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UPSI Digital Repository (UDRep)
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| 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 (?) and number of leading components (?). The forecasting
approaches in SSA model was based on the Grade Point Average (GPA) for
undergraduate students from Faculty of Science and Mathematics, UPSI via online
classes during COVID-19 outbreak. The experiment revealed that parameter L= 11
( ?/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 affectivity of
obtaining more data sets including more respondents from various universities in
Malaysia. |
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