Comparison of singular spectrum analysis forecasting algorithms for student’s academic performance during COVID-19 outbreak

Published on Jan 31, 2021
· DOI :10.19101/IJATEE.2020.S1762138
Muhammad Fakhrullah Mohd Fuad1
Estimated H-index: 1
Shazlyn Milleana Shaharudin4
Estimated H-index: 4
+ 2 AuthorsMuhammad Fareezuan Zulfikri1
Estimated H-index: 1
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
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