The higher education system is one of the sectors that has been affected by the coronavirus disease 2019 (COVID-19) pandemic. In response to that, Universiti Teknologi MARA (UiTM) has moved all classes to open and distance learning (ODL) effective 13 April 2020. ODL has greatly assisted the process of teaching and learning during the pandemic, but the sudden adoption from face-to-face learning to ODL can lead to stress among university students. Thus, this study aimed to identify the level of stress perceived by UiTM students during ODL and explore the influence of socio-demographic characteristics on the level of perceived stress among UiTM students during ODL. A cross-sectional survey design was used which was constructed of three main parts; demographic characteristics, Perceived Stress Scale-10 (PSS-10) developed by Cohen, Kamarck, and Mermelstein (1983), and a qualitative exploratory question. The finding concluded that in general, UiTM students showed moderate stress during ODL. The factors that contributed to this are a poor internet connection, academic workload, high academic workload, deadline of assignment, internet connection, learning environment, and family problems.
E-learning using Massive Open Online Courses (MOOCs) has attracted a great deal of attention among higher education providers. The use of MOOCs is one of the great ideas supported by the Malaysian Ministry of Education in making the standard of our country’s education system in line with global education. Today, there were a growing number of MOOCs that continually becoming available on commercial platforms such as Openlearning.com and MOOCs Universiti Teknologi MARA (UiTM). Like many other subjects, Mathematical Statistics is a challenging subject for teaching and learning. Certain skills in this subject require ongoing guidance on how to provide the best teaching. Hence, MOOCs for Mathematical Statistics has been created to provide knowledge and skills to teach mathematics and statistical modeling briefly, conveniently, and effectively. This study focuses on providing a general design for assessing learners who study Mathematical Statistics through MOOCs platform. The platform is developed in such a way that learners can discuss and reflect. The design of MOOCs is governed by the Guidelines for Development and Delivery of Malaysia MOOCs. With the development of these MOOCs, it is hoped that it could help learners to understand Mathematical Statistics in a more effective and efficient way.
Unlike simple random sampling, complex sample designs involve additional considerations such as multistage sampling, stratification, and unequal probability of selection. A basic problem with complex surveys is in variance estimation which requires the use of approximate methods. Generally, such methods are based on either the Taylor series linearization or the replication techniques. Statistical software that use standard packages usually assume that simple random sampling of elements is inadequate for data analysis from complex surveys, especially for purpose of variance estimation. This study compares the complex sample design features produced by three statistical software packages designed to handle complex surveys (SPSS 16.0 Complex Samples, SAS 9.0 Complex Samples, and WesVarPc 5.1). Comparisons among the software are made based on the types of sample design, sampling error estimates, method of variance estimation and cost of software packages. The results of the finding show that WesVarPc can be downloaded for free from Web and offers complete basic of descriptive analyses. Although expensive, SPSS 16.0 Complex Samples and SAS 9.0 Complex Samples have been dominant in the field of data management and data analysis.
Projecting future infant mortality rate (IMR) is an important subject in ensuring the stability of health in one nation or a specific region in general. Secondary data of IMR from December 1950 until December 2020 from United NationsWorld Population Prospects were used to project the trend of IMR in Malaysia up to 2023. In this study, five different forecasting models were adopted including Mean model, Naïve model, Autoregressive Integrated Moving Average (ARIMA) model, Exponential State Space model and Neural Network model. The results were analyzed using R programing and RStudio. The out-sample forecasts of mortality rates were evaluated using six error measures namely, Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Percentage Error (MPE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Scaled Error (MASE). Consequently, the keen analysis was focused on the trend and projection of infant mortality rate in the future using the most accurate model. The results showed that the “win” model for this study is ARIMA (0,2,0) model. The model provided a consistent estimate of IMR in relation to a similar decreasing pattern as shown by the original data and hence a reliable projection of IMR. The three ahead forecast values showed that IMR is likely to keep on continuously decreasing in the future. This study could become a guideline for human resource management and health care allocation planning. A forecast of IMR can help the implementation of interventions to reduce the burden of infant mortality within the target range.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.