Browsing by Author "Alqarni, Reem"

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Meaningful Online Interactions as a Predictor of Student Learning Outcomes in Online Learning Environments: Moderating Effect of Student Differences
(Saudi Digital Library., 2025) Alqarni, Reem; Greg, Kessler
The purpose of this study was to investigate the direct influence of meaningful interactions, as classified by Moore’s interaction model, on student learning outcomes in online learning environments. Specifically, this study explored the predictive relationships between three types of online interactions (learner-instructor interaction (LII), learner-learner interaction (LLI), and learner-content interaction (LCI)) and student learning outcomes, namely student satisfaction and perceived learning in online courses, focusing on their unique and combined contributions. Furthermore, the study examined the moderating effects of student-related variables (gender, prior online learning experience, and academic degree level) on these relationships. The study involved 217 undergraduate and graduate students from the College of Education at Ohio University who had previously enrolled in at least one online course. Data was collected during the Spring semester of 2024. A quantitative, correlational research design was employed. A self-reported survey instrument incorporating validated scales for student satisfaction, perceived learning, and the three types of interactions (learner-instructor interaction (LII), learner-learner interaction (LLI), and learner-content interaction (LCI)) within an online course was administered. A pilot test was conducted to assess the survey's validity and reliability and research procedures, showing strong measurement consistency within the sample. Data analysis utilized multiple regression to examine predictive relationships among the variables. Moderated multiple regression, implemented through Hayes' PROCESS macro and hierarchical regression, was employed to investigate the potential moderating effects of student-related variables on the relationship between interaction types and student outcomes. Results indicated that the three types of interaction (LCI, LII, and LLI) significantly predict SAT and PL in online courses. Among the predictors, LCI demonstrated the strongest and most significant predictor of both student satisfaction and perceived learning, followed by LII, which had a significant impact on SAT but a limited effect on PL. LLI demonstrated a moderate contribution to PL but had a minimal impact on SAT. Furthermore, the moderation analysis revealed that academic degree level and prior online learning experience significantly moderated the relationship between LCI and PL. In contrast, gender did not significantly moderate the relationship between the examined interaction types (LCI and LLI) and perceived learning. Additionally, none of the examined moderators influenced the relationship between interaction types (LCI and LII) and student satisfaction.
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Optimizing HTGR Spherical Fuel Element Manufacture Technology through Dispersion Fuel Press Processing
(Tsinghua University, 2024-06-25) Alqarni, Reem; Liu, Bing
This thesis presents an in-depth analysis of the dispersion fuel press process for High- Temperature Gas-Cooled Reactors (HTGRs), focusing on optimizing key parameters that influence the quality and performance of HTGR fuel elements. Through comprehensive simulations performed utilizing COMSOL Multiphysics(FEM), this study systematically investigates the effects of pressing pressure and TRISO-coated particle count on the stress during the pressing process of spherical fuel elements. The research is structured into two main phases. The first phase examines the impact of applying varying pressures (20 to 35 MPa) on graphite powder within a rubber mold, emphasizing the necessity of achieving uniform compaction and material integrity. The second phase extends the investigation to incorporating TRISO-coated particles, analyzing how varying counts of these particles (ranging from 8000 to 20000) affect stress distri- bution, displacement, and volumetric strain within the fuel spheres. These simulations provide critical insights into optimizing the dispersion fuel press process, highlighting the balance between pressure application and particle count for enhancing fuel element fabrication. Key findings reveal that precise control and uniform application of pressure are cru- cial for ensuring the desired compaction and structural integrity of fuel spheres. Moreover, the study demonstrates that an optimized distribution of TRISO particles significantly in- fluences the mechanical behavior and resilience of the fuel elements, offering pathways to improve fuel performance and reactor eﬀiciency. The research outcomes contribute valuable guidelines for the design, optimization, and manufacturing of HTGR fuel ele- ments, proposing advancements that could enhance the safety, eﬀiciency, and reliability of nuclear reactors. This thesis underscores the importance of meticulous parameter optimization in HTGR fuel fabrication, providing a foundation for future research and development in nuclear fuel technology. By advancing our understanding of the dispersion fuel press process, this work aims to contribute to the nuclear energy sector’s efforts to develop safer, more eﬀicient, and sustainable reactors.
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