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<span lang="EN-US">Online learning is being considered a new model of knowledge exchange in modern education. In parallel with the incredible impacts of the global pandemic, this is considered an opportunity to promote the development of online learning globally. Therefore, this study proposed a research framework including four factors affecting learner satisfaction towards online learning during the COVID-19 pandemic at a university, which are system quality, service quality, transformational leadership, and self-efficacy.</span><span lang="EN-US"> A questionnaire was conducted online to assess which 131 respondents were representative students from two large private universities in Da Nang: FPT University and Duy Tan University. The results from the regression analysis show that three factors have a positive impact on learner satisfaction during COVID-19. This study concludes that students at private universities in Da Nang prioritize system quality as the most significant factor in their satisfaction with the online learning system, followed by transformational leadership and the last one is self-efficacy. Therefore, it can be more strategic for private organizations, developers, software designers, or even transformation-trained trainers to be emphasized to build a system of processes for implementing online learning for students effectively.</span>

Aim/Purpose: The purpose of this study is to assess the factors that have significant influences on students’ adoption of e-learning systems and to what extent these factors affect them. Background: E-learning has become an essential tool and makes it an inevitable option for education in the future. E-learning has received considerable attention in recent times as a global spread of the COVID-19 pandemic. Nevertheless, developing countries, including Vietnam, are facing many difficulties when adopting e-learning systems. Therefore, it is essential to comprehensively evaluate the factors that influence the intention of students to use e-learning to enhance the implementation process and also improve educational quality. Methodology: Initially, the authors synthesized a literature review from 112 related studies to complete the proposed research model including the combination of C-TAM-TPB model and external variables impacting students’ adoption of e-learning systems. After that, a sample of 172 students at FPT University Vietnam was collected to test the proposed model and explain students’ intentions. The dataset was investigated and analyzed with PLS-SEM using the SmartPLS 3.3.3 tool. Contribution: The study has made a significant contribution to the current literature by pro-posing an extended model between C-TAM-TPB and three external variables to provide a better understanding on students' behavioral intention to use e-learning. Furthermore, the research findings also provide useful guidelines for innovating and improving an effective e-learning system to advance student learning motivation in the educational environment. Findings: The findings demonstrate that Computer Self-efficacy and Perceived Accessibility have an important influence on Perceived Ease of Use by learners of an e-learning system. Furthermore, Perceived Enjoyment affects the Perceived Usefulness of e-learning systems. For the TAM, Perceived Usefulness and Perceived Ease of Use both have a positive impact on Attitude toward Use, and Attitude has a positive relationship with the Behavioral Intention of students. In addition, the factors from the TPB model (i.e., Perceived Behavioral Control and Subjective Norm) were identified as having a significant positive effect on Behavioral Intention to use e-learning. Recommendations for Practitioners: Firstly, educational institutions should help along with the culture of using e-learning among students and lecturers. A supportive team should be accessible to help students use e-learning by providing instructions and addressing their questions. Secondly, system developers should concentrate on system-related aspects that have a significant influence on learners’ attitudes and intentions to utilize, as well as build the most appropriate e-learning system for students. Recommendation for Researchers: Firstly, the study fulfills a significant literature gap on evaluating e-learning effectiveness for learners in private institutions as they are focusing on developing quality education to gain competitive advantages. Secondly, based on research findings, the researchers may be able to advance studies to improve and innovate a quality system for ensuring the long-term usage of e-learning. Finally, this paper contributes to the theoretical foundation and development of an extended model for future studies to assess the intention when employing new technologies in education and other fields. Impact on Society: E-learning will become a necessary tool and an unavoidable possibility in the next period of education. Therefore, this study presents an overview of the factors that have a notable influence on students’ intention to adopt e-learning systems. This study then proposes to develop an optimal system for the teaching and learning process, as well as to adapt to future demands. Future Research: Firstly, there are just three external variables that are considered to have an impact on learners’ intention via TAM. However, other external factors could be exploited in future research. Secondly, the participants in this study are only students. If the lecturers could take part in this survey, the comparisons between faculty and students may have more usefulness for assessment. Thirdly, this model just interprets the results at a certain time, which is the COVID-19 outbreak and e-learning is an urgent response to maintain the process of teaching and learning. The perception, attitude, and performance of students may change over time. Therefore, as other researchers have recommended, longitudinal surveys should be considered here. Finally, the differences between majors may appear. Future studies can divide groups of learners according to their majors for a more significant test.

Requirements engineering is often the first stage in the software process to understand the problem statement. Finding mistakes earlier in requirements helps reduce the development cost. One activity contributing to defining clear, complete and precise requirements is classifying requirement items in the specification. This paper presents a classification approach of functional and non-functional requirementsin Vietnamese using different supervised machine learning techniques. Five supervised machine learning algorithms, including Na¨ıve Bayes (NB), Support Vector Machine (SVM), Logistics Regression (LR), Multi-layer Perceptron Neural Network (MLP), and FastText, are implemented, trained, tested and compared using a dataset. The experimental results show that NB is the best model in terms of accuracy.

4The negative environmental impacts of globalization have given birth to the concept of green entrepreneurship, which might still be absent in many courses in higher education. Furthermore, despite its prominent role, intention does not translate into behaviour that makes entrepreneurial activities happen. Therefore, green entrepreneurial behaviour in students plays an essential part in helping develop and enhance green entrepreneurship. This study tries to investigate the factors affecting the green entrepreneurial behaviour of students. An online questionnaire assessing the impact of 5 factors was distributed to 157 students from FPT University Da Nang in Vietnam, who have had experience studying any course related to entrepreneurship in the school. The results show the relations and correlations between five important factors and Green entrepreneurial behaviour. These findings could contribute to the literature relating to Green entrepreneurial behaviour and help educators in making decisions for green entrepreneurial development in higher school. Index Terms4Green entrepreneurial behaviour, University entrepreneurial support, Green entrepreneurial intention.