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The global prominence of languages and Saudi Arabia’s Vision 2030, which supports the necessity of German proficiency for the nation’s socioeconomic evolution, necessitate a deeper understanding of German teaching in Saudi international schools. This study delves into the influence of various teaching strategies on students' German writing skills. The research particularly focuses on traditional and innovative methods and considers the factors that drive these teaching approaches.

Data were collected from 304 students in Riyadh, Saudi Arabia, through a questionnaire. The relationships between teaching strategies and students' German writing abilities were analyzed using regression techniques.

The results indicate that both traditional and innovative teaching strategies positively influence students' writing skills. The regression analysis shows that the independent variables (traditional teaching strategies, innovative teaching strategies and factors influencing teaching strategies) collectively account for 68.9% of the variation in students' German writing skills. Even though a variety of techniques influence students' academic performance, the study’s findings indicate that several strategies – such as self-evaluation, pair work, oral feedback, grammar instruction and translation – have a major impact on students' German writing abilities.

This research brings unique insights into the German teaching realm of Saudi international schools, emphasizing the harmony between Vision 2030 goals and effective teaching methodologies. It elucidates the considerable influence of both traditional and innovative strategies on student writing outcomes. For educators in Saudi Arabia’s international educational environment, the study’s findings underline the importance of adopting student-centric approaches in the writing process, ensuring students evolve as proficient writers. Additionally, the research underscores the significant role of variables affecting teaching strategies, spotlighting their pivotal role in shaping student outcomes.

The impact of education and research skills on the strategic digital transformation of education is straightforward. In this context, the Saudi National Institute of Health plays a pivotal role in the design and implementation of a resilient and robust strategy for the development of skills and competencies to young health professionals. In this chapter, the authors provide a brief overview of the Vision 2030 in Saudi Arabia and its basic priorities in the areas related to the Education and Research in the healthcare domain. The authors also elaborate on the key plans and initiatives undertaken by the education and research skills directory of the Saudi National Institute of Health (SNIH) towards transformative learning with impact on the implementation of the Vision 2030.

The aim of this study is to investigate the roles of psychiatric mental health nurses during their work experiences in inpatient clinical settings.

A focus group of 10 graduate psychiatric nurses with more than two years’ practice in inpatient psychiatric settings reflected on their last six months’ work placements and continuous employment. The transcripts and field notes were analyzed through thematic analysis of inductive data.

Two main themes emerged: management roles and clinical roles. The participants reflected on caring activities and obstacles encountered in fulfilling their professional roles.

Multiple practice issues emerged. The participants perceived that psychiatric nurse specialists are required to perform more caring functions than practicable in the inpatient setting due to an excess of noncaring duties, structural minimization of the caring role and inadequate training. They felt that many of the functions performed were not within their expectations of the caring role of a psychiatric nurse specialist and believed that changes in nurse education and attention to clarification of nurses’ roles might enhance the role they play in patient care.

The paper is aimed at identifying, analysing and prioritizing the critical constraints affecting efficient management of historic buildings in remote areas of Saudi Arabia.

Thematic content analysis and expert-based evaluation approaches were used to identify and evaluate the constraints. The DEMATEL technique was deployed to define the interrelationship complexities between the constraints and evaluate the impact of these interrelationships to ascertain the influential constraints.

The results identify “lack of clearly defined roles for the multiple government agencies” as the most influential constraint for managing historic sites and buildings in remote areas of Saudi Arabia.

The contextual interrelationship between the constraints is due to the experts' perceptions, which may be biased due to their proficiencies and professional backgrounds. Since the evidence on which the findings of this paper are established is predominantly from experiences related to historic sites and buildings in remote areas of Saudi Arabia, the results of this paper may not be entirely applicable elsewhere.

The paper provides invaluable methodology that can support practitioners and policymakers to establish sustainable strategies that can enhance the management and protection of historic buildings in remote areas of Saudi Arabia.

This study noticeably contributes to knowledge by providing comprehensive understanding of the major impediments to the successful management of historic buildings in remote areas of Saudi Arabia, which can assist in mitigating the potential impacts of these constraints and to advocate for the achievement of efficient management and protection of the historic sites and buildings.

Spam emails classification using data mining and machine learning approaches has enticed the researchers' attention duo to its obvious positive impact in protecting internet users. Several features can be used for creating data mining and machine learning based spam classification models. Yet, spammers know that the longer they will use the same set of features for tricking email users the more probably the anti-spam parties might develop tools for combating this kind of annoying email messages. Spammers, so, adapt by continuously reforming the group of features utilized for composing spam emails. For that reason, even though traditional classification methods possess sound classification results, they were ineffective for lifelong classification of spam emails duo to the fact that they might be prone to the so-called “Concept Drift”. In the current study, an enhanced model is proposed for ensuring lifelong spam classification model. For the evaluation purposes, the overall performance of the suggested model is contrasted against various other stream mining classification techniques. The results proved the success of the suggested model as a lifelong spam emails classification method.

Multi-view fuzzy clustering algorithms are not widely used in image segmentation, and many of these algorithms are lacking in robustness. The purpose of this paper is to investigate a new algorithm that can segment the image better and retain as much detailed information about the image as possible when segmenting noisy images.

The authors present a novel multi-view fuzzy c-means (FCM) clustering algorithm that includes an automatic view-weight learning mechanism. Firstly, this algorithm introduces a view-weight factor that can automatically adjust the weight of different views, thereby allowing each view to obtain the best possible weight. Secondly, the algorithm incorporates a weighted fuzzy factor, which serves to obtain local spatial information and local grayscale information to preserve image details as much as possible. Finally, in order to weaken the effects of noise and outliers in image segmentation, this algorithm employs the kernel distance measure instead of the Euclidean distance.

The authors added different kinds of noise to images and conducted a large number of experimental tests. The results show that the proposed algorithm performs better and is more accurate than previous multi-view fuzzy clustering algorithms in solving the problem of noisy image segmentation.

Most of the existing multi-view clustering algorithms are for multi-view datasets, and the multi-view fuzzy clustering algorithms are unable to eliminate noise points and outliers when dealing with noisy images. The algorithm proposed in this paper has stronger noise immunity and can better preserve the details of the original image.