas have increased critical consideration lately. Utilizing these ideas in conjunction with CBL can enhance learning capacity by giving genuine developmental medicinal cases. It additionally empowers understudies to assemble trust in their basic leadership, and effectively improves collaboration in the learning condition. We propose an IoT-based Flip Learning Platform, called, where an IoT foundation is abused to support flipped case-based learning in a cloud domain with cutting edge security and protection measures for customized restorative information. It likewise offers help for application conveyance in private, open, and crossover approaches. The proposed stage is an expansion of our Interactive Case-Based Flipped Learning Tool (ICBFLT), which has been produced in view of current CBL rehearses. ICBFLT details outlines of CBL cases through cooperative energy amongst understudies' and medicinal master information. The ease and decreased size of sensor gadget, support of IoTs, and late flipped learning headways can upgrade restorative understudies' scholarly and down to earth encounters. With a specific end goal to show a working situation for the proposed stage, constant information from IoTs contraptions is gathered to create a genuine case for a therapeutic understudy utilizing ICBFLT. Keywords:- Medical Education, Flippped Learning, Teamwork, Educational. I. INTRODUCTION Different showing techniques have been connected in therapeutic training. Among them, Case-Based Learning (CBL) is viewed as a successful learning strategy for restorative understudies [1,2]. It is a mutual learning approach for little gatherings of understudies to distinguish and tackle the patients' concern [3]. In CBL, real cases are utilized for clinical practice [4] and a facilitator's part is more dynamic [5] contrasted with conventional learning strategies. Furthermore, CBL encourages understudies to research reality based information and gives a chance to watch hypothesis by and by [6]. Nonetheless, in CBL, formal learning exercises are performed straightforwardly, and understudies have a tendency to delay to effectively take an interest because of the absence of past experience, elucidation of issues, and information. Late patterns demonstrate that expanding consideration is being paid to web based learning situations [3] and flipped learning approaches for boosting learning capacities [7,8]. As of now, CBL is ordinarily performed without misusing the upsides of the flipped learning approach, which has noteworthy confirmation supporting it over customary learning techniques [8,9]. As characterized by Kopp [10], "Flipped learning is a procedure in which a teacher conveys online directions to understudies earlier and outside the class and aides them intelligently to elucidate issues. While in class, the educator bestows learning in a proficient way". Concerning with flipped learning ideas, we have outlined and built up an Interactive Case-Based Flipped Learning Tool (ICBFLT) for restorative training [11] to empower therapeutic understudies to pick up CBL encounter, ICBFLT was planned and created in light of current CBL rehearses at the School of Medicine in the University of Tasmania, Australia. Keeping in mind the end goal to help social insurance changes, huge work has been performed to procure data through IoT gadgets. However there is as yet an absence of frameworks and structures for effectively misusing IoT information and utilizing it for the reasons for learning extraction, producing information with fractional contribution of field specialists, and utilizing the procured learning to give ongoing RESEARCH ARTICLE OPEN ACCESS