Nayyar Ahmed Khan,Rund Fareed Mahafdah,Omaia Mohammad Al-Omari,Samia Dardouri,Ahmed MasihUddinSiddiqi,Mohammad Ahmad Mohammad Nasimuddin,



IoT,e-learning,computational learning,System Adaption,Security,privacy,challenges,smart devices,sensors-based devices,


Internet of Things (IoT) is an emerging trend in the field of technology, which has derived a lot of attention in the recent years. The ability of this technology for reducing the burden and strain on the education or academic system makes it possible for deriving a potential and raising the standards of academics. This study proposes a standard model for the educational system with the help of IoT. This paper gives an IoT based modal for the student engagement till the industry institute linkage plan. It gives a design in which the monitoring of RFID based data can be done and results could be discovered using the IoT techniques for the further selection criteria of industries. The results for any student shall be updated and made available based on the student data and business intelligence can be applied to the university system for giving the industry for best students. The study tries to relate various components which are later for the model generation, including the strength, weaknesses, opportunities and threats for a wearable IoT university system. A lot of challenges are based by the field of academics and University’s as far as security and privacy is concerned. Future direction in the research can be derived from the existing proposed model in the study.


I. Ansari, A.N., et al. Automation of attendance system using RFID, biometrics, GSM Modem with. Net framework. in Multimedia Technology (ICMT), 2011 International Conference on. 2011. IEEE.
II. Baradwaj, B.K. and S. Pal, Mining educational data to analyze students’ performance. arXiv preprint arXiv:1201.3417, 2012.
III. Bevitt, D., C. Baldwin, and J. Calvert, Intervening early: Attendance and performance monitoring as a trigger for first year support in the biosciences. Bioscience Education, 2010. 15(1): p. 1-14.
IV. Bsoul, Q., & Salim, Z. 2016. Effect Verb Extraction on Crime Traditional Cluster, world applied science journal.
V. Cambria, E., & White, b. 2014. Jumping NLP Curves: A Review of Natural Language Processing Research. IEEE Computational Intelligence Magazine, 9(1): 48-57.
VI. Chawathe, S.S., et al. Managing RFID data. in Proceedings of the Thirtieth international conference on Very large data bases-Volume 30. 2004. VLDB Endowment.
VII. Cleveland, B.W., Engaging spaces: Innovative learning environments, pedagogies and student engagement in the middle years of school. 2011: University of Melbourne, Faculty of Architecture, Building and Planning.
VIII. Darcy, P., B. Stantic, and A. Sattar. Applying a neural network to recover missed RFID readings. in Proceedings of the Thirty-Third Australasian Conferenc on Computer Science-Volume 102. 2010. Australian Computer Society, Inc.
IX. Darcy, P., S. Tucker, and B. Stantic, Integrating RFID technology with intelligent classifiers for meaningful prediction knowledge. Advances in Internet of Things, 2013. 3(2): p. 27-33.
X. Ding, X. & Tang, Y. 2013. Improved Mutual Information Method For Text Feature Selection. The 8th International Conference on Computer Science & Education. IEEE, pp: 163-166.
XI. Doyle, L., et al., An evaluation of an attendance monitoring system for undergraduate nursing students. Nurse education in practice, 2008. 8(2): p. 129-139.
XII. Dyer, M. 1995. Connectionist natural language processing: a status report. in Computational Architectures Integrating Neural and Symbolic Processes, Sun and L. Bookman, Eds. Dordrecht. The Netherlands: Kluwer Academic, 292(1):389–429.
XIII. Ferreira, D.D.J.S.S.F.B.V., Knowledge and technology transfer between university — Industry — Society: A new crowdsourcing framework for Internet of Things, in Microwaves, Antennas, Communications and Electronic Systems (COMCAS), 2017 IEEE International Conference, IEEE, Editor. 2017, IEEE Explore: Tel-Aviv, Israel.
XIV. Fodeh, S., Punch, W. & Tan, P. 2011. On ontology-driven document clustering using core semantic features. On ontology-driven document clustering using core semantic features, Journal of KnowlInfSyst, Springer-Verlag London. 28(2): 395-421.
XV. Gershenfeld, N., R. Krikorian, and D. Cohen, The internet of things. Scientific American, 2004. 291(4): p. 76-81.
XVI. Halibas, A.S., I.G. Pillai, and A.C. Matthew. Utilization of RFID analytics in assessing student engagement. in Applications of Information Technology in Developing Renewable Energy Processes & Systems (IT-DREPS), 2017 2nd International Conference on the. 2017. IEEE.
XVII. Hanna, M., Data mining in the e-learning domain. Campus-wide information systems, 2004. 21(1): p. 29-34.
XVIII. Hotho, A., Staab, S., &Stumme, G. 2003. WordNet improves text document clustering. In Proc. of the SIGIR 2003 Semantic Web Workshop, pp: 541-544.
XIX. Hughes, G. and C. Dobbins, The utilization of data analysis techniques in predicting student performance in massive open online courses (MOOCs). Research and Practice in Technology Enhanced Learning, 2015. 10(1): p. 10.
XX. Jeffery, S.R., M. Garofalakis, and M.J. Franklin. Adaptive cleaning for RFID data streams. in Proceedings of the 32nd international conference on Very large data bases. 2006. VLDB Endowment.
XXI. Jindal, N. and B. Liu. Mining comparative sentences and relations. in AAAI. 2006.
XXII. Jing, B.-Z., et al. RFID access authorization by face recognition. in Machine Learning and Cybernetics, 2009 International Conference on. 2009. IEEE.
XXIII. Jones, K., J. Thomson, and K. Arnold, Questions of data ownership on campus. 2014.
XXIV. Kassim, M., et al. Web-based student attendance system using RFID technology. in Control and System Graduate Research Colloquium (ICSGRC), 2012 IEEE. 2012. IEEE.
XXV. Kummer, O., Savoy, J., & Argand, E. 2012. Feature selection in sentiment analysis.
XXVI. Lewis, D. 1997. Reuters-21578 text categorization test collection. AT&T Labs Research.Matthew, A.S.H.I.G.P.A.C., Utilization of RFID analytics in assessing student engagement, in Applications of Information Technology in Developing Renewable Energy Processes & Systems (IT-DREPS), 2017 2nd International Conference, IEEE, Editor. 2017, IEEE: Amman, Jordan.
XXVII. Li, D.-Y., et al. Design of Internet of Things System for Library Materials Management using UHF RFID. in RFID Technology and Applications (RFID-TA), 2016 IEEE International Conference on. 2016. IEEE.
XXVIII. Lim, T., S. Sim, and M. Mansor. RFID based attendance system. in Industrial Electronics & Applications, 2009. ISIEA 2009. IEEE Symposium on. 2009. IEEE.
XXIX. Liu, Xin&Beyrend-Dur, Delphine&Dur, Gael & Ban, Syuhei. (2014).
XXX. Mihăescu, C., et al. Learning analytics solution for building personalized quiz sessions. in Carpathian Control Conference (ICCC), 2017 18th International. 2017. IEEE.
XXXI. Porter, F. 1997. An algorithm for suffix stripping in K. Sparck Jones, P. Willett (1st Eds) Readings in Information Retrieval, Morgan Kaufmann Multimedia Information and Systems Series, pp: 313–316.
XXXII. Rogati, Monica & Yang, Yiming. 2002. High-performing feature selection for text classification. 659. 10.1145/584902.584911.
XXXIII. Romero, C. and S. Ventura, Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2013. 3(1): p. 12-27.
XXXIV. Romero, C., et al., Web usage mining for predicting final marks of students that use Moodle courses. Computer Applications in Engineering Education, 2013. 21(1): p. 135-146.
XXXV. Srinidhi, M. and R. Roy. A web enabled secured system for attendance monitoring and real time location tracking using Biometric and Radio Frequency Identification (RFID) technology. in Computer Communication and Informatics (ICCCI), 2015 International Conference on. 2015. IEEE.
XXXVI. Teague, D.M. and M.W. Corney, Is anybody there? Bootstrapping attendance with engagement. 2011.
XXXVII. Welbourne, E., et al. Challenges for pervasive RFID-based infrastructures. in Pervasive Computing and Communications Workshops, 2007. PerCom Workshops’ 07. Fifth Annual IEEE International Conference on. 2007. IEEE.
XXXVIII. Wu, D.-L., et al. Access control by RFID and face recognition based on neural network. in Machine Learning and Cybernetics (ICMLC), 2010 International Conference on. 2010. IEEE.
XXXIX. Yao, H., Liu, C., Zhang, P., & Wang, L. 2017. A feature selection method based on synonym merging in text classification system. Journal on Wireless Communications and Networking. Springer. pp: 1-8.
XL. Zhou, Q., et al. Design and Implementation of Learning Analytics System for Teachers and Learners Based on the Specified LMS. in Educational Innovation through Technology (EITT), 2014 International Conference of. 2014. IEEE

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