The COVID-19 pandemic has impelled the majority of schools and universities around the world to switch to remote teaching. One of the greatest challenges in online education is preserving the academic integrity of student assessments. The lack of direct supervision by instructors during final examinations poses a significant risk of academic misconduct. In this paper, we propose a new approach to detecting potential cases of cheating on the final exam using machine learning techniques. We treat the issue of identifying the potential cases of cheating as an outlier detection problem. We use students’ continuous assessment results to identify abnormal scores on the final exam. However, unlike a standard outlier detection task in machine learning, the student assessment data requires us to consider its sequential nature. We address this issue by applying recurrent neural networks together with anomaly detection algorithms. Numerical experiments on a range of datasets show that the proposed method achieves a remarkably high level of accuracy in detecting cases of cheating on the exam. We believe that the proposed method would be an effective tool for academics and administrators interested in preserving the academic integrity of course assessments.

This work employs machine learning methods to develop and test a technique for dynamic stability analysis of the mathematical model of a power system. A distinctive feature of the proposed method is the absence of a priori parameters of the power system model. Thus, the adaptability of the dynamic stability assessment is achieved. The selected research topic relates to the issue of changing the structure and parameters of modern power systems. The key features of modern power systems include the following: decreased total inertia caused by integration of renewable sources energy, stricter requirements for emergency control accuracy, highly digitized operation and control of power systems, and high volumes of data that describe power system operation. Arranging emergency control in these new conditions is one of the prominent problems in modern power systems. In this study, the emergency control algorithms based on ensemble machine learning algorithms (XGBoost and Random Forest) were developed for a low-inertia power system. Transient stability of a power system was analyzed as the base function. Features of transmission line maintenance were used to increase accuracy of estimation. Algorithms were tested using the test power system IEEE39. In the case of the test sample, accuracy of instability classification for XGBoost was 91.5%, while that for Random Forest was 81.6%. The accuracy of algorithms increased by 10.9% and 1.5%, respectively, when the topology of the power system was taken into account.

Purpose – The purpose of this study is to examine students' perception of end-of-semester Student Evaluations of Teaching (SET), and to explore the extent to which SET are biased by non-instructional factors. Design/methodology/approach – A survey questionnaire about the end-of-semester SET was designed and administered to 819 students selected from a random list of summer classes at the American University of Sharjah in the United Arab Emirates. Appropriate statistical analysis methods of the resulting data were performed. Findings – The results of this study show that significant differences exist among the various demographic groups with respect to both students' perceptions of the evaluation process and their tendency to be biased by a number of non-instructional factors. The study has presented evidence on how students' cultural and linguistic backgrounds affect their responses to SET. Practical implications – This paper provides useful information for the academic community concerning the validity and reliability of SET rating scales used in US universities abroad and whether the data obtained from such rating scales should be used for administrative and personnel decisions. In addition, teachers should examine SET assessments with care before undertaking modifications to their teaching practices. Originality/value – This paper was the first to examine SET in a US university overseas, where the majority of students are non-native speakers of the English language, and of an Arab origin. The findings illuminate the importance of understanding the cultural and linguistic contexts of the institution in which SET are conducted.

Objectives: To identify the type and frequency of services provided through community pharmacies in the United Arab Emirates (UAE). Methods: A survey was conducted using an anonymous questionnaire distributed by hand to 700 community pharmacies. Items included information about the pharmacists and pharmacies, type of products sold, type and extent of enhanced services provided and perceived barriers to providing these services. Key findings: Most pharmacies provided a wide range of medicinal and non-medicinal products. The frequency with which services were provided was assessed on a scale of 1 (never) to 5 (always). Enhanced professional services were not provided to a large extent in most pharmacies. Fewer than one-third (29%) reported they always supplied printed information to patients (mean = 3.37, 95% confidence interval = 3.23–3.52); fewer than one-third (28%) counselled patients on a regular basis (3.25, 3.09–3.40); nearly two-thirds (62%) reported monitoring patients' adherence to therapy at least sometimes (2.96, 2.81–3.10). Most pharmacies (92%) in the UAE did not routinely keep patient records (2.09, 1.96–2.32). While just over a quarter of respondents claimed that they always reported medication errors (27%) and adverse drug reactions (28%), these activities were not often performed in around 40% of pharmacies. Conclusions: This is the first study to explore the type and extent of professional services provided through community pharmacies in the UAE and provides baseline data critical to inform the development of strategies to improve the quality of community pharmacy services.