An IoT-Enabled Machine Learning Framework for Automated Teacher Performance Feedback to Enhance Teaching Quality

Hardi Abbas; Safieh Siadat; Amir Masoud Rahmani

doi:10.24017/science.2025.2.17

Authors

Hardi Abbas Information Technology Department, Kalar Technical Institute, Garmian Polytechnic University, Kurdistan, Iraq. https://orcid.org/0009-0001-5314-613X
Safieh Siadat Computer Engineering Department, Payam Nour University, Tehran, Iran. https://orcid.org/0000-0001-9655-9850
Amir Masoud Rahmani Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin, Taiwan https://orcid.org/0000-0001-8641-6119

Abstract

Given the crucial role of teachers in the education system, robust mechanisms are necessary to enhance their teaching effectiveness. Through leveraging advanced technological methods, both teacher and student evaluation processes can be conducted with high accuracy. This study proposes an IoT-based automated teacher performance evaluation system that utilizes machine learning algorithms and computer vision techniques to provide immediate feedback on teaching performance to supervisors. The system analyzes key elements such as hand movements and the teacher's position in the classroom. By enhancing teaching performance, the model aims to improve student learning outcomes. In addition, to develop and test the system, a hypothetical dataset - called the teacher dataset - was created for this proposed model by collecting 35 publicly available videos from YouTube. This approach employs a ResNet50 pre-trained neural network for transfer learning and feature extraction to classify teacher behavior into 8 classes. Fuzzy logic converts the predictions into three teaching quality ratings (poor/medium/good). Using this custom dataset, the model achieved an accuracy of 84.8%, indicating strong performance. This approach enables automated feedback on teaching style, reducing the need for in-person evaluations by educational supervisors. The proposed system has the potential to significantly enhance the overall quality of teaching and learning.

Keywords:

Internet of Things (IoT), Transfer Learning, ResNet50, Smart Classroom, Teacher Body language

References

B. Kumar, V. Sharma, T. K. Vashishth, R. Panwar, K. K. Sharma, and S. Chaudhary, “Exploring the transformative power of internet of things (IoT) technologies in the age of industry 4.0: unleashing the potential in the digital economy,” in Ad-vanced IoT Technologies and Applications in the Industry 4.0 Digital Economy, Boca Raton, 2024, pp. 42–59, doi: 10.1201/9781003434269-3. DOI: https://doi.org/10.1201/9781003434269-3

J. S. Yalli, M. H. Hasan, and A. Badawi, “Internet of things (IoT): Origin, embedded technologies, smart applications, and its growth in the last decade,” IEEE Access, vol. 12, no. June 2024, pp. 1–26, 2024, doi: 10.1109/ACCESS.2024.3418995. DOI: https://doi.org/10.1109/ACCESS.2024.3418995

A. Magalhães, J. Matias and A. António, “The internet of things in education. technology, Pedagogy and learning", Revista Portuguesa de Investigação Educacional, vol. 1, no. 27, pp. 1–25, 2024, doi: 10.34632/investigacaoeducacional.2024.16057.

S. Yue, “The evolution of pedagogical theory: from traditional to modern approaches and their impact on student engage-ment and success,” Journal of Education and Educational Research, vol. 7, no. 3, pp. 226–230, 2024, doi: 10.54097/j4agx439. DOI: https://doi.org/10.54097/j4agx439

T. Kucuk, “The power of body language in education: A study of teachers’ perceptions,” International Journal of Social Sciences and Educational Studies , vol. 10, no. 3, pp. 275-289, 2023, doi: 10.23918/ijsses.v10i3p275. DOI: https://doi.org/10.23918/ijsses.v10i3p275

U. Karim, “Implementation of group work in the classroom,” LINGUA Jurnal Bahasa Sastra dan Pengajarannya , vol. 12, no. 1, pp. 97–106, 2015, doi: 10.30957/lingua.v12i1.74. DOI: https://doi.org/10.30957/lingua.v12i1.74

H. Sharif and A. Atif, “ The evolving classroom : How learning analytics is shaping the future of education and feedback mechanisms”, Education Sciences, vol. 14, no. 2, p. 176, 2024, doi: 10.3390/educsci14020176. DOI: https://doi.org/10.3390/educsci14020176

M. W. Alibali et al., “Students learn more when their teacher has learned to gesture effectively,” Gesture, vol. 13, no. 2, pp. 210–233, 2013, doi: 10.1075/gest.13.2.05ali. DOI: https://doi.org/10.1075/gest.13.2.05ali

I. A. Ghashim and M. Arshad, “Internet of things (IoT)-Based teaching and learning: Modern trends and open challenges,” sustainability, vol. 15, no. 21, pp. 1–21, 2023, doi: 10.3390/su152115656. DOI: https://doi.org/10.3390/su152115656

E. I. Obilor, “Influence of teaching effectiveness on students ’ learning outcome,” International Journal of Innovative So-cial & Science Education Research, vol. 7, no. 2, pp. 37–48, 2019, [Online]. Available: https://seahipaj.org/journals-ci/june-2019/IJISSER/full/IJISSER-J-4-2019.pdf.

A. M. Nejeru, M. S. Omar, S. Yi, S. Paracha, and M. Wannous, “Using IoT technology to improve online education through data mining,” 2017 International Conference on Applied System Innovation (ICASI) , pp. 515-518, Sapporo, Japan, 2017, doi: 10.1109/ICASI.2017.7988469. DOI: https://doi.org/10.1109/ICASI.2017.7988469

S. A. Narayanan, M. Prasanth, P. Mohan, M. R. Kaimal, and K. Bijlani, “Attention analysis in e-learning environment using a simple web camera,” 2012 IEEE International Conference on Technology Enhanced Education (ICTEE), pp. 1-4, Am-ritapuri, India, 2012, doi: 10.1109/ICTEE.2012.6208618. DOI: https://doi.org/10.1109/ICTEE.2012.6208618

K. Matsuo, M. Yamada, K. Bylykbashi, M. Cuka, Y. Liu, and L. Barolli, “Implementation of an IoT-Based E-learning testbed : Performance evaluation using Mean-Shift clustering approach considering four types of brain waves,” 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 203–209, Kra-kow, Poland, 2018, doi: 10.1109/WAINA.2018.00088. DOI: https://doi.org/10.1109/WAINA.2018.00088

K-Y, Tan, K-W Ng, and K. Ramasamy, “Classroom environment analysis via internet of things,” Journal of Informatics and Web Engineering , vol. 3, no. 2, pp. 19–36, 2024, doi: 10.33093/jiwe.2024.3.2.2. DOI: https://doi.org/10.33093/jiwe.2024.3.2.2

G. Zhao, Y. Zhang, and J. Chu, “Internet of things a multimodal teacher speech emotion recognition method in the smart classroom,” Internet of Things, vol. 25, no. January, pp. 101069, 2024, doi: 10.1016/j.iot.2024.101069. DOI: https://doi.org/10.1016/j.iot.2024.101069

A. Saxena, A. L. Styles, and V. A. K. Theory, “Automated enhanced learning system using IOT,” 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), pp. 1–5, Ghaziabad, India, 2019, doi: 10.1109/IoTSIU.2019.8777711. DOI: https://doi.org/10.1109/IoT-SIU.2019.8777711

S. Satu, S. Roy, and F. Akhter, “IoLT : An IoT based collaborative blended learning platform in higher education,” 2018 International Conference on Innovation in Engineering and Technology (ICIET), pp. 1–6, Dhaka, Bangladesh, 2018, doi: 10.1109/CIET.2018.8660931. DOI: https://doi.org/10.1109/CIET.2018.8660931

Shaurya, S. Som, and A. Rana, “IoT based educational model for better teaching- learning environment,” 2020 8th Interna-tional Conference on Reliability, Infocom Technologies and Optimization, pp. 824–828, Noida, India, 2020, doi: 10.1109/ICRITO48877.2020.9197852. DOI: https://doi.org/10.1109/ICRITO48877.2020.9197852

A. Khaleel and S. Yussof, “An investigation on the viability of using IoT for student safety and attendance monitoring in Iraqi primary schools,” Journal of Theoretical and Applied Information Technology, vol. 85, no. 3, pp. 394–402, 2016. Available: https://www.jatit.org/volumes/Vol85No3/14Vol85No3.pdf

J. Hu, “Teaching evaluation system by use of machine learning and artificial intelligence methods,” International Journal of Emerging Technologies in Learning, vol. 16, no. 5, pp. 87–101, 2021, doi: 10.3991/ijet.v16i05.20299. DOI: https://doi.org/10.3991/ijet.v16i05.20299

N. Dehbozorgi, “Exploring the influence of emotional states in peer interactions on students ’ academic performance,” IEEE Transactions on Education, vol. 67 , no. 3, pp. 405 - 412, 2024, doi: 10.1109/TE.2023.3335171. DOI: https://doi.org/10.1109/TE.2023.3335171

E. Guan, Z. Zhang, M. He, and X. Zhao, “Evaluation of classroom teaching quality based on video processing technology,” Chinese Automation Congress (CAC), pp. 1926–1929, Jinan, China, 2017, doi: 10.1109/CAC.2017.8243084. DOI: https://doi.org/10.1109/CAC.2017.8243084

W. X. Huang, X. Gao, N. Wang, Y. C. Yang, and Y. Yan, “Teaching quality assessment system based on support vector machine technology,” International Journal of Emerging Technologies in Learning , vol. 11, no. 11, pp. 4–8, 2016, doi: 10.3991/ijet.v11i11.6251. DOI: https://doi.org/10.3991/ijet.v11i11.6251

M. Ali, “Developing applications for voice enabled IoT devices to improve classroom activities,” 2018 21st International Conference of Computer and Information Technology (ICCIT), pp. 1–4, Dhaka, Bangladesh, 2018, doi: 10.1109/ICCITECHN.2018.8631906. DOI: https://doi.org/10.1109/ICCITECHN.2018.8631906

M. Ciolacu, A. F. Tehrani, L. Binder, and P. M. Svasta, “Education 4 . 0 - artificial intelligence assisted higher education : early recognition system with machine learning to support students ’ success,” 2018 IEEE 24th International Symposium for Design and Technology in Electronic Packaging (SIITME), pp. 23–30, Iasi, Romania, 2018, doi: 10.1109/SIITME.2018.8599203. DOI: https://doi.org/10.1109/SIITME.2018.8599203

N. Verma, S. Getenet, C. Dann, and T. Shaik, “Computers and education: Artificial intelligence designing an artificial intelli-gence tool to understand student engagement based on teacher’s behaviours and movements in video conferencing,” Com-puters and Education: Artificial Intelligence, vol. 5, no. June, p. 100187, 2023, doi: 10.1016/j.caeai.2023.100187. DOI: https://doi.org/10.1016/j.caeai.2023.100187

K. D. Zahra, “New approach to the design of decision support system to improve e-learning environments,” 4th Internation-al Conference on e-Learning and e-Teaching (ICELET 2013), pp. 26–29, Shiraz, Iran, 2013, doi: 10.1109/ICELET.2013.6681640. DOI: https://doi.org/10.1109/ICELET.2013.6681640

J. Yi, T. H. Clausen, W. M. Townsley, and C. Systems, “A study of LoRa: Long range & low power networks for the internet of things,” Sensors, vol. 16, no. 9, pp. 1466, 2016, doi: 10.3390/s16091466. DOI: https://doi.org/10.3390/s16091466

I. O. Muraina, “Ideal dataset splitting ratios in machine learning algorithms: General concerns for data scientists and data analysts,” 7th International Mardin Artuklu Scientific Researches Conference, no. February, pp. 496–504, Mardin, Turkey, 2022. Available: https://www.uniselinus.education/sites/default/files/2022-04/ismail%20muraina.pdf.

M. Browne and S. S. Ghidary, “Convolutional neural networks for image processing: An application in robot vision,” 16th Australian Conference on Artificial Intelligence, vol. 2903, no. December 2003, pp. 641–652, Perth, Australia, 2003, doi: 10.1007/978-3-540-24581-0_55. DOI: https://doi.org/10.1007/978-3-540-24581-0_55

G. Xu, X. Wang, X. Wu, X. Leng, and Y. Xu, “Development of residual learning in deep neural networks for computer vision: A survey,” Engineering Applications of Artificial Intelligence, vol. 142, no. 8, p. 109890, 2025, doi: 10.48550/arXiv.2405.01725. DOI: https://doi.org/10.1016/j.engappai.2024.109890

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, Las Vegas, NV, USA, 2016, doi: 10.1109/CVPR.2016.90. DOI: https://doi.org/10.1109/CVPR.2016.90

H. Mujtaba, “Introduction to resnet or residual network,” pp. 1–8, 2020. Available: https://batistalab.com/classes/CHEM584/Resnet.pdf.

D. Sarwinda, R. Hilya, A. Bustamam, and P. Anggia, “ Deep learning in image classification using residual network ( ResNet ) variants for detection of colorectal cancer,” Procedia Computer Science, vol. 179, no. 2019, pp. 423–431, 2021, doi: 10.1016/j.procs.2021.01.025. DOI: https://doi.org/10.1016/j.procs.2021.01.025

H. Lee and J. Song, “Introduction to convolutional neural network using Keras; An understanding from a statistician,” Communications for Statistical Applications and Methods, vol. 26, no. 6, pp. 591–610, 2019, doi: 10.29220/CSAM.2019.26.6.591. DOI: https://doi.org/10.29220/CSAM.2019.26.6.591

R. Saatchi, “Fuzzy logic concepts, developments and implementation,” Information, vol. 15, no. 10, p. 656, 2024, doi: 10.3390/info15100656. DOI: https://doi.org/10.3390/info15100656

A. Supani et al. "Enhancing deeper layers with residual network on CNN architecture: A review", Atlantis Press Internation-al BV, vol. 2., no. 13, 2023, doi: 10.2991/978-94-6463-118-0_46. DOI: https://doi.org/10.2991/978-94-6463-118-0_46

A. James et al., “Inferring the climate in classrooms from audio and video recordings: A machine learning approach,” 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), pp. 983-988, Wollongong, NSW, Australia, 2018, doi: 10.1109/TALE.2018.8615327. DOI: https://doi.org/10.1109/TALE.2018.8615327

A. Sanmorino and H. Di Kesuma, “Fine-tuning a pre-trained ResNet50 model to detect distributed denial of service attack,” Bulletin of Electrical Engineering and Informatics, vol. 13, no. 2, pp. 1362–1370, 2024, doi: 10.11591/eei.v13i2.7014. DOI: https://doi.org/10.11591/eei.v13i2.7014

M. Altalhan, A. Algarni, and M. Turki-Hadj Alouane, “Imbalanced data problem in machine learning: A review,” IEEE Ac-cess, vol. 13, pp. 13686–13699, 2025, doi: 10.1109/ACCESS.2025.3531662. DOI: https://doi.org/10.1109/ACCESS.2025.3531662