UPSI Digital Repository (UDRep)
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UPSI Digital Repository (UDRep)
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| Abstract : Universiti Pendidikan Sultan Idris |
| This study proposed a statistical investigate the pattern of students’ academic
performance before and after online learning due to the Movement Control Order
(MCO) during pandemic outbreak and a modelling students’ academic performance
based on classification and regression in Support Vector Machine (SVM). Data sample
consist of 228 students were taken from undergraduate students of Department of
Mathematics, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris
(UPSI). The instrument used for data collection is a questionnaire. Student’s Grade
Point Average (GPA) before and after online learning were taken to identify the pattern
of students’ academic performance before and after Movement Control Order (MCO)
while Cumulative Grade Point Average (CGPA) after online learning were obtained to
developed model of academic performances during COVID-19 outbreak. The
algorithm of Support Vector Machine (SVM) classification and regression was used to
develop a model of students’ academic performance. For the Support Vector Machine
(SVM) classification algorithm, there are two important parameters which are C
(misclassification tolerance parameter) and epsilon while for the Support Vector
Machine (SVM) regression also need to identify two important parameter which are
penalty term and epsilon. All the parameters need to identify before proceed the further
analysis. The parameters was applied to four different types of kernel which is linear
kernel, radial basis function kernel, polynomial kernel and sigmoid kernel for both
model. The result was found that the best accuracy achieved by SVM classification are
73.69% by using radial basis kernel with parameter of misclassification tolerance C is
128 and epsilon is 0.6. Meanwhile, SVM regression model also resulted radial basis
function as the best kernel with parameter of penalty term is 4 and epsilon 0.8 with the
0.255659 as the lowest value of RMSE. The pattern of prediction of students’ academic
performance are similar with the current CGPA. Therefore, Support Vector Machine
regression are able to predict students’ academic performance. |
| References |
Ahn, Nguyen. (2017). The Impact of Online Learning Activities on Student Learning Outcome in Blended Learning Course. Journal of Information & Knowledge Management. 16. 10.1142/S021964921750040X.
Akinode John Lekan. (2019, May 4). STUDENT ACADEMIC PERFORMANCE PREDICTION USING SUPPORT VECTOR MACHINE. ResearchGate; unknown.https://www.researchgate.net/publication/332864818_STUDENT_AC ADEMIC_PERFORMANCE_PREDICTION_USING_SUPPORT_VECTOR_ MACHINE
Analytics Vidhya. (2020, March 27). Support Vector Regression In Machine Learning. Retrieved February 18, 2021, from Analytics Vidhya website: https://www.analyticsvidhya.com/blog/2020/03/support-vector-regression- tutorial-for-machine-learning/
Baba, M. D. I. B., & Pendek, G. J. (2009). Keberkesanan Pengajaran Dan Pembelajaran Dan Kaitannya Terhadap Prestasi Akademik Pelajar Uthm.
Bernama. (2020). UPSI to implement online teaching in line ministry’s instruction. The Sun Daily. Retrieved from https://www.thesundaily.my/local/upsi-to- implement-online-teaching-in-line-ministry-s-instructionDB2175401
Gonzalez, C. (2009). Conceptions of, and approaches to, teaching online: a study of lecturers teaching postgraduate distance courses. Higher Education, 57(3), 299– 314. https://doi.org/10.1007/s10734-008-9145-1.
Hsu, Chih-wei & Chang, Chih-chung & Lin, Chih-Jen. (2003). A Practical Guide to Support Vector Classification Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin.
Kayode, E.O., & Teng, T.-L. (2014). The Impact Of Transactional Distance Dialogic Interactions On Student Learning Outcomes In Online And Blended Environments. Comput Educ, 78, 414–427. article. http://doi.org/10.1016/j.compedu.2014.06.011
Moura, M. das C., Zio, E., Lins, I. D., & Droguett, E. (2011). Failure and reliability prediction by support vector machines regression of time series data. Reliability Engineering & System Safety, 96(11), 1527–1534. https://doi.org/10.1016/j.ress.2011.06.006
Mushtaq, I., & Khan, S. N. (2012). Factors Affecting Students’ Academic Performance. Global journal of management and business research, 12(9).
Nahar, K., Ottom, M. A., Alshibli, F., & Abu Shquier, M. (2020). Air Quality Index Using Machine Learning – A Jordan Case Study. Compusoft: An International Journal of Advanced Computer Technology, 9(9), 3831-3840. Retrieved from https://ijact.in/index.php/ijact/article/view/1218
Nurul Ainina Filza Sulaiman, Syazlyn Milleana Shaharudin, Nurul Hila Zainuddin, Sumayyah Aimi Mohd Najib. (2020). Improving Support Vector Machine Rainfall Classification Accuracy based on Kernel Parameters
Oloruntoba, S. A., and Akinode, J. L. (2017). Student academic performance prediction using support vector machine. International Journal of Engineering Sciences and Research Technology 6(12): 588–597.
O.Yamini and Prof S. Ramakrishna. (2015). A Study on Advantages of Data Mining Classification Techniques, International Journal of Engineering Research & Technology (IJERT), vol. 4, no. 9,pp. 969-972.
Oye N. A., et al. “The impact of e-learning on students’ performance in tertiary institutions.” International Journal of Computer Networks and Wireless Communications, Vol. 2, No. 2, 2012, pp. 121-30.
Raihana, Z., and Farah Nabilah, A. M. (2018). Classification of students based on quality of life and academic performance by using support vector machine. Journal of Academia UiTM Negeri Sembilan 6(1): 45–52.
R.G Brereton and G. R. Lloyd. (2010). Support Vector Machines for Classification and Regression, The Royal Society of Chemistry, Analyst, 135, 230–267.
S. M. Shaharudin, N. Ahmad, and F. Yusof. (2013). Improved Cluster Partition in Principal Component Analysis Guided Clustering, International Journal of Computer Applications, vol. 75, no. 11, pp. 22-25.
S. Mukherjee, E. Osuna and F. Girosi, "Nonlinear prediction of chaotic time series using support vector machines," Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop, Amelia Island, FL, USA, 1997, pp. 511-520, doi: 10.1109/NNSP.1997.622433.
Shahiri, A. M., Husain, W., Rashid, N. A. (2015). A review on predicting students performance using data mining techniques. The 3rd Information System International Conference, Elsevier, 72, p 414–422.
Tang, Ashley. (2020). Malaysia announces movement control order after spike in Covid-19 cases (updated). The Star. Archived from https://www.thestar.com.my/news/nation/2020/03/16/malaysia-announces- restricted- movement-measure-after-spike-in-covid-19-cases
U.S. Department of Education, Office of Planning, Evaluation, and Policy Development. (2010). Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies, Washington, D.C. https://www2.ed.gov/rschstat/eval/tech/evidence-based- practices/finalreport.pdf.
World Health Organization. (2020). Novel Coronavirus (2019-nCoV) Advice for the Public. Retrieve from https://www.who.int/emergencies/diseases/novel- coronavirus-2019/advice-for-public.
World Health Organization. (2020). Pneumonia of Unknown Cause. Retrieve from https://www.who.int/csr/don/05- january-2020-pneumonia-of-unkown-cause- china/en/
World Health Organization. (2020, June 30). Timeline of WHO’s response to COVID-19. Retrieved from https://www.who.int/malaysia/emergencies/coronavirus-disease-(covid-19)-in- malaysia
Y. Palansamy. (2020). Higher Education Ministry: All university lectures to be online-only until end 2020, with a few exceptions. Retrieved from https://www.malaymail.com/news/malaysia/2020/05/27/higher-education- ministryall-university-lectures-to-be-online-only-until-e/1869975
Y. Zhang and L. Wu. (2012). Classification of fruits using computer vision and a multiclass support vector machine, Sensors (Switzerland), vol. 12, no. 9, pp. 12489-12505.
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