Predicting Certification in MOOCs based on Students’ Weekly Activities

Massive Open Online Courses (MOOCs) have been growing rapidly, offering low-cost knowledge for both learners and content providers. However, currently there is a very low level of course purchasing (less than 1% of the total number of enrolled students on a given online course opt to purchase its certificate). This can impact seriously the business model of MOOCs. Nevertheless, MOOC research on learners’ purchasing behaviour on MOOCs remains limited. Thus, the umbrella question that this work tackles is if learner’s data can predict their purchasing decision (certification). Our fine-grained analysis attempts to uncover the latent correlation between learner activities and their decision to purchase. We used a relatively large dataset of 5 courses of 23 runs obtained from the less studied MOOC platform of FutureLearn to: (1) statistically compare the activities of non-paying learners with course purchasers, (2) predict course certification using different classifiers, optimising for this naturally strongly imbalanced dataset. Our results show that learner activities are good predictors of course purchasibility; still, the main challenge was that of early prediction. Using only student number of step accesses, attempts, correct and wrong answers, our model achieve promising accuracies, ranging between 0.81 and 0.95 across the five courses. The outcomes of this study are expected to help design future courses and predict the profitability of future runs; it may also help determine what personalisation features could be provided to increase MOOC revenu

Distribution of influenza A and B antibodies and correlation with ABO/Rh blood grouping

Background: Influenza is a clinically-significant infection with significant number of globally reported annual deaths. The aim of this study was to study the distribution of influenza A and B antibodies in Najran, the Southwest region of Saudi Arabia, and to investigate the correlation between demographic characteristics and influenza virus antibody levels.Methods: Enzyme linked immunosorbent assay was used to detect antibody level of influenza A and B. The correlation with ABO/Rh blood groupings was also examined. The total number of participants was 252. Only twenty-four subjects received the flu vaccine.Results: It was found that 33.7% and 24.1% of unvaccinated subjects were IgG-positive for influenza A and B, respectively. Interestingly, the antibody levels of the unvaccinated participants were higher than the vaccinated group. A significant difference was found between unvaccinated participants with O+ and influenza A and B antibody levels (**p=0.0045). The antibody level was inversely correlated with age in influenza B IgG subjects but not influenza A IgG (r=-0.1379; R squared=0.01900; p=0.0375). Forty-three subjects (17%) were positive for antibodies of both influenza A and B.Conclusions: IgG antibody positivity is greater in cases of influenza type A compared to influenza B. A significant correlation was found in the unvaccinated group between influenza B IgG antibody levels and age, but not influenza A (*p=0.0375). More research is needed to investigate the role of O+ blood group in influenza infections

Efficient spectral element methods for partial differential equations

In this thesis we applied a spectral element approximation to some elliptic partial differential equations. We demonstrated the difficulties related to the approximation of a discontinuous function in which the discontinuity is not fitted to the computational mesh. Such a situation gives rise to the Gibbs phenomenon. A h− p spectral element equivalent of the eXtended Finite Element Method (XFEM), which we termed the eXtended Spectral Element Method (XSEM) was developed. This was applied to some model problems. XSEM removes some of the oscillations caused by Gibbs phenomenon. We then explained that when approximating a discontinuous function, XSEM is able to capture the discontinuity precisely. We derive spectral element error estimates. The convergence of the approximations is studied. We have introduced several enrichment functions with the purpose of improving the approximation of discontinuous functions. In particular we have considered the twodimensional Poisson equation. Unfortunately, this implementation of XSEM was not able to recover spectral convergence. An alternative idea in which the discontinuity is constrained within a spectral element produces accurate SEM approximation. The Stokes problem was considered and solved using SEM coupled with an iterative PCG method. The zero volume condition on the pressure is satisfied identicaly using an alternative formulation of the continuity equation. Furthermore, we investigated the dependence of the accurency of the spectral element approximation on the weighting factor as well as the convergence properties of the preconditioner. An efficient and robust preconditioner is constructed for the Stokes problem. Exponential convergence was attained

Optimal control analysis of Monkeypox disease with the impact of environmental transmission

Monkeypox is an emerging zoonotic viral disease resembling that of smallpox, although it is clinically less severe. Following the COVID-19 outbreak, monkeypox is an additional global health concern. The present study aims to formulate a novel mathematical model to examine various epidemiological aspects and to suggest optimized control strategies for the ongoing outbreak. The environmental viral concentration plays an important role in disease incidence. Therefore, in this study, we consider the impact of the environmental viral concentration on disease dynamics and control. The model is first constructed with constant control measures.The basic mathematical properties including equilibria, stability, and reproduction number of the monkeypox model are presented. Furthermore, using the nonlinear least square method, we estimate the model parameters from the actual cases reported in the USA during a recent outbreak in 2022. Normalized sensitivity analysis is performed to develop the optimal control problem. Based on the sensitivity indices of the model parameters, the model is reformulated by introducing six control variables. Based on theoretical and simulation results, we conclude that considering all suggested control measures simultaneously is the effective and optimal strategy to curtail the infection. We believe that the outcomes of this study will be helpful in understanding the dynamics and prevention of upcoming monkeypox outbreaks

Towards Designing Profitable Courses: Predicting Student Purchasing Behaviour in MOOCs

Since their ‘official’ emergence in 2012 (Gardner and Brooks 2018), massive open online courses (MOOCs) have been growing rapidly. They offer low-cost education for both students and content providers; however, currently there is a very low level of course purchasing (less than 1% of the total number of enrolled students on a given online course opt to purchase its certificate). The most recent literature on MOOCs focuses on identifying factors that contribute to student success, completion level and engagement. One of the MOOC platforms’ ultimate targets is to become self-sustaining, enabling partners to create revenues and offset operating costs. Nevertheless, analysing learners’ purchasing behaviour on MOOCs remains limited. Thus, this study aims to predict students purchasing behaviour and therefore a MOOCs revenue, based on the rich array of activity clickstream and demographic data from learners. Specifically, we compare how several machine learning algorithms, namely RandomForest, GradientBoosting, AdaBoost and XGBoost can predict course purchasability using a large-scale data collection of 23 runs spread over 5 courses delivered by The University of Warwick between 2013 and 2017 via FutureLearn. We further identify the common representative predictive attributes that influence a learner’s certificate purchasing decisions. Our proposed model achieved promising accuracies, between 0.82 and 0.91, using only the time spent on each step. We further reached higher accuracy of 0.83 to 0.95, adding learner demographics (e.g. gender, age group, level of education, and country) which showed a considerable impact on the model’s performance. The outcomes of this study are expected to help design future courses and predict the profitability of future runs; it may also help determine what personalisation features could be provided to increase MOOC revenue

Bentall Procedure for an Adolescent with Sickle Cell Disease, Hodgkin’s Lymphoma, and old Cerebrovascular Accident

Cardiopulmonary bypass (CPB) in patients with sickle cell anemia can trigger lethal vaso-occlusive crises, especially in cases of hypoxia, hypothermia, acidosis, or low-flow states. We described a patient with sickle cell anemia who had bicuspid aortic valve stenosis and aneurysmal dilatation of the ascending aorta complicated with infective endocarditis. The patient had a history of stroke. During routine workup, Hodgkin’s Lymphoma was diagnosed. The patient underwent exchange transfusion preoperatively and immediately before the initiation of CPB. We performed a Bentall procedure, and the patient was discharged in a stable condition.  Sickle Cell Disease can be very challenging during CPB, and special precautions are required to prevent vaso-occlusive crises

The impact of usability, social and organisational factors on students' use of learning management systems in Saudi tertiary education

Advances in e-learning have reshaped universities worldwide. Universities place great emphasis on technology-enhanced learning development and are investing significantly in information technology infrastructure. However, in spite of this effort and investment, it seems that instructors and students do not fully benefit from learning technology, and more often Learning Management Systems (LMSs) remain underutilized. This is evident in Saudi higher education where LMSs have recently been introduced. Understanding the factors affecting the use of LMSs and prompting their engagement are therefore crucial to the success of such platforms. This study aims to fill this gap by examining usability, and organisational and social factors affecting the students’ intentions and use of LMSs in Saudi tertiary education. To this end, a theoretical framework was proposed that combined perceived usability attributes with the Unified Theory of Acceptance and Use of Technology (UTAUT) variables to identify the impact on students’ intention and use of the LMS. Furthermore, the study examined the moderating effect of demographic characteristics (gender, age, experience, and training) on the model’s proposed relationships. This study used a quantitative approach to validate the proposed model and test the research hypotheses. A cross-sectional survey method was adopted to collect the data. Using the probability multi-stage cluster-sampling technique, the empirical data were collected from five state universities in different regions of Saudi Arabia. The data were coded, cleaned, and preliminarily analysed using the Statistical Package for Social Science (SPSS) package. In total, 605 responses were usable for testing the measurement and structural model, employing partial least squares structural equation modelling (PLS-SEM) technique and SmartPLS software. The results reveal the significant drivers of student use of LMS and the moderating effect of demographics on the proposed relationships. The results confirm that the study model is valid and reliable to indicate the key factors that influence the use of LMS. The dimension of social influence emerged to significantly influence the students’ usage behaviour. The performance expectancy was affected by information quality and the system interactivity whereas the effort expectancy was influenced by system navigation, system learnability and instructional assessment. The statistical analysis reveals that six associations were moderated by the four proposed personal characteristics. In the light of the findings of this study, recommendations were put forward to universities to gain insights into the best way to promote e-learning system popularity and acceptance among students