Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
Browse
4 results
Search Results
Item Restricted THE MOTIVATIONS OF AMERICAN PUBLIC ATTENTION TO THE MEDIA COVERAGE OF THE RUSSIAN-UKRAINIAN WAR(University of Missouri-Columbia, 2024-07) Alamer, Yousef Fouad; Houston, BrianThe Russian-Ukraine war presents a significant event to understand the American public's attention to international crises that the US is not directly involved in them. Drawing on Use and Gratification Theory as a theoretical framework, I conducted a survey combined with an experimental condition to explore several internal and external motivations that influenced American public attention to the media coverage of the ongoing conflict between Russia and Ukraine. Data collected in an online survey of 453 college students revealed a positive direct influence of social interaction, political interest, and worry on their media consumption related to that war. In addition, partisanship and identification with Russia showed a negative influence on the attention given to the war news in Ukraine. However, personal connection, view on Russia, beliefs about democracy in Ukraine, and identification with Ukraine showed no relationship with media use related to the war. About the differences in the condition between the peer concern message and the peer non-concern message, participants who received the peer concern message about the implications of the Russian-Ukrainian war reported a higher level of worry and a desire for social interaction linked to the war. The analysis reveals that peer concern-inducing message influence the worry and social interaction associated with the implications of the Russian-Ukrainian war. However, this is not the case with their need to consume more media related to the war. Overall, this dissertation contributes to our understanding of the motivations behind media consumption during the Russo-Ukrainian war. Finally, we discuss some implications of our findings, the limitations of this research, and directions for future research.22 0Item Restricted PERSPECTIVES OF INSTRUCTIONAL COACHES IN SAUDI ARABIA ABOUT THE CURRENT MODEL TO IDENTIFY STUDENTS WITH LEARNING DISABILITIES(Washington State University, 2024) Alwadei, Hassan Mesfer; Dunn, MichaelInstructional coaches support teachers in referring and evaluating students through the learning disability (LD) identification process in Saudi Arabia. The instructional coaches also help students as they do day-to-day learning and assessment tasks. This study explored instructional coaches’ perspectives about the current model for identifying students with LD, its effectiveness, and the potential implementation of Multi-Tiered Systems of Support (MTSS) as an alternative model. This study used semi-structured interviews with 12 instructional coaches to evaluate Saudi Arabia’s current identification methods. The results highlighted significant concerns about accuracy of identification and the risks of misidentifying students with LD. The findings suggest that MTSS, although it is not currently employed in Saudi schools, could provide a more reliable and comprehensive approach to addressing students’ academic and behavioral needs. Emphasizing the need for culturally- and linguistically-appropriate Special Education practices, the study advocates for MTSS’s gradual implementation with teacher training. This study contributes to the understanding about LD identification in Saudi schools and positions MTSS as an alternative solution for more effective support and identification. This study includes practical implications and recommendations for future research.44 0Item Restricted Constructing the Self as a Translator: An Ethnographic Study Exploring the Saudi Translators’ Identification Processes on Twitter(Queen's University Belfast, 2024-03-27) Alkhashan, Ohud; Blumczyński, Piotr; Kaess, KathleenTwitter is one of the most popular social media platforms in Saudi Arabia, with over 12 million users. Since 2016, a growing number of translators in Saudi Arabia have been utilizing this platform to connect and interact with each other, creating a network of Saudi translators on the platform. The goal of this thesis is to explore how the Saudi translators construct their identity as professional translators on Twitter since the emergence of their network by investigating the impact of their Twitter interactions on their identification processes. To achieve this goal, the study follows a multi-sited internet ethnography where Twitter is conceptualized as an ethnographic field site for participant observation, in addition to conducting semi-structured interviews with ten Saudi professional translators from this Twitter network to gain an in-depth look into their experiences on the platform. Examining the manifestations of the concept of identification, described as a process of constructing the self by recognizing similarities and differences between the self and other during social interaction, highlights identification as processual and continuous. More specifically, the manifestations of the Saudi translators’ identification processes in their discursive practices on Twitter reflect their perceptions of visibility to one another motivated by finding a sense of belonging to a professional community for Saudi translators. This visibility fuels their perceptions of (having) power as a form of influence on the platform. Finally, (gaining) recognition of the self as a translator and translation as professional practice in Saudi Arabia is viewed as an outcome of (self-)representation practices. Exploring the Saudi translators’ identification processes on Twitter reveals the nuances in constructing a professional identity on the platform particularly in a professional network. More importantly as well, it reveals the concept’s ontological dimension as a process of becoming.17 0Item Restricted Machine Learning Methods for Human Identification from Dorsal Hand Images(2023-06-27) Alghamdi, Mona; Angelov, Plamen; Rahmani, HusseinPerson identification is a process that uniquely identifies an individual based on physical or behavioural traits. This study investigates methods for the analysis of images of the human hand, focusing on their uniqueness and potential use for human identification. The human hand has significant and distinctive characteristics, and is highly complex and interesting, yet it has not been explored in much detail, particularly in the context of the contemporary high level of digitalisation and, more specifically, the advances in artificial intelligence (AI), machine learning (ML) and computer vision (CV). This research area is highly multi-disciplinary, involving anatomists, anthropologists, bioinformaticians, image analysts and, increasingly, computer scientists. A growing pool of advanced methods based on AI, ML and CV can benefit and relate directly to a better representation of the human hand in computer analysis. Historically, the research methods in this area relied on ‘handcrafted’ features such as the local binary pattern (LBP) and histogram of gradient (HOG) extraction, which necessitated human intervention. However, such approaches struggle to encode the human hand in variable conditions effectively, because of the change in camera viewpoint, hand pose, rotation, image quality, and self-occlusion. Thus, their performance is limited. Recently, there has been a surge of interest in deep learning neural network (DLNN) approaches, specifically convolutional neural networks (CNNs), due to the highly accurate results achieved in many applications and the wide availability of images. This work investigates advanced methods based on ML and DLNN for segmenting hand images with various rotation changes into different patches (e.g., knuckles and fingernails). The thesis focuses on developing ML methods like pre-trained CNN models on the 'ImageNet' dataset to learn the underlying structure of the human hand by extracting robust features from hand images with diverse conditions of rotation, and image quality. Also, this study investigates fine-tuning the pre-trained models of DLNN on subsets of hand images, as well as using various similarity metrics to find the best match of the individual’s hand. Furthermore, this work explores different types of ensemble learning or fusions, those of different region and similarity metrics to improve human identification results. This thesis also presents a study of a Siamese network on sub-images or segments of human dorsal hands for identification tasks. All presented approaches are compared with the state-of-the-art methods. This study advances the understanding of variations in and the uniqueness of humans using patches of their hands (e.g., different types of knuckles and fingernails). Lastly, it compares the matching performances of the left- and right-hand patches using various hand datasets and investigates whether the fingernail produces better identification results than the knuckles. This research shows that the proposed framework for person identification based on hand components led to better person identification results. The framework consists of vital feature extractions based on deep learning neural network (DLNN) and similarity metrics. These elements enhanced the performance. Also, the fingernails' shape performed better than other hand components, including the base, major, and minor knuckles. The left hand can be more distinguishable to individuals than the right hand. The fine-tuning of the hand components and ensemble learning improved the identification results.38 0