Volume 15, Issue 3 (9-2023)
itrc 2023, 15(3): 43-52 |
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1- Department Engineering Payame Noor University (PNU) Tehran, Iran
2- Faculty Member Department Engineering Payame Noor University (PNU) Tehran, Iran amir.tajfar@pnu.ac.ir , am.tajfar@itrc.ac.ir
2- Faculty Member Department Engineering Payame Noor University (PNU) Tehran, Iran amir.tajfar@pnu.ac.ir , am.tajfar@itrc.ac.ir
Abstract: (900 Views)
The Internet of Things (IoT) is one of the new technologies that has received significant attention in the last decade and has been used in all aspects of life (including agriculture, medicine, business, industry, education, etc.).
One of the most important applications of the Internet of Things is in the process of student education, which has been discussed a lot in recent years and has led to a significant progress in the education industry, however, there are still many challenges in this field, which includes managing classrooms, conference halls, teaching in offices, public schools, and e-learning websites. Therefore, it is necessary to create a new framework to improve teaching methods. On the other hand, providing educational materials for students according to their level of understanding and learning goals can have a significant effect on improving the quality of teaching and learning.
In this research, a framework for improving the quality of teaching to students in the context of the Internet of Things has been presented. In this framework, the mental and psychological condition and stress conditions of the students are investigated and provided to the teacher in real-time, so that they can make the right decision with sufficient information based on the conditions of each student in the classroom and use the information to adapt their teaching methods and tests. This framework, with the help of the Internet of Things, provides information about each student and mental and psychological elements (such as heart rate, body temperature, etc.) as well as factors affecting the classroom environment (including the amount of noise pollution, ambient temperature, light, etc.), collects and uses fuzzy logic to place students in different categories with and without stress conditions. Kuja simulator and MATLAB software are used for simulation. The results of the simulation show that this framework can detect the students' stress and by adapting the test and teaching conditions to the mental and psychological state of the students, it can indirectly improve the educational quality for students.
One of the most important applications of the Internet of Things is in the process of student education, which has been discussed a lot in recent years and has led to a significant progress in the education industry, however, there are still many challenges in this field, which includes managing classrooms, conference halls, teaching in offices, public schools, and e-learning websites. Therefore, it is necessary to create a new framework to improve teaching methods. On the other hand, providing educational materials for students according to their level of understanding and learning goals can have a significant effect on improving the quality of teaching and learning.
In this research, a framework for improving the quality of teaching to students in the context of the Internet of Things has been presented. In this framework, the mental and psychological condition and stress conditions of the students are investigated and provided to the teacher in real-time, so that they can make the right decision with sufficient information based on the conditions of each student in the classroom and use the information to adapt their teaching methods and tests. This framework, with the help of the Internet of Things, provides information about each student and mental and psychological elements (such as heart rate, body temperature, etc.) as well as factors affecting the classroom environment (including the amount of noise pollution, ambient temperature, light, etc.), collects and uses fuzzy logic to place students in different categories with and without stress conditions. Kuja simulator and MATLAB software are used for simulation. The results of the simulation show that this framework can detect the students' stress and by adapting the test and teaching conditions to the mental and psychological state of the students, it can indirectly improve the educational quality for students.
Keywords: Internet of Things (IoT), e-learning, Stress, psychological elements, environmental influencing factors.rs
Type of Study: Research |
Subject:
Information Technology
References
1. [1] Papakostas, G. A. Strolis, A, K. Panagiotopolulos, F. and Atisidis. (2018) " Social Robot Selection: A Case Study in Education", 26 International Conference on Software, Telecommunications and Computer Networks. pp. 1-4,September. 13-15(2018), Split, Croatia. [DOI:10.23919/SOFTCOM.2018.8555844]
2. [2] Salsabeel, S. Zualkernan A, I. (2019) "IoT for Ubiquitous Learning Applications: Current Trends and Future Prospects", Ubiquitous Computing and Computing Security of IoT, pp. 53-68. [DOI:10.1007/978-3-030-01566-4_3]
3. [3] Barakat, S. (2016). "Education And The Internet Of Everything", International Business Management, pp. 4301-430 .
4. [4] Cheng, Y. Wang, Y. Kinshuk, and Chen, N. (2019), " A framework for designing an immersive language learning environment integrated with educational robots and IoTbased toys", Foundations and Trends in Smart Learning, pp.1-4. [DOI:10.1007/978-981-13-6908-7_1]
5. [5] Guo Y., Zheng L., Chen Y. (2019) "Research on Robot Education Curriculum System and Settings in Internet of Things Major", International Symposium for Intelligent Transportation and Smart City (ITASC) 2019 Proceedings, ITASC 2019, Smart Innovation, Systems and Technologies,Vol. 127, pp. 232-240 . [DOI:10.1007/978-981-13-7542-2_23]
6. [6] Veeramanickam, M.R.M and Mohanapriya, Dr. M.(2016), "IOT enabled Futurus Smart Campus with effective ELearning : i-Campus", GSTF Journal of Engineering Technology, p 81-87.
7. [7] Gligoric, Nenad, Uzelac, Ana, Krco, Srdjan, Kovacevic, Ivana and Nikodijevic, Ana (2015) ,"Smart classroom system for detecting level of interest a lecture creates in a classroom", Journal of Ambient Intelligence and Smart Environments. [DOI:10.3233/AIS-150303]
8. [8] Temkar, Rohini, Gupte, Mohanish and Kalgaonkar, Siddhesh, (2016),"Internet of Things for Smart Classrooms", International Research Journal of Engineering and Technology (IRJET), 3(7), pp. 203-207.
9. [9] J-Long Hung & K. Zhang, (2008), "Revealing Online Learning Behaviors and Activity Patterns and Making Predictions with Data Mining Techniques in Online Teaching ",MERLOT Journal of Online Learning and Teaching, Vol. 4, No. 4, pp. 426- 437.
10. [10] P. Ratnapala & R. G. Ragel, S. Deegalla, (2015), "Students Behavioural Analysis in an Online Learning Environment Using Dataining ", https://pdfs.semanticscholar.org/1b52/59aec53f9d211bfdb aff6c65a0b394e84045.pdfU31T [Accessed on Feburary 2017]
11. [11] A. Hershkovitz & R. Nachmias, (2009), "Learning about Online Learning Processes and Students' Motivation through Web Usage Mining ",Interdisciplinary Journal of E-Learning and Learning Objects, Vol. 05, pp. 197- 214. Volume 15- Number 3 - 2023 (43 -52) 51 [DOI:10.28945/73]
12. [12] Fadeyi, M., Alkhaja, K., Bin Sulayem, M., & Abu-Hijleh, B. (2014). Evaluation of indoor environmental quality conditions in elementary schools' classrooms in the United Arab Emirates. Frontiers of Architectural Research, 3(2), 166-177. [DOI:10.1016/j.foar.2014.03.001]
13. [13] Erol, B. A. Wallace, P. Benavidez, P. and Jamshidi, M. (2018) "Voice Activation and Control to Improve Human Robot Interactions with IoT Perspectives", 2018 World Automation Congress (WAC), pp. 1-5, 2018, Stevenson,USA. [DOI:10.23919/WAC.2018.8430412]
14. [14] https://www.historyofinformation.com/detail.php?entryid= 2495 [Accessed on October 2020]
15. [15] Reecha Sharma, M.S. Patterh, (2014), "SSN 2229-5518 IJSER © 2014 http://www.ijser.org A Systematic Review of PCA and Its Different Form for Face Recognition ",International Journal of Scientific & Engineering Research,Vol. 5, No. 7, pp. 1306-1309.
16. [16] S Mahmood, K Mueller, (2019), "Taxonomizer: Interactive Construction of Fully Labeled Hierarchical Groupings from Attributes of Multivariate Data", IEEE Transactions on Visualization and Computer Graphics. [DOI:10.1109/TVCG.2019.2895642] [PMID]
17. [17] R. Fielding, R. Gettys, J. Mogul, J. Frystyk, H. Masinter, L. Leach, P. and T. Berners-Lee, (1999), "Hypertext transfer protocol-HTTP/1.1 ", Internet Engineering Task Force (IETF). [DOI:10.17487/rfc2616]
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