In this ever-progressive digital era, conventional e-learning methods have become inadequate to handle the requirements of upgraded learning processes especially in the higher education. E-learning adopting Cloud computing is able to transform e-learning into a flexible, shareable, content-reusable, and scalable learning methodology. Despite plentiful Cloud e-learning frameworks have been proposed across literature, limited researches have been conducted to study the usability factors predicting continuance intention to use Cloud e-learning applications. In this study, five usability factors namely Computer Self Efficacy (CSE), Enjoyment (E), Perceived Ease of Use (PEU), Perceived Usefulness (PU), and User Perception (UP) have been identified for factor analysis. All the five independent variables were hypothesized to be positively associated to a dependent variable namely Continuance Intention (CI). A survey was conducted on 170 IT students in one of the private universities in Malaysia. The students were given one trimester to experience the usability of Cloud e-Learning application. As an instrument to analyse the usability factors towards continuance intention of the application, a questionnaire consisting thirty questions was formulated and used. The collected data were analysed using SMARTPLS 3.0. The results obtained from this study observed that computer self-efficacy and enjoyment as intrinsic motivations significantly predict continuance intention, while perceived ease of use, perceived usefulness and user perception were insignificant. This outcome implies that computer self-efficacy and enjoyment significantly affect the willingness of students to continue using Cloud e-learning application in their studies. The discussions and implications of this study are vital for researchers and practitioners of educational technologies in higher education.

Customers are prominent resources in every business for its sustainability. Therefore, predicting customer churn is significant for reducing churn, particularly in the high-churn-rate telecommunications business. To identify customers at risk of churning, tactical marketing actions can be strategized to raise the likelihood of the churn-probable customers remaining as customers. This might provide a corporation with significant savings. Hence, in this work, a churn prediction system is developed to assist telecommunication operators in detecting potential churn customers. In the proposed framework, the input data quality is improved through the processes of exploratory data analysis and data preprocessing for identifying data errors and comprehending data patterns. Then, feature engineering and data sampling processes are performed to transform the captured data into an appropriate form for classification and imbalanced data handling. An optimized ensemble learning model is proposed for classification in this framework. Unlike other ensemble models, the proposed classification model is an optimized weighted soft voting ensemble with a sequence of weights applied to weigh the prediction of each base learner with the hypothesis that specific base learners in the ensemble have more skill than others. In this optimization, Powell’s optimization algorithm is applied to optimize the ensemble weights of influence according to the base learners’ importance. The efficiency of the proposed optimally weighted ensemble learning model is evaluated in a real-world database. The empirical results show that the proposed customer churn prediction system achieves a promising performance with an accuracy score of 84% and an F1 score of 83.42%. Existing customer churn prediction systems are studied. We achieved a higher prediction accuracy than the other systems, including machine learning and deep learning models.

Technological progress has resulted in constant change of social structures and led to continuous evolution of attitudes and values. Thus, a timely and compre-hensive integrated technology acceptance framework was established, and factors predicting intention to use Cloud e-learning were empirically identified. A total of 11 determinants from varying sources of IS success, learning object criteria, technology acceptance, motivations, social cognitive, and expectancy values were unified and analysed in an integrated manner. The target population for this study was a group of IT students in a private university in southern region Malaysia. The results obtained from this study observed that content quality, pedagogical quality, perceived usefulness, social influence, attainment value, and utility value significantly predict intention to use Cloud e-learning. By integrating IS success, learning object criteria, technology acceptance, motivations, social cognitive, and expectancy values into a single framework, new insights on Cloud e-learning ac-ceptance in higher education can be attained.

This study presents the development of a student-centric framework for utilizing virtual reality (VR) technologies in education, specifically focusing on enhancing computational thinking skills. While numerous frameworks exist in this domain, they often lack consideration of student preferences, which are integral for fostering learner autonomy. Our proposed framework, with components developed from the constructivist learning theory, emphasises creating knowledge through interaction with the environment, focusing on autonomy, mastery and purpose as drivers of intrinsic outcomes. Through a survey administered to hundred and fifty-seven participants, we sought to identify student-preferred strategies for learning computational thinking skills via VR interventions. Results highlighted key challenges students face when working on computational tasks are related to algorithmic and abstraction thinking. To ease the aforementioned challenges, our findings suggest a preference among students for situated-based learning approaches within VR environments. Additionally, participants recognized the importance of motivational outcomes in improving autonomy and mastery within VR-based learning tasks. Students also preferred tasks that enhanced self-efficacy, contributing to a greater sense of purpose in their learning endeavours. Overall, this investigation sets a foundation for more student-centric, constructivist and intrinsically-based VR frameworks in education.

Self-Perceived Entrepreneurship Skills for Undergraduates of Private University in Malaysia