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Student Performance Prediction using Machine Learning Algorithms: A Review
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 32 |
| Year of Publication: 2022 |
| Authors: Saloni Shrigoud, Shweta Agrawal |
10.5120/ijca2022922396
|
Saloni Shrigoud, Shweta Agrawal . Student Performance Prediction using Machine Learning Algorithms: A Review. International Journal of Computer Applications. 184, 32 ( Oct 2022), 48-50. DOI=10.5120/ijca2022922396
Abstract
Today’s students are tomorrow’s future. It is important to invent a method to help each student “be all they can be!” .[11]Since it is used to assess how effectively educational institutions' programs are operating, student performance is obviously important to both students and institutions.. [11] The majority of institutions or departments consider student academic success to be their primary objective for the coming year, thus they implement their tactical plans to that end. Today’s era is a digital er ,so there is a lot of online information’s about students. Large amounts of online and offline learning data that students have left behind make it possible to anticipate their performance and to pre-intervene with at-risk kids, using data mining, machine learning, and deep learning to predict student achievement.[1] These methods can help the student as well as teachers to get the best results. These methods of analyzing using data can be used to identify the areas where students are falling short or excelling. Therefore, it will be beneficial for both students and teachers to understand their progress. [1]It aids teachers and supervisors in monitoring students to support them and integrate training programs to get the best results, in addition to forecasting students' performance..
References
- li, Shuping, and Taotang Liu. Performance Prediction for Higher Education Students Using Deep Learning. Volume 2021.
- Xiang Li, XiaoshengTang, ,Xiaoying Zhu, Yang Ji, and Xinning Zhu in “Student Academic Performance PredictionUsing Deep Multi-source Behaviour Sequential Network
- ChuangmingShiand, Bo Guo, GuangXu, , Li Yang, Rui Zhang” Deep learning architecture for predicting student performance”
- Abu Zohair and Lubna Mahmoud “Prediction on student performance by Modelling small student size”,
- Asif, R., Merceron, A., Ali, Haider, N.G. & Analyzing undergraduate students’ performance using educational data mining.
- Sánchez, E and Bahritidinov, B.,. Probabilistic classifiers and statistical dependency: the case for grade predictionbetween natural and artificial computation (pp. 394–403): Springer.
- Al-Razgan, M., Al-Khalifa, A. S., Al-Khalifa, H. S. (2014). Educational data mining: A systematic review of the published literature 2006-2013. In Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013) (711-719). Singapore: Springer
- Vipul Bansal,HimanshuBuckchash and Balasubramanian Raman “Computational Intelligence Enabled Student Performance Estimation ”
- Chen JF, Do QH. Training neural networks to predict student academic performance: a comparison of cuckoo search and gravitational search algorithms
- RamdayalTanwar, “Analysis of Student Performance using Modified K-Means Algorithms”
- Bailey, J., Zhang, R., Rubinstein, B., et al.: Identifying at-risk students in massive open online courses. In: AAAI (2015)
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