The impact of gamification on learning and instruction: A systematic review of empirical evidence

In today's organisations and politics, there is a growing awareness of the gap between existing and needed digital competencies of the workforce to master the challenges of the digitalised future at work. Nevertheless, no comprehensive framework or definition of digital competencies at work has been proposed so far. Our aim is to offer a holistic view and broaden the scope of the concept of digital competencies, thereby focussing on applications at work. We combine diverse methods to integrate different perspectives on digital competencies. By conducting an extensive literature review about definitions and frameworks of digital competencies that might be applicable at work, we provide an overview of the current state of the art in research on digital competencies. Additionally, eleven half-structured interviews based on the critical incidents technique (CIT) were conducted to gain insights into the perspectives of professionals with expertise in digitalisation processes and digital competencies. Subsequently, researchers with different educational backgrounds clustered the results from both approaches and agreed on twenty-five dimensions that constitute digital competencies for white-collar workers with office jobs, encompassing a large variety of knowledge, skills, and abilities. The results of this research indicate that even though there is overlapping content, each perspective adds unique content to the concept of digital competencies at work. By creating a coherent and detailed framework and a definition, our research enhances the applicability of professional learning and development of digital competencies at work.

Digital competencies: A review of the literature and applications in the workplace

E-government adoption in sub-Saharan Africa

Automated virtual reality exposure therapy for spider phobia vs. in-vivo one-session treatment : A randomized non-inferiority trial

Image segmentation is among the most important techniques in image processing, and many methods have been developed to perform this task. This paper presents a new multi-objective metaheuristic based on a multi-verse optimization algorithm to segment grayscale images via multi-level thresholding. The proposed approach involves finding an approximate Pareto-optimal set by maximizing the Kapur and Otsu objective functions. Both Kapur’s and Otsu’s methods are highly used for image segmentation performed by means of bi-level and multi-level thresholding. However, each of them has certain characteristics and limitations. Several metaheuristic approaches have been proposed in the literature to separately optimize these objective functions in terms of accuracy, whereas only a few multi-objective approaches have explored the benefits of the joint use of Kapur and Otsu’s methods. However, the computational cost of Kapur and Otsu is high and their accuracy needs to be improved. The proposed method, called Multi-objective Multi-verse Optimization, avoids these limitations. It was tested using 11 natural grayscale images and its performance was compared against three of well-known multi-objective algorithms. The results were analyzed based on two sets of measures, one to assess the performance of the proposed method as a multi-objective algorithm, and the other to evaluate the accuracy of the segmented images. The results showed that the proposed method provides a better approximation to the optimal Pareto Front than the other algorithms in terms of hypervolume and spacing. Moreover, the quality of its segmented image is better than those of the other methods in terms of uniformity measures.

Multi-level thresholding-based grey scale image segmentation using multi-objective multi-verse optimizer

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