The COVID-19 pandemic significantly impacted medical education, causing a shift towards online learning. However, this transition posed challenges in administering online assessments, particularly in proctoring and detecting academic misconduct. This study aimed to investigate the prevalence of academic misconduct among medical students during remote online examinations using hierarchical clustering and comparing self-reported confessions and recorded video reviews. The results of the study confirmed the existence of academic misconduct in remote online examinations, as evidenced by both self-report and video footage review. (...) Hierarchical clustering was able to predict academic misconduct with an accuracy rate of approximately 55%. These findings highlight the need for medical educators to develop and implement ethics and morality-focused courses to address the issues of dishonesty among medical students in online assessments. In conclusion, this study shed light on the challenges of remote online assessments in medical education, particularly in detecting academic misconduct. Using a data-driven approach to analyse the prevalence of academic misconduct, this work provided insights into the need for ethical and moral education in medical curricula, which can help address the dishonesty among medical students during online assessments. (shrink)

This paper proposes a two-level metaheuristic consisting of lower- and upper-level algorithms for the job-shop scheduling problem with multipurpose machines. The lower-level algorithm is a local search algorithm used for finding an optimal solution. The upper-level algorithm is a population-based metaheuristic used to control the lower-level algorithm’s input parameters. With the upper-level algorithm, the lower-level algorithm can reach its best performance on every problem instance. Most changes of the proposed two-level metaheuristic from its original variants are in the lower-level algorithm. (...) A main purpose of these changes is to increase diversity into solution neighborhood structures. One of the changes is that the neighbor operators of the proposed lower-level algorithm are developed to be more adjustable. Another change is that the roulette-wheel technique is applied for selecting a neighbor operator and for generating a perturbation operator. In addition, the proposed lower-level algorithm uses an adjustable delay-time limit to select an optional machine for each operation. The performance of the proposed two-level metaheuristic was evaluated on well-known benchmark instances. The evaluation’s results indicated that the proposed two-level metaheuristic performs well on most benchmark instances. (shrink)