Table 1 Method used and findings of previous studies

T-1

• Amir et al. (2020)

• Battisti et al. (2022)

• Dong et al. (2020)

• Plakhotnik et al. (2021)

• Online survey data were analyzed using descriptive study, logistic regression analysis and Cronbach’s alpha for reliability test

• Online survey data were analyzed using Friedman’s ANOVAs, Post hoc using Wilcoxon signed-rank tests and Cohens' applied to measure effect size

• Online survey data were analyzed using descriptive study (mean and standard deviation)

• Survey data were analyzed using descriptive study; a Chi-square test was conducted

• Majorities of the students preferred blended learning with distance and face-to-face learning, and first-year students react differently than senior students

• The girls perceived more stress from online lessons and use of devices than boys of special educational needs students

• Parents think that virtual interaction between teachers and students, or other related parties helps maintain social distancing

• University community and instructors play a significant role in building the relationship between the impact of COVID-19 and students’ program completion, job prospects and well-being

T-2

• Plakhotnik et al. (2021)

• Wang et al. (2020); Kohls et al. (2020)

• Lee et al. (2021)

• Leal et al. (2021)

• Bashir et al. (2021)

• Survey data were analyzed using descriptive study; Chi-square test was conducted

• Survey data were analyzed using descriptive study (mean and SD), t-test and Pearson’s correlation analysis. Data were collected from an online cross-sectional study and analyzed using ANOVA and asymptotic Kruskal–Wallis H-test

• Written survey responses and interview transcripts were used to collect data and were analyzed using a double-coded phenomenological analysis

• Conducted online survey and use a non-probability sampling and analyzed using descriptive statistics and inferential statistics

• Data were collected by online survey with open and closed questions and analyzed the postcode data using Teaching Excellence and Student Outcomes Framework (TEF) metrics; Participation of Local Areas (POLAR) and Indices of Multiple Deprivation (IMD)

• Students’ mental health and well-being significantly impact their engagement in learning activities and course completion

• The COVID-19 disruption significantly increased students’ levels of anxiety, depression, and drug and alcohol consumption, which decreased their well-being

• The frequency and severity of domestic violence due to COVID-19 closures impacted students’ physical health, which negatively influenced Indigenous and other marginalized students in Canada

• The COVID-19 pandemic reduces social interaction and communication, inversely affecting students' learning and university staff

• A hybrid delivery that students’ learning from home, and lockdown affect their mental well-being and quality of life

T-3

• Saleem et al. (2022)

• Rusli et al. (2020)

• Youngmann and Kushnirovich (2021)

• Indspire (2021)

• Data were analyzed using PROCESS Macro and Stepwise linear regression

• Online survey data were analyzed using descriptive statistics

• The national representative survey data were analyzed through confirmatory factor analysis (CFA) and root-mean-square error of approximation (RMSEA)

• Survey was analyzed using descriptive study (mean and standard deviation

• Motivational factors, instructors’ support, and university support predict the quality of online learning

• Students use desktop PC, laptops, smartphones and a combination of smartphones and laptops with the internet

• The ethnic minorities' insufficient resources during COVID-19 give a higher level of stress, affecting their emotional well-being than the majority populations

• Indigenous post-secondary students’ infrastructural challenges, including Internet access or space, lead to their mental and emotional negatively impacting their learning during the COVID-19 pandemic

T-4

• Pham et al. (2021)

• Baxter and Hainey (2023)

• The survey was conducted in the convenience sampling method; Cronbach’s Alpha was used for the reliability test, and the Bayesian Exploratory Factor Analysis (BEFA) was performed

• A case study methodology was adopted in this study

• The learner characteristics, perceived usefulness, course design, course materials, ease of use, and instructors’ teaching capacity affect online learning outcomes

• University students view remote learning as beneficial for instant feedback and fostering a community learning practice but prefer face-to-face learning

T-5

• Laksana’s (2020)

• Plakhotnik et al. (2021)

• Zhao et al. (2022)

• Survey data were analyzed using descriptive statistics

• Online survey data were analyzed using descriptive analysis and Chi-square test

• The survey data were analyzed through confirmatory factor analysis (CFA) and structural equation modeling (SEM)

• The administration of online delivery, including online learning infrastructure, teaching skills, academic interaction, learning support and learning expectations affect online delivery in minimal internet access areas

• The university community and instructors play a role in building the relationship between the impact of COVID-19 on students’ degree completion and well-being

• A positive correlation between university students’ learning satisfaction and students’ support services such as management, cognitive, and emotional support during COVID-19