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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.

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.