EXCEL through IoT (Exploring Cognitive and Emotional Learning through IOT)

Deepika Kamboj, Swapnil Sharma, Shivani Sharma

Abstract


Cognitive Learning is a process that involves learner’s knowledge into consideration. It involves the use of human brain. These days understanding students’s emotional state of mind is one of the research area where student face problems in tackling academic tasks. It has been observed that emotions are a crucial part of students' psychosomatic life, and that they may strongly influence academic motivation, cognitive strategies of learning and achieving the desired results.So, our research is to augment student learning and teacher instruction by giving the real-time reaction of students' state of mind, so that teacher can engage students in the learning processes, help them to learn or use the brain in much and far better way to relate thing with the previous one while learning something new.

 

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References


Kavitha P Thomas, A. P. Vinod , “A Study on the Impact of Neurofeedback in EEG Based Attention-driven Game”, 2016 in IEEE International Conference on Systems, Man, and Cybernetics.

Dino Dvorak, Andrea Shang, Samah Abdel-Baki, Wendy Suzuki, André A. Fenton, “Cognitive behavior classification from scalp EEG signals”, DOI 10.1109/TNSRE.2018.2797547, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Luigi Atzori, Antonio Iera, Giacomo Morabito, “The Internet of Things: A survey”, Comput. Netw. (2010), doi:10.1016/ j.comnet.2010.05.010

M. A. Jan et al. (eds.), Recent Trends and Advances in Wireless and IoT-enabled Networks, EAI/Springer Innovations in Communication and Computing.

Ivan Stojmenovic, Sheng Wen, “The Fog Computing Paradigm: Scenarios and Security Issues”, Proceedings of the 2014 Federated Conference on Computer Science and Information Systems pp. 1–8.

Flavio Bonomi, Rodolfo Milito, Jiang Zhu, Sateesh Addepalli, “Fog Computing and Its Role in the Internet of Things”, MCC’12, August 17, 2012.

Mark G. Simkin and William L. Kuechler, “Multiple- Choice Tests and Student Understanding: What Is the Connection?”, Decision Sciences Journal of Innovative Education Volume 3 Number 1 Spring 2005.

N. Applebee and Judith A. Langer, Martin Nystrand and Adam Gamoran, “Discussion-Based Approaches to Developing Understanding: Classroom Instruction and Student Performance in Middle and High School English”, American Educational Research Journal Fall 2003, Vol. 40, No. 3, pp. 685–730.

LISA M. BLANK, “A Metacognitive Learning Cycle: A Better Warranty for Student Understanding?”, in 2000 John Wiley & Sons, Inc.

Narayanan Srinivasan, “Cognitive neuroscience of creativity: EEG based approaches”, in 2006 Elsevier Inc. doi:10.1016/j.ymeth.2006.12.008.

J. Katona, I. Farkas, T. Ujbanyi, P. Dukan, A. Kovari, “Evaluation Of The Neurosky MindFlex EEG Headset Brain Waves Data”, IEEE 12th International Symposium on Applied Machine Intelligence and Informatics • January 23- 25, 2014.

Ronald H. Stevens, Trysha Galloway, and Chris Berka, “EEG-Related Changes in Cognitive Workload, Engagement and Distraction as Students Acquire Problem Solving Skills”, © Springer-Verlag Berlin Heidelberg 2007.

Kavitha P Thomas, A. P. Vinod Senior Member IEEE and Cuntai Guan Senior Member IEEE, “ Enhancement of Attention and Cognitive Skills using EEG based Neurofeedback Game”, 6th Annual International IEEE EMBS Conference on Neural Engineering San Diego, California, 6 - 8 November, 2013.

A.Nancy, Dr. M. Balamurugan and Vijaykumar S, “A Brain Eeg Classification System For The Mild Cognitive Impairment Analysis”, 2017 International Conference on Advanced Computing and Communication Systems (ICACCS -2017), Jan. 06 – 07, 2017, Coimbatore, INDIA.

Amah Rakshit and Rimita Lahiri, “Discriminating Different Color from EEG Signals using Interval-Type 2 Fuzzy Space Classifier (A Neuro-Marketing Study on the Effect of Color to Cognitive State)”, 1st IEEE International Conference on Power Electronics; Intelligent Control and Energy Systems (ICPEICES-2016).

Sadique Ahmad, Kan Li, Yang Li, Humera Qureshi, Siraj Khan, “ Formulation of Congnitive Skills: A Theoretical model based on Psychological and Neurosciences Studies”, 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing.

Balkis Solehah Binti Zainuddin, Zakaria Hussain, Iza Sazanita Isa, “Alpha and Beta EEG Brainwave Signal Classification Technique: A Conceptual Study”, Conference Paper · March 2014 DOI: 10.1109/CSPA.2014.6805755


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