Motivation is a crucial factor in online learning as it can significantly impact the student’s engagement and success. Some students may be more motivated by a need for affiliation and social connection, while others may be more motivated by a need for achievement and recognition, or a need for power and autonomy. Thus, this quantitative study aims to explore the perception of learners on their learning motivation by examining how affiliation, achievement, and power influence learners’ motivation. A total of 208 respondents from public and private universities in Malaysia were gathered using a questionnaire that was adapted from a previous study by (Fowler, 2018). There are seven sections with related questions to measure the three types of factors that affected learning motivation. In general, the results revealed there is a significant association between affiliation, achievement, and power in learning motivation. By understanding the findings of this study, instructors might be able to design interventions and strategies to enhance learner motivation. Then, by recognizing the importance of affiliation, achievement, and power in learning motivation, policymakers may consider designing education frameworks that prioritize the supportive learning environment and promote student engagement and motivation.

Because of the COVID-19 epidemic, several educational institutions all around the world had to postpone classes. In Malaysia, the suspension began in March 2020. Throughout the first semester, this scenario persisted, requiring educational institutions to move to online classrooms and changing the academic calendar for the following years. The reality of online learning has sparked a discussion about a new pedagogical approach in which teachers and students must learn how to manage online learning and make the most of online tools. Students must first understand the challenges presented by online distance learning in order to do this (ODL). This essay will examine how students perceive the difficulties of online education in the wake of the COVID-19 epidemic. 495 UiTM Terengganu students who actively participated in online learning activities served as the study's sample. Exploratory Factor Analysis (EFA) and Logistic Regression were used to examine the difficulties of online learning. In this study, the data was analysed using EFA and Logistic Regression to determine what factors account for the ODL issues that UiTM Terengganu students face. The results show that for most students, the internet and technology provide the biggest obstacles. Interaction between students and lecturers, psychological problems, challenging examinations, and self-management were also mentioned as obstacles.

Efficient management of household trash is essential to maintaining a sustainable society and a good environment. Low community engagement in environmental cleanup has led to dozens of unused refuse management apps. Today’s refuse management system lacks a secure identification protocol for identifying users, especially those who have signed up for the app. Predicting and understanding human household behavior is needed, and it remains a complex challenge. Therefore, this study aims to predict human household behavior in the refuse management system using artificial neural networks (ANN). The work involved in developing the prediction model included data collection, data pre-processing, neural network model development, and performance validation. There are 505 participants, urban residents in Kuala Lumpur obtained for this study. ANN with one hidden layer is developed in MATLAB. The results show that the accuracy of the developed model is 83%. It indicates that ANN performed well in predicting household behavior in the refuse management system.