2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 17 , Issue 2 , PP: 488-505, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
M. E. ElAlmi 1 * , A. F. Elgamal 2 , Samar O. AbouElwafa 3
Doi: https://doi.org/10.54216/JISIoT.170231
Student performance during the lecture needs to be closely watched to ensure effective learning takes place. This helps the lecturer monitor the performance of the students in real time. By observing the performance of the students, the lecturer can detect the ones who find performance difficult and assist them accordingly. Besides this, the lecturer can also modify the method of teaching whenever needed. By understanding that their performance can be checked through the system, the student remains motivated to perform even in class. The study will help to develop a system that can be used to monitor the performance of the student during the real time lecture using sound and image processing. The method of developing the system involves the use of two methods: image processing and sound processing. The image processing technique can be used to detect the image of the student, while the sound processing technique will be used to detect the sound of the student during the performance. In the proposed system, Gray Level Co-occurrence Matrix technique has been used along with the Viola-Jones method to detect images along with the weighted Euclidean distance method used in image processing. Additionally, the Mel Frequency Cepstral Coefficients method has been used to detect the relevant sound along with the classification method involving the K-Nearest neighborhood method. The experiment has shown the efficiency of the system developed because the accuracy of image and sound identification of the student was at an average of 89% and 90% respectively. All of this helped to ascertain the efficiency of the system in the development of the research study.
Student performance , Monitoring performance , Performance measurement , Viola-Jones , MFCC , K-NN , Weighted Euclidean Distance , Real time learning , Image processing , Sound processing
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