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

Abstract

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.