This paper endeavors to ascertain the prevailing classroom management styles of instructors as well as the prevailing instructor-student relationship in a Philippine higher education institution. It   employed mixed methods of quantitative and qualitative methods of research to investigate the line of inquiry. A total of thirty faculty members and three-hundred students sampled from the population were the sources of data for the quantitative component of the study while eighteen students were selected as informants for the FGD to gather qualitative data. Findings showed that a great deal of authoritative classroom management style is being adhered by instructors while the students manifested a moderate level of connectedness and anxiety towards the classroom management styles of their instructors. Variables such as civil status, years of teaching experience, and level of educational attainment spelled differences on the classroom management styles instructors. It was uncovered that the different classroom management styles of instructors were attached with positive and negative labels and typifications. Implications of this study will serve as a reference to better prepare classroom managers of 21st-century college classrooms.

Because of the Coronavirus Disease 2019 (COVID-19) outbreak, most universities were forced to choose Online Distance Learning (ODL). The study aimed to examine the response of university students to the new situation. A questionnaire was sent to the entire university student population. Based on responses from 606 students, it was revealed that use of all applications in ODL increased. However, only the use of MS Teams increased significantly, while the use of the other applications (email, Moodle, e-textbooks) increased in a range of low to medium in terms of effect sizes, and even nonsignificant for applications such as Padlet and Kahoot. Based on the replies of 414 respondents, a Model of Forced Distance Online Learning Preferences (MoFDOLP) based on Structural Equation Modeling was developed. With a chosen combination of predictors, we succeeded in predicting 95% of variance for Satisfaction, more than 50% for Continuance Preferences variance in MS Teams applications, and nearly 20% in the case of e-materials. Among hypothesized constructs, only Attitudes are a strong predictor of Satisfaction, while Organizational Support, Perceived Ease of Use and Learner Attitude toward Online Learning are not. Satisfaction is a good predictor of Continuance Preferences to use Information Technology after the lockdown ended.

During the Coronavirus disease (COVID-19) pandemic, universities were obligated to transform from traditional classroom teaching environments to virtual ones. This sudden transformation highlighted the issue of low e-learning system usage amongst instructors of humanity faculties in Jordan's public universities. This study empirically investigated the moderating impact of technology anxiety on the relationship between the instructor's self-efficacy and the e-learning system's actual use to contribute to solving the problem. A survey was distributed to 468 instructors to test the theoretical framework, which yielded 285 valid and complete instruments analyzed using Structural Equation Modeling (SEM). Results showed the positive direct effect of self-efficacy on actual use. Furthermore, the level of technology anxiety moderated the relationship. Instructors with low technology anxiety showed a higher level of e-learning system actual usage than those with a high anxiety level. To improve the use of e-learning systems, top management must understand the fundamental role of compulsory training as the Covid-19 pandemic, and the instructor's confidence must be increased by providing training and support.

Online learning during the Coronavirus disease (COVID-19) pandemic has awakened and affirmed the necessity of learning based on digital technology. The article was aimed to analyze the effectiveness of online learning at bachelor’s, master’s, and doctoral degrees of Islamic Religious Education as a reference to develop a learning pattern post-COVID-19 pandemic. The research employed a mixed-method design with a concurrent triangulation model. The samples were taken using stratified random and purposive sampling. Meanwhile, the data were collected through questionnaires, in-depth interviews, and forum group discussion. A descriptive analysis and one-way analysis of variance were used to analyze the quantitative data, while interpretative descriptive for the qualitative data. The research showed that online learning during the COVID-19 pandemic at the bachelor’s, master’s, and doctoral degrees of Islamic Religious Education have been effective. In detail, online learning at the doctoral degree was the most effective among all. On the other hand, face-to-face learning is still necessary. Therefore, the learning pattern developed post-COVID-19 pandemic combines face-to-face and online learning (hybrid learning). The formulation is adjusted to the characteristics, educational purpose and orientation, level of ability, readiness, and learning autonomy of the students at each educational level.

Online learning is an obligation in teaching and learning activities during the Coronavirus disease (COVID-19). Game-based learning is a solution in improving student learning outcomes. This research aims to determine the level of acceptance of gamification in terms of Gamification quality (GQ), instructor characteristic (IC), and technology anxiety (TA). The target respondents were students taking information systems courses based on enterprises resources planning (ERP) Gamification. The sample used is a census. That is, the entire population is taken as a sample. A total of 93 students filled out the online questionnaire. Then, data analysis using Structural Equation Model - Partial Least Square (SEM-PLS). Student satisfaction (SS) and perceived ease of use (PEOUG) are the most influences. PEOUG is also the construct that has the most significant relationship impact, especially with the perceived usefulness (PUG). Meanwhile, two constructs do not significantly impact TA on PUG and PUG on Intention to use gamification (INTG). The obligation of students requires students to ignore the impact and function of gamification. The results of this research also show that technology acceptance model (TAM), the constructs IC, TA, and GQ have a positive effect on PEOUG. Then PUG and PEOUG can positively affect SS.

Vietnam has a reputation for being a successful nation in preventing the Coronavirus disease (COVID-19) outbreak in 2020, with a lower number of illnesses than other ASEAN countries. However, to ensure that students are safe and informed about the coronavirus outbreak, Vietnamese higher education has developed online learning (OL). During the COVID-19 epidemic, this paper explores the relationship between elements such as learning readiness, learning strategies, and learning performance in the Vietnamese OL setting. Four hundred undergraduate students were randomly selected from Hong Duc universities, and Saigon University participated in this study in different zones. Analyzed data has applied structural equation modeling (SEM) using partial least squares (SmartPLS-SEM). The findings found that Vietnamese students were much more likely to believe in interaction in OL, to feel comfortable using a computer with their computer efficacy, and to have confidence in communicating in the digital environment, all of which were important variables in assuring the success of using OL. The factors of “motivation” and “test preparation” show a poor relationship with learning performance. Therefore, the OL process in Vietnamese, on the other hand, needs to be more inventive, with a greater focus on lecturers' awareness and practice of online teaching pedagogies such as motivation, techniques, and test arrangement. During OL, students' readiness in terms of learning control, self-directed learning, and engagement must be considered and supported.

This study aims to explain the success factors of e-learning. The participants were 427 students in public universities in Indonesia. To demonstrate the success of this e-learning, we developed a more comprehensive e-learning evaluation model that considers the system's characteristics, students, and instructors. The results show that higher student performance is associated with higher student satisfaction. However, the increase in performance is not due to the use of e-learning. Social and cultural factors influence the use of e-learning. Culture and social environment influence students' use of e-learning. The instructor's ability to implement e-learning has been shown to influence student satisfaction. The difference in the implementation of e-learning compared to classroom learning requires different teaching methods that affect student performance. In addition, e-learning is used in all courses during the COVID-19 pandemic.

This study aimed to investigate the relationship between teacher professional development, quality of lecture design, student engagement, teacher technical skills, pedagogical content knowledge and teacher satisfaction in using Artificial Intelligence (AI)-Powered Facilitator for designing lectures. The study used a non-random sample technique, and 208 participants answered a survey via Google Form after one semester, using a 5-point Likert scale to rate their responses. The structural equation model was used to analyze the data, and six factors were included in the study. The study confirmed hypotheses that teacher professional development, quality of lecture design, student engagement, and pedagogical content knowledge have a positive effect on teacher satisfaction. However, the study also revealed that teacher technical skills have a negative effect on teacher satisfaction, and pedagogical content knowledge has no significant effect. The proposed conceptual model explained 55.7% of the variance in teacher satisfaction Theoretical and practical implications were also discussed. These findings provide insights into the factors that contribute to teacher satisfaction in utilizing AI-Powered Facilitator for designing lectures and could inform the development of effective teacher training programs.