Predicting Performance of Online Education Students Using IOT and Boosting Algorithmic Framework

Vaibhav Panwar; Vishal Srivastava

doi:10.55524/ijirem.2025.12.2.9

Abstract

There are many problems and algorithms that allow machines or systems to take decisions for trainers of online education industry. These machine models enhance user participation in many ways. Many studies/research have been conducted in online education industry to solve many problems related to student’s orientations and performance using various IoT and AI related algorithm specially boosting algorithm. The main focal area of this paper is to foresee, final performance of online education learners specially university/institute offering online education in India using some IoT and AI techniques specially Boosting Algorithm towards maximum utility of trainers.

Keywords

AI; Algorithm; Data Science, ML, Online Education Industry, Data Analysis, Boosting Algorithm, Data Mining, Support Vector Machine (SVM), Artificial Neural Network (ANN), Technology-Enhanced Learning (TEL), GBM Technology enhance learning(TEL).

References

H. A. Tarik and F. Yousef, "Artificial intelligence and machine learning to predict student performance during the COVID-19," Procedia Comput. Sci., vol. 184, pp. 835-840, 2021. Available from: https://doi.org/10.1016/j.procs.2021.03.104
A. Tarik et al., "Predicting Students Final Performance using Artificial Neural Networks," Big Data Mining and Analytics, pp. 294-301, 2022. Available from: http://dx.doi.org/10.26599/BDMA.2021.9020030
R. Luckin et al., "Intelligence unleashed: An argument for AI in education," 2016. Available from: https://discovery.ucl.ac.uk/id/eprint/1475756/
A. Fellah and A. Bandi, "The Essence of Recursion: Reduction, Delegation, and Visualization," Journal of Computing Sciences in Colleges, vol. 33, no. 5, pp. 115-123, 2018. Available from: https://shorturl.at/VI7Rb
J. Y. Chung and S. Lee, "Dropout early warning systems for high school students using machine learning," Children and Youth Services Review, vol. 96, pp. 346-353, 2019. Available from: https://doi.org/10.1016/j.childyouth.2018.11.030
S. Hamouda et al., "RecurTutor: An Interactive Tutorial for Learning Recursion," ACM Transactions on Computing Education, vol. 19, no. 1, pp. 1-25, 2019. Available from: https://dl.acm.org/doi/abs/10.1145/3218328
A. Guilherme, "AI and education: the importance of teacher and student relations," AI & society, vol. 34, pp. 47-54, 2019. Available from: https://doi.org/10.1007/s00146-017-0693-8
S. I. T. Joseph and I. Thanakumar, "Survey of data mining algorithm’s for intelligent computing systems," Journal of Trends in Computer Science and Smart Technology (TCSST), vol. 1, no. 01, pp. 14-24, 2019. Available from: https://doi.org/10.36548/jtcsst
K. Ever et al., "Comparison of machine learning techniques for prediction problems," in Web, Artificial Intelligence and Network Applications: Proceedings of the Workshops of the 33rd International Conference on Advanced Information Networking and Applications (WAINA-33), pp. 713-723, Springer International Publishing, 2019. Available from: http://dx.doi.org/10.1007/978-3-030-15035-8_69
O. Iatrellis et al., "A two-phase machine learning approach for predicting student outcomes," Education and Information Technologies, vol. 26, pp. 69-88, 2021. Available from: https://doi.org/10.1007/s10639-020-10260-x
E. Fernandes et al., "Educational data mining: Predictive analysis of academic performance of public-school students in the capital of Brazil," Journal of Business Research, vol. 94, pp. 335-343, 2019. Available from: https://doi.org/10.1016/j.jbusres.2018.02.012
N. Tomasevic et al., "An overview and comparison of supervised data mining techniques for student exam performance prediction," Computers & Education, vol. 143, pp. 103676, 2020. Available from: https://doi.org/10.1016/j.compedu.2019.103676
V. L. Uskov et al., "Machine learning-based predictive analytics of student academic performance in STEM education," in 2019 IEEE Global Engineering Education Conference (EDUCON), 2019, pp. 1370-1376. Available from: http://dx.doi.org/10.1109/EDUCON.2019.8725237
A. A. Saa et al., "Mining student information system records to predict students’ academic performance," in The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019), pp. 229-239, Springer International Publishing, 2020. Available from: https://doi.org/10.1007/978-3-030-14118-9_23
Y. Ever et al., "Classification analysis of intrusion detection on NSL-KDD using machine learning algorithms," in Mobile Web and Intelligent Information Systems: 16th International Conference, MobiWIS 2019, pp. 111-122, Springer International Publishing, 2019. Available from: https://doi.org/10.1007/978-3-030-27192-3_9
A. Abu Saa et al., "Factors affecting students’ performance in higher education: a systematic review of predictive data mining techniques," Technology, Knowledge and Learning, 2019. Available from: https://shorturl.at/TrJ5n
F. Ricci et al., Handbook on Recommender Systems in Technology Enhanced Learning, 2011. Available from: https://doi.org/10.1007/978-0-387-85820-3_12
N. Thai-Nghe et al., "Improving academic performance prediction by dealing with class imbalance," in 2009 Ninth International Conference on Intelligent Systems Design and Applications, 2009, pp. 878-883. Available from: https://doi.org/10.1109/ISDA.2009.15
N. Thai-Nghe et al., "Recommender system for predicting student performance," Procedia Computer Science, vol. 1, no. 2, pp. 2811-2819, 2010. Available from: https://doi.org/10.1016/j.procs.2010.08.006
C. Romero et al., "Data mining algorithms to classify students," in Educational Data Mining 2008, 2008. Available from: https://www.educationaldatamining.org/EDM2008/uploads/proc/1_Romero_3.pdf
R. Bekele and W. Menzel, "A Bayesian approach to predict performance of a student (BAPPS): A case with Ethiopian students," in Proceedings of the 2005 International Conference on Artificial Intelligence, 2005, pp. 1-6. Available from: https://nats-www.informatik.uni-hamburg.de/~menzel/papers/aia2005.pdf
T. Ahajjam et al., "Predicting Students' Final Performance Using Artificial Neural Networks," Big Data Mining and Analytics, vol. 5, no. 4, pp. 294-301, 2022. Available from: https://doi.org/10.26599/BDMA.2021.9020030
O. Chavarriaga et al., "A recommender system for students based on social knowledge and assessment data of competences," in Open Learning and Teaching in Educational Communities: 9th European Conference on Technology Enhanced Learning, EC-TEL 2014, pp. 56-69, Springer International Publishing, 2014. Available from: https://doi.org/10.1007/978-3-319-11200-8_5
M. Moutaib et al., "Internet of things: Energy consumption and data storage," Procedia Computer Science, vol. 175, pp. 609-614, 2020. Available from: https://doi.org/10.1016/j.procs.2020.07.088
B. Sekeroglu et al., "Student Performance Prediction and Classification Using Machine Learning Algorithms," in The 2019 8th International Conference, 2019. Available from: https://doi.org/10.1145/3318396.3318419

Cites this article as

V. Panwar, V. Srivastava, "Predicting Performance of Online Education Students Using IOT and Boosting Algorithmic Framework", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-12, Issue-2, Page No-53-56, 2025. Available from: https://doi.org/10.55524/ijirem.2025.12.2.9