Abstract
Due to the opportunities provided by the Internet, people are taking advantage of e-learning courses and enormous research efforts have been dedicated to the development of e-learning systems. So far, many e-learning systems are proposed and used practically. However, in these systems the e-learning completion rate is low. One of the reasons is the low study desire and motivation. In this work, we present an IoT-Based e-learning testbed. We carried out some experiments considering meditation parameter with a student of our laboratory. We used Mind Wave Mobile (MWM) to get the data and considered four situations: Playing Game, Watching Movie, Listening Music and Reading Book. The evaluation results show that our testbed can judge the student situation by meditation parameter.
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References
Matsuo, K., Barolli, L., Xhafa, F., Kolici, V., Koyama, A., Durresi, A., Miho, R.: Implementation of an e-learning system using P2P, web and sensor technologies. In: Proceedings of IEEE Advanced Information Networking and Applications (AINA-2009), pp. 800–807 (2009)
Matsuo, K., Barolli, L., Arnedo-Moreno, J., Xhafa, F., Koyama, A., Durresi, A.: Experimental results and evaluation of SmartBox stimulation device in a P2P e-learning system. In: Proceedings of Network-Based Information Systems (NBiS-2009), pp. 37–44 (2009)
Domingo, M.G., Forner, J.A.M.: Expanding the learning environment: combining physicality and virtuality - the Internet of Things for eLearning. In: Proceedings of 10th IEEE International Conference on Advanced Learning Technologies (ICALT-2010), pp. 730–731 (2010)
Gasparini, I., Eyharabide, V., Schiaffino, S., Pimenta, M.S., Amandi, A., de Oliveira, J.P.M.: Improving user profiling for a richer personalization: modeling context in e-learning. In: Intelligent and Adaptive Learning Systems: Technology Enhanced Support for Learners and Teachers, pp. 182–197 (2012). Chapter 12
de Freitas, V., Marcal, V.P., Gasparini, I., Amaral, M.A., Proenca Jr., M.L., Brunetto, M.A.C., Pimenta, M.S., Ribeiro, C.H.F.P., de Lima, J.V., de Oliveira, J.P.M.: AdaptWeb: an adaptive web-based courseware. In: Proceedings of International Conference on Information and Communication Technologies in Education (ICTE-2002), pp. 131–134 (2002)
Schiaffino, S., Garcia, P., Amandi, A.: eTeacher: providing personalized assistance to e-learning students. Comput. Educ. 51(4), 1744–1754 (2008)
Zanella, A., Bui, N., Castellani, A., Vangelista, L.: Internet of Things for smart cities. IEEE Internet Things J. 1(1), 22–32 (2014)
Atzori, L., Iera, A., Morabito, G.: The Internet of Things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)
Bellavista, P., Cardone, G., Corradi, A., Foschini, L.: Convergence of MANET and WSN in IoT urban scenarios. IEEE Sensors J. 13(10), 3558–3567 (2013)
Derpanis, K.G.: Mean shift clustering. http://www.cse.yorku.ca/~kosta/CompVis-Notes/mean-shift.pdf. Accessed 14 Sept 2016
Comaniciu, D.: Variable bandwidth density-based fusion. In: Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR-2003), vol. 1, pp. 59–66 (2003)
Tuzel, O., Porikli, F., Meer, P.: Kernel methods for weakly supervised mean shift clustering. In: Proceedings of 12th IEEE International Conference on Computer Vision, pp. 48–55 (2009)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)
Raspberry Pi Foundation. http://www.raspberrypi.org/
Oda, T., Barolli, A., Sakamoto, S., Barolli, L., Ikeda, M., Uchida, K.: Implementation and experimental results of a WMN testbed in indoor environment considering LoS scenario. In: Proceedings of 29th IEEE International Conference on Advanced Information Networking and Applications (AINA-2015), pp. 37–42 (2015)
NeuroSky to Release MindWave Mobile. http://mindwavemobile.neurosky.com
Knyazev, G., et al.: EEG delta oscillations as a correlate of basic homeostatic and motivational processes. Neurosci. Biobehav. Rev. 36(1), 677–695 (2012). https://doi.org/10.1016/j.neubiorev.2011.10.002
Klimesch, W., et al.: EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res. Rev. 29(2–3), 169–195 (1999)
Teplan, M., et al.: Fundamentals of EGG measurement. Measur. Sci. Rev. 2(2), 1–11 (2002)
Vialatte, F.B., Bakardjian, H., Prasad, R., Cichocki, A.: EEG paroxysmal gamma waves during Bhramari Pranayama: a yoga breathing technique. Conscious. Cognit. 18(4), 977–988 (2009). https://doi.org/10.1016/j.concog.2008.01.004
Akin, M.: Comparison of Wavelet Transform and FFT methods in the analysis of EEG signals. J. Med. Syst. 26(3), 241–247 (2002)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(10), 2825–2830 (2011)
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Yamada, M., Bylykbashi, K., Liu, Y., Matsuo, K., Barolli, L., Kolici, V. (2018). Performance Evaluation of an IoT-Based E-learning Testbed Considering Meditation Parameter. In: Barolli, L., Xhafa, F., Javaid, N., Spaho, E., Kolici, V. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-75928-9_97
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