The COVID-19 pandemic has created substantial challenges across higher education, requiring academic institutes to conduct all teaching, learning, and assessments remotely online. To obtain a stronger understanding of the opinions of teachers and students in Hong Kong during this period, an anonymous online survey was distributed to all teachers and students at a higher education institute. Overall, 48 teachers and 425 students responded to the survey. This paper specifically analyzes the responses to open-ended questions from the survey to identify key topics and concepts. Open coding was used to obtain key terms, which were further grouped into categories. Results indicated that higher education teachers and students had similar issues (e.g., access to digital devices and software) and benefits (e.g., flexible scheduling and time arrangements) regarding online education. However, some categories were reported as both issues and benefits by teachers and students (e.g., communication/interaction), demonstrating that individual circumstances, abilities, preferences, and experiences, may influence satisfaction levels. Furthermore, results indicated that the delivery of quality online education enforced during a pandemic requires a multi-factorial and tailored approach. The information presented in this study will help higher education institutes to develop, redefine, and re-conceptualize online learning initiatives. Copyright © 2021 The University of Hong Kong - Faculty of Education

Despite the wide adoption of emergency remote learning (ERL) in higher education during the COVID-19 pandemic, there is insufficient understanding of influencing factors predicting student satisfaction for this novel learning environment in crisis. The present study investigated important predictors in determining the satisfaction of undergraduate students (N = 425) from multiple departments in using ERL at a self-funded university in Hong Kong while Moodle and Microsoft Team are the key learning tools. By comparing the predictive accuracy between multiple regression and machine learning models before and after the use of random forest recursive feature elimination, all multiple regression, and machine learning models showed improved accuracy while the most accurate model was the elastic net regression with 65.2% explained variance. The results show only neutral (4.11 on a 7-point Likert scale) regarding the overall satisfaction score on ERL. Even majority of students are competent in technology and have no obvious issue in accessing learning devices or Wi-Fi, face-to-face learning is more preferable compared to ERL and this is found to be the most important predictor. Besides, the level of efforts made by instructors, the agreement on the appropriateness of the adjusted assessment methods, and the perception of online learning being well delivered are shown to be highly important in determining the satisfaction scores. The results suggest that the need of reviewing the quality and quantity of modified assessment accommodated for ERL and structured class delivery with the suitable amount of interactive learning according to the learning culture and program nature. Copyright © 2021 Public Library of Science.