|
UPSI Digital Repository (UDRep)
|
|
| ||||||||||||||||||||||||||||
| Abstract : Universiti Pendidikan Sultan Idris |
| Due to the spread of COVID-19 that hit Malaysia, all academic activities at educational institutions including universities had to be carried out via online learning. However, the effectiveness of online learning is remains unanswered. Besides, online learning may have a significant impact if continued in the upcoming academic sessions. Therefore, the core of this study is to predict the academic performance of undergraduate students at one of the public universities in Malaysia by using Recurrent Forecasting-Singular Spectrum Analysis (RF-SSA) and Vector Forecasting-Singular Spectrum Analysis (VF-SSA). The key concept of the predictive model is to improve the efficiency of different types of forecast model in SSA by using two parameters which are window length (L) and number of leading components (r). The forecasting approaches in SSA model was based on the Grading Point Assessments (GPA) for undergraduate students from Faculty Science and Mathematics, UPSI via online classes during COVID-19 outbreak. The experiment revealed that parameter L= 11 (T/20) has the best prediction result for RF-SSA model with RMSE value of 0.19 as compared to VF-SSA of 0.30. This signifies the competency of RF-SSA in predicting the students? academic performances based on GPA for the upcoming semester. Nonetheless, an RF-SSA algorithm should be developed for higher effectivity of obtaining more data sets including more respondents from various universities in Malaysia. ? 2021 Muhammad Fakhrullah Mohd Fuad et al. |
| References |
Aggarwal, D., & Sharma, D. (2019). Application of clustering for student result analysis. International Journal of Recent Technology and Engineering, 7(6), 50-53. Retrieved from www.scopus.com Alexandrov, T., Golyandina, N., & Spirov, A. (2008). Singular spectrum analysis of gene expression profiles of early drosophila embryo: Exponential-in-distance patterns. Res Lett Signal Process, 2008, 1-5. Retrieved from www.scopus.com Alvin, M. D., George, E., Deng, F., Warhadpande, S., & Lee, S. I. (2020). The impact of COVID-19 on radiology trainees. Radiology, 296(2), 246-248. doi:10.1148/radiol.2020201222 Asif, R., Merceron, A., & Pathan, M. K. (2014). Predicting student academic performance at degree level: A case study. International Journal of Intelligent Systems and Applications, 7(1), 49-61. Retrieved from www.scopus.com Bao, W. (2020). COVID-19 and online teaching in higher education: A case study of peking university. Human Behavior and Emerging Technologies, 2(2), 113-115. doi:10.1002/hbe2.191 Burman, I., & Som, S. (2019). Predicting students academic performance using support vector machine. Paper presented at the Proceedings - 2019 Amity International Conference on Artificial Intelligence, AICAI 2019, 756-759. doi:10.1109/AICAI.2019.8701260 Retrieved from www.scopus.com Danilov, D. (1997). The caterpillar method for time series forecasting. Principal Components of Time Series: The Caterpillar Method, , 73-104. Retrieved from www.scopus.com Danilov, D. L. (1997). Principal components in time series forecast. Journal of Computational and Graphical Statistics, 6(1), 112-121. doi:10.1080/10618600.1997.10474730 de Carvalho, M., & Rua, A. (2017). Real-time nowcasting the US output gap: Singular spectrum analysis at work. International Journal of Forecasting, 33(1), 185-198. doi:10.1016/j.ijforecast.2015.09.004 Dong, Y., Zhang, L., Liu, Z., & Wang, J. (2020). Integrated forecasting method for wind energy management: A case study in china. Processes, 8(1) doi:10.3390/pr8010035 Ghodsi, M., Hassani, H., Rahmani, D., & Silva, E. S. (2018). Vector and recurrent singular spectrum analysis: Which is better at forecasting? Journal of Applied Statistics, 45(10), 1872-1899. doi:10.1080/02664763.2017.1401050 Golyandina, N., & Korobeynikov, A. (2014). Basic singular spectrum analysis and forecasting with R. Computational Statistics and Data Analysis, 71, 934-954. doi:10.1016/j.csda.2013.04.009 Golyandina, N., Nekrutkin, V., & Zhigljavsky, A. (2001). Analysis of Time Series Structure: SSA and Related Techniques, Retrieved from www.scopus.com Golyandina, N., & Shlemov, A. (2014). Variations of Singular Spectrum Analysis for Separability Improvement:Non-Orthogonal Decompositions of Time Series, Retrieved from www.scopus.com Hamoud, A. K., Hashim, A. S., & Awadh, W. A. (2018). Predicting student performance in higher education institutions using decision tree analysis. International Journal of Interactive Multimedia and Artificial Intelligence, 5(2), 26-31. Retrieved from www.scopus.com Hassani, H., & Mahmoudvand, R. (2013). Multivariate singular spectrum analysis: A general view and new vector forecasting approach. International Journal of Energy and Statistics, 1(1), 55-83. Retrieved from www.scopus.com Iyer, P., Aziz, K., & Ojcius, D. M. (2020). Impact of COVID-19 on dental education in the united states. Journal of Dental Education, 84(6), 718-722. doi:10.1002/jdd.12163 Kanneganti, A., Sia, C. -., Ashokka, B., & Ooi, S. B. S. (2020). Continuing medical education during a pandemic: An academic institution's experience. Postgraduate Medical Journal, doi:10.1136/postgradmedj-2020-137840 Lau, E. T., Sun, L., & Yang, Q. (2019). Modelling, prediction and classification of student academic performance using artificial neural networks. SN Applied Sciences, 1(9) doi:10.1007/s42452-019-0884-7 Mahmoudvand, R., Alehosseini, F., & Rodrigues, P. C. (2015). Forecasting mortality rate by singular spectrum analysis. Revstat Statistical Journal, 13(3), 193-206. Retrieved from www.scopus.com Mian, A., & Khan, S. (2020). Medical education during pandemics: A UK perspective. BMC Medicine, 18(1) doi:10.1186/s12916-020-01577-y Rose, S. (2020). Medical student education in the time of COVID-19. Medical Student Education in the Time of COVID-19, Retrieved from www.scopus.com Sandhu, P., & de Wolf, M. (2020). The impact of COVID-19 on the undergraduate medical curriculum. Medical Education Online, 25(1) doi:10.1080/10872981.2020.1764740 Shaharudin, S. M., Ahmad, N., Mohamed, N. S., & Aziz, N. (2020). Performance analysis and validation of modified singular spectrum analysis based on simulation torrential rainfall data. International Journal on Advanced Science, Engineering and Information Technology, 10(4), 1450-1456. doi:10.18517/ijaseit.10.4.11653 Shaharudin, S. M., Ahmad, N., & Yusof, F. (2015). Effect of window length with singular spectrum analysis in extracting the trend signal on rainfall data. Paper presented at the AIP Conference Proceedings, , 1643 321-326. doi:10.1063/1.4907462 Retrieved from www.scopus.com Shaharudin, S. M., Ahmad, N., & Zainuddin, N. H. (2019). Modified singular spectrum analysis in identifying rainfall trend over peninsular malaysia. Indonesian Journal of Electrical Engineering and Computer Science, 15(1), 283-293. doi:10.11591/ijeecs.v15.i1.pp283-293 Shaharudin, S. M., Ismail, S., Tan, M. L., Mohamed, N. S., & Sulaiman, N. A. F. (2020). Predictive modelling of covid-19 cases in malaysia based on recurrent forecasting-singular spectrum analysis approach. International Journal of Advanced Trends in Computer Science and Engineering, 9(1.4), 175-183. Retrieved from www.scopus.com Vile, J. L., Gillard, J. W., Harper, P. R., & Knight, V. A. (2012). Predicting ambulance demand using singular spectrum analysis. Journal of the Operational Research Society, 63(11), 1556-1565. doi:10.1057/jors.2011.160 Zakaria, R., Satari, S. Z., Damahuri, N. A., & Khairuddin, R. (2019). Descriptive analysis of students' CGPA: A case study of universiti malaysia pahang. Paper presented at the IOP Conference Series: Materials Science and Engineering, , 469(1) doi:10.1088/1757-899X/469/1/012100 Retrieved from www.scopus.com |
| This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |