Education plays a very significant role in the context of sustainability. As the world population is growing, providing education through the traditional classroom setting is not sufficient and not feasible to extend learning in professional life. Therefore, modern technology-mediated learning paradigms such as mobile learning are becoming increasingly popular. Mobile learning is said to integrate multiple contexts, learning types, mobilities and communications. As information and communications technology (ICT) plays a vital role in the delivery of mobile learning services, it is very essential to adopt sustainable IT resources to keep it viable. Cloud computing offers a range of affordable, scalable and on-demand solutions. This paper attempts to model important critical success factors (CSFs) in the area of cloud-based mobile learning using the interpretive structural modeling (ISM) technique. ISM helps in identifying the hierarchical inter-relationships between the variables of study with the help of experts in the field. Finally, Matrice d’Impacts Croisés-Multiplication Appliquée á un Classement (MICMAC) analysis is employed to classify the variables into dependent and independent variables. Management support has been identified as most rudimentary among sixteen CSFs identified through a literature review to establish a distinguished relative advantage. Further, the paper discusses the theoretical underpinning of all the constructs. This study will help organizations to implement mobile learning in sustainable ways.

Coronavirus disease 2019 (COVID-19 pandemic has affected the psychological health of people, causing a higher level of stress. Stress can exaggerate the symptoms of irritable bowel syndrome (IBS). To assess the effect of the COVID-19 pandemic stress on patients with IBS in Saudi Arabia. A descriptive cross-sectional approach was used, which targeted accessible subjects with IBS from different regions of Saudi Arabia. Data were collected from participants using a structured electronic questionnaire, which captured the participants’ socio-demographic data, medical history, IBS clinical data, self-reported stress due to COVID-19, and its effect on IBS symptoms. A total of 1255 IBS patients completed the questionnaire. About 63.4% of them reported stress due to the pandemic. The most frequently reported causes of stress were fear of infection occurring in the family, followed by fear of self-infection (43.5%), and death due to COVID-19 infection (17.2%). Most of the stressed participants (56.6%) reported that stress usually exaggerated IBS symptoms. Almost 22% of them consulted a physician for stress aggravation of the symptoms, 18.1% used sedatives due to stress, 9.2% modified IBS medications due to the stress, and 75.5% of the participants reported impaired daily activities due to symptoms exacerbation. Coexisting chronic morbidities and inability to differentiate between COVID-19 gastrointestinal symptoms and IBS symptoms were significantly associated with COVID-19 related stress ( P = .039 and .001, respectively). Two-thirds of IBS patients were stressed during the first few months of COVID-19 pandemic. Patients unable to differentiate between COVID-19 gastrointestinal tract symptoms and IBS symptoms, and patients suffering from chronic morbidities were more vulnerable. Pandemic stress exacerbated patients’ symptoms and impacted their activities of daily life.

Data imbalance with respect to the class labels has been recognised as a challenging problem for machine learning techniques as it has a direct impact on the classification model’s performance. In an imbalanced dataset, most of the instances belong to one class, while far fewer instances are associated with the remaining classes. Most of the machine learning algorithms tend to favour the majority class and ignore the minority classes leading to classification models being generated that cannot be generalised. This paper investigates the problem of class imbalance for a medical application related to autism spectrum disorder (ASD) screening to identify the ideal data resampling method that can stabilise classification performance. To achieve the aim, experimental analyses to measure the performance of different oversampling and under-sampling techniques have been conducted on a real imbalanced ASD dataset related to adults. The results produced by multiple classifiers on the considered datasets showed superiority in terms of specificity, sensitivity, and precision, among others, when adopting oversampling techniques in the pre-processing phase.

this is the extended research work where we tried to add more sections in Quality instruments to their role in achieving interoperability in e-learning and quality. In this paper we included more reference models such as experiences of Quality instruments affected by demographic criteria. In Higher education systems E-Learning faces two major challenges, first to ensure the interoperability of E-Learning (IEL) and secondly, developing quality learning through e-Learning. This research studies the concept, scope and dimensions of interoperability of E-Learning in education with special reference to King Khalid University then the connection and interdependence between with quality development. To impart learning and teaching through E-learning, KKU has adopted Learning Management Services (LMS) through Blackboard. The university has three types of learning and teaching methods; full online, Blended and Supportive. In this paper we have described the dimensions of quality and the standards of E- Learning for the objectives of IEL and quality development (QD) in KKU. The research is based principally on secondary data observed from KKU E-Learning deanship. Also sample of 20 E-Learning experts at KKU were given closed ended as well as semi closed questionnaires for evaluating the assurance of IEL and QD. These experts are mainly certified online facilitators and admin staff. Results provide the verification of application and presence of IEL and assured the QD process in KKU in imparting knowledge.

Purpose: To examine student perceptions towards the flipped classroom approach and its impact on their learning and their course evaluation  when compared to the traditional classroom method.Methods: Five classes of the pharmacoepidemiology course were delivered using the flipped classroom approach. Student perception towards the flipped teaching method was measured using a satisfaction survey. Measuring the impact of the flipped classroom on student learning and the  student course evaluation was achieved by comparing the midterm grades and the results of the standard endof- course evaluations with the previous semester's cohort.Results: Students’ perceptions of the flipped classroom were mostly favourable. The course and its various components were viewed more favourably in the second semester than in the first semester. Statistically significant improvements were observed in the perception of the topics covered in the course (p = 0.045), fairness of the grade assessment (p = 0.004), and perception of course feedback (p = 0.021). No statistical  difference was noted between the midterm examination scores of the first semester cohort (24.53 ± 3.80) and the second semester cohort (25.15 ± 3.00); [t (22.54) = 0.53, p =0.3].Conclusion: This study demonstrates that using the flipped classroom approach for teaching pharmacoepidemiology can improve student satisfaction, as well as maintain their academic performance. Keywords: Flipped classroom, Pharmacy education, Blended learning