The week's pick
Reseach Article
Extreme Learning Machine Models for Predicting Student Performance
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 174 - Number 22 |
| Year of Publication: 2021 |
| Authors: Wedson L. Soares, Roberta A. De A. Fagundes |
10.5120/ijca2021921122
|
Wedson L. Soares, Roberta A. De A. Fagundes . Extreme Learning Machine Models for Predicting Student Performance. International Journal of Computer Applications. 174, 22 ( Feb 2021), 1-7. DOI=10.5120/ijca2021921122
Abstract
Predicting the individual performance of each student can provide valuable information as to which students are at greatest risk of failure or dropout, and consequently highlight which characteristics negatively influence the student’s academic life. Data mining provides the tools necessary to address this educational data in the search for knowledge and patterns that can be obtained. Therefore, this work uses an educational database obtained at the UCI machine learning repository related to students grades in Portuguese and proposes models using extreme learning machine networks, ensemble learning and optimization by particle swarm in order to predict students’ grades. In addition two simulated data sets were also used to verify the consistency of the results obtained through the proposed regression models. After obtaining the error value for each proposed model, hypothesis tests were performed to ascertain the veracity of the results. The results indicate a better performance of the model that combines the ensemble learning, particle swarm optimization and extreme learning machine networks.
References
- Hind Almayan andWaheeda Al Mayyan. Improving accuracy of students’ final grade prediction model using pso. In 2016 6th International Conference on Information Communication and Management (ICICM), pages 35–39. IEEE, 2016.
- RSJD Baker et al. Data mining for education. International encyclopedia of education, 7(3):112–118, 2010.
- Peter B¨uhlmann. Bagging, boosting and ensemble methods. In Handbook of Computational Statistics, pages 985–1022. Springer, 2012.
- Pete Chapman, Julian Clinton, Randy Kerber, Thomas Khabaza, Thomas Reinartz, Colin Shearer, Rudiger Wirth, et al. Crisp-dm 1.0: Step-by-step data mining guide. SPSS inc, 9:13, 2000.
- Jing Chen, Jun Feng, Xia Sun, Nannan Wu, Zhengzheng Yang, and Sushing Chen. Mooc dropout prediction using a hybrid algorithm based on decision tree and extreme learning machine. Mathematical Problems in Engineering, 2019, 2019.
- Paulo M da Silva, Marilia NCA Lima, Wedson L Soares, Iago RR Silva, A de A Roberta, and Fernado F de Souza. Ensemble regression models applied to dropout in higher education. In 2019 8th Brazilian Conference on Intelligent Systems (BRACIS), pages 120–125. IEEE, 2019.
- Thomas G Dietterich et al. Ensemble learning. The handbook of brain theory and neural networks, 2:110–125, 2002.
- Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. From data mining to knowledge discovery in databases. AI magazine, 17(3):37–37, 1996.
- William J Frawley, Gregory Piatetsky-Shapiro, and Christopher J Matheus. Knowledge discovery in databases: An overview. AI magazine, 13(3):57–57, 1992.
- Magdalena Graczyk, Tadeusz Lasota, Bogdan Trawi´nski, and Krzysztof Trawi´nski. Comparison of bagging, boosting and stacking ensembles applied to real estate appraisal. In Asian conference on intelligent information and database systems, pages 340–350. Springer, 2010.
- David J Hand, Heikki Mannila, and Padhraic Smyth. Principles of data mining (adaptive computation and machine learning). MIT Press, 2001.
- Seyed MH Hasheminejad and M Sarvmili. S3pso: Students’ performance prediction based on particle swarm optimization. Journal of AI and Data Mining, 7(1):77–96, 2019.
- Guang-Bin Huang and Haroon A Babri. Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions. IEEE transactions on neural networks, 9(1):224–229, 1998.
- Guang-Bin Huang, Qin-Yu Zhu, and Chee-Kheong Siew. Extreme learning machine: theory and applications. Neurocomputing, 70(1-3):489–501, 2006.
- James Kennedy and Russell Eberhart. Particle swarm optimization. In Proceedings of ICNN’95-International Conference on Neural Networks, volume 4, pages 1942–1948. IEEE, 1995.
- Mrinal Pandey and S Taruna. A comparative study of ensemble methods for students’ performance modeling. International Journal of Computer Applications, 103(8), 2014.
- Crist´obal Romero and Sebasti´an Ventura. Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6):601–618, 2010.
- Yuhui Shi and Russell C Eberhart. Parameter selection in particle swarm optimization. In International conference on evolutionary programming, pages 591–600. Springer, 1998.
- Yuhui Shi et al. Particle swarm optimization: developments, applications and resources. In Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546), volume 1, pages 81–86. IEEE, 2001.
- EdwardWakelam, Amanda Jefferies, Neil Davey, and Yi Sun. The potential for student performance prediction in small cohorts with minimal available attributes. British Journal of Educational Technology, 51(2):347–370, 2020.
- Qi Yu, Yoan Miche, Emil Eirola, Mark Van Heeswijk, Eric S´eVerin, and Amaury Lendasse. Regularized extreme learning machine for regression with missing data. Neurocomputing, 102:45–51, 2013.
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