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Students pursuing different professional courses at the higher education level during 2021–2022 saw the first-time occurrence of a pandemic in the form of coronavirus disease 2019 (COVID-19), and their mental health was affected. Many works are available in the literature to assess mental health severity. However, it is necessary to identify the affected students early for effective treatment.

Predictive analytics, a part of machine learning (ML), helps with early identification based on mental health severity levels to aid clinical psychologists. As a case study, engineering and medical course students were comparatively analysed in this work as they have rich course content and a stricter evaluation process than other streams. The methodology includes an online survey that obtains demographic details, academic qualifications, family details, etc. and anxiety and depression questions using the Hospital Anxiety and Depression Scale (HADS). The responses acquired through social media networks are analysed using ML algorithms – support vector machines (SVMs) (robust handling of health information) and J48 decision tree (DT) (interpretability/comprehensibility). Also, random forest is used to identify the predictors for anxiety and depression.

The results show that the support vector classifier produces outperforming results with classification accuracy of 100%, 1.0 precision and 1.0 recall, followed by the J48 DT classifier with 96%. It was found that medical students are affected by anxiety and depression marginally more when compared with engineering students.

The entire work is dependent on the social media-displayed online questionnaire, and the participants were not met in person. This indicates that the response rate could not be evaluated appropriately. Due to the medical restrictions imposed by COVID-19, which remain in effect in 2022, this is the only method found to collect primary data from college students. Additionally, students self-selected themselves to participate in this survey, which raises the possibility of selection bias.

The responses acquired through social media networks are analysed using ML algorithms. This will be a big support for understanding the mental issues of the students due to COVID-19 and can taking appropriate actions to rectify them. This will improve the quality of the learning process in higher education in Oman.

Furthermore, this study aims to provide recommendations for mental health screening as a regular practice in educational institutions to identify undetected students.

Comparing the mental health issues of two professional course students is the novelty of this work. This is needed because both studies require practical learning, long hours of work, etc.

When the repercussions of COVID-19 were being absorbed by the world, the higher educational institutions (HEIs) were conceiving of strategies to run educational institutions on remote basis too, including the challenges linked with the teaching–learning as well as the management and other departmental needs. Leaning itself on the teaching–learning tectonic shifts amid the COVID-19 climes, the study reviews the status of “readiness” of the HEIs in Oman while bearing this in mind that the usage of information and communications technology (ICT) for distance learning and conventional learning has been considered as one of the parameters to judge the “quality” of the HEIs in Oman.

Documentary analysis alongside an in-depth reading of the quality audit reports sourced from the Oman Academic Accreditation Authority, which are publicly accessible are being referred for driving home the arguments in the study.

The study concludes that despite the case being in favor of distance learning from early on, the same doesn’t get reflected in the conclusions derived in the research conducted for assessing the teaching–learning mechanisms during the COVID-19 period. Therefore, the HEIs’ “readiness” in terms of ensuring the smooth transitioning to the remote learning pedagogical arrangements for meeting the challenges of the COVID-19 wasn’t efficacious.

The present study may be followed up with an understanding as to how the HEIs of Oman need to incorporate the perspectives of all the concerned stakeholders for the refurbishment of the teaching–learning process, especially in times of contingencies.

Apart from the policy-makers, the management of the HEIs of Oman needs to appreciate the need to be proactive and appreciative of the inclusion of ICT tools and techniques in the mainstream pedagogical settings.

Notwithstanding the emphasis upon the preparedness and readiness for tackling the challenges posed by the COVID-19 for the HEIs in Oman, no study has attempted to delve into the issue succinctly—the present study fills this gap.

The purpose of this paper is to identify the factors affecting learners' perception of e-learning during the Coronavirus-2019 (Covid-19) pandemic. Furthermore, it investigates mechanisms that enhance students' engagement in e-learning, especially under the constraints created by the pandemic.

Quantitative research approach was used, and data were collected using a structured questionnaire. The sample consisted of 163 undergraduates registered at the Faculty of Management Studies of the Open University of Sri Lanka (OUSL). The structured questionnaire was designed taking into consideration learners attached to different regional centers and study centers of the OUSL.

As per the findings, performance expectancy, social influence, effort expectancy and service quality are the factors which have significant effects on students' “intention to use” e-learning as a method of pursuing education. Furthermore, it was observed that 65.6% of the students had access to technology through mobile phones, while 53.4% of the students had engaged in e-learning for the first time. This was mainly due to restrictions that were imposed during the Covid-19 pandemic.

The study will help in formulating policies and introducing procedures in relation to online teaching-learning models to be used by both teachers and learners, especially in similar pandemic situations in the future.

This study will assist to determine the effectiveness of the e-learning system used by the OUSL. The findings highlighted the importance of improving information technology (IT) facilities available at all the regional and study centers across Sri Lanka.

The current study has a two-fold purpose. Firstly, it aims to analyze the extent to which knowledge management (KM) affects the performance of individuals (task and contextual) on the one hand and that of organizations (product or service, perceived and financial) on the other hand. Secondly, it proposes to investigate the mediating effect of motivation and innovation in the relationship between KM and individual and organizational performance.

Partial least squares structural equation modeling (PLS-SEM) was employed in this study, with mediation analysis performed using advanced PLS-SEM techniques. A total of 1,284 respondents from organizations in both the public and private sectors were included in the sample.

The findings emphasize that KM has a more significant direct effect on individual performance compared to organizational performance. Concurrently, in terms of indirect influence, it is found that KM, through motivation and innovation, has a positive and significant effect on both individual and organizational performances, with a higher influence on the organizational one.

The originality of the work can be noted in designing two different structural models to represent the proposed relationships at the individual and organizational levels. These findings could provide organizational decision makers with empirical evidence, helping them (1) internalize the significance of the KM process in organizations as well as its subsequent effects on individual and organizational performance and (2) identify factors that mediate variable relationships.

This paper aims to investigate the factors influencing chief executive officers’ (CEOs') intentions to implement cloud technology in Italian small and medium-sized enterprises (SMEs).

The study proposes a model that integrates the theoretical construct of the technology acceptance model (TAM) with a classification of perceived benefits and risks related to cloud computing. The study employs a structural equation modeling approach to analyze data gathered through a Likert scale-based survey.

The findings indicate that risk perception has a strong negative effect on the intention to introduce cloud technology in firms. This effect is partially offset by the perceived ease of use of the technology.

The study provides a new theoretical framework that integrates the TAM and a classification of perceived risks to provide a clear view of management's cognitive processes during technological change. Moreover, the results show the main factors influencing decisions regarding the implementation of cloud computing in firms in light of the perception of risks. Finally, this study provides interesting findings for cloud service providers (CSPs) about their customers' decision-making processes.