ABSTRACT
Since the emergence of COVID-19, virtual learning has been the norm at institutions of higher education. Video lecture have emerged as a useful tool for academics in delivering distance education courses. Furthermore, video lecture are expected to keep the educational system running smoothly despite the pandemic. Therefore, the purpose of this study is to assess university students' acceptance of video lecture by extending the Technology Acceptance Model (TAM) with internet self-efficacy and learner-content interaction as the extended variable. Data from 390 students were gathered and analysed by Partial Least Squares-Structural Equation Modelling (PLS-SEM). The result shows that both internet self-efficacy and learner-content interaction plays an important role to determine the acceptance of video lecture and all path relationships in TAM were significant. Compared to internet self-efficacy, learner-content interaction have a bigger impact on the perceived usefulness of video lecture. This demonstrates that interaction between the learner and the content is important in deciding the acceptance of video lecture, and that educators should make video lecture brief and engaging so that students would enjoy using them. © 2022 IEEE.
ABSTRACT
Optimization problem is a type of mathematical problem with several uses in real life. As one of the commonly applied methods for solving optimization problem, the conjugate gradient (CG) method has undergone numerous modifications in order to improve its efficiency. The hybrid CC is one of the best CC methods which can also be applied for data fitting [1]. Nowadays, Corona Virus Disease (Covid-19) has been extensively studied by many researchers worldwide due to the rising number of infected cases and deaths resulting from contracting the virus. In 2020, the Malaysian government enforced the first 'Movement Control Order (MCO)' in order to significantly lower the infection rate. However, the number of cases rose again at the end of year 2020, leading to the necessity to implement the second MCO in January 2021. In this study, a new hybrid conjugate gradient method is introduced to analyse the Covid-19 cases in Malaysia before MCO 2.0. This new hybrid method yields a better numerical result compared to classical and other hybrid CG methods.
ABSTRACT
Conjugate gradient (CG) method is well-known for its ability to solve unconstrained optimization (UO.) problems. This article presenting a new CG method with sufficient descent conditions which improves the former method developed by Rvaie, Mustafa, Ismail and Leong (RMIL). The efficacy of the proposed method has been demonstrated through simulations on the Kijang Emas pricing regression problem. The daily data between January 2021 to May 2021 were obtained from Malaysian Ministry of Health and Bank Negara Malaysia. The dependent variable for this study was the Kijang Emas price, and the independent variables were the coronavirus disease (COVID-19) measures (i.e., new cases, R-naught, death cases, new recovered). Data collected were analyzed on its correlation and coefficient determinant, and the influences of COVID-19 on Kijang Emas price was examined through multiple linear regression model. Findings revealed that the suggested technique outperformed the existing CG algorithms in terms of computing efficiency. © 2022 Institute of Advanced Engineering and Science. All rights reserved.