Saudi Cultural Missions Theses & Dissertations
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Item Restricted EDUBUDDY: A WEB-BASED AI TOOL FOR DETECTING LEARNING STYLES AND RECOMMENDING PERSONALIZED STUDY STRATEGIES(Saudi Digital Library, 2025) ALDUWAYS, TAHANI MOHAMMED SH; Dumlao, Menchita FThe ongoing challenge of meeting diverse learning preferences within standardized education highlights the need for more personalized learning support. Although learning styles—preferences for perceiving, processing, and retaining information—are often linked to engagement and academic outcomes, common assessment approaches rely heavily on self-report tools (e.g., VARK, Felder-Silverman ILS) that face major concerns: inconsistent reliability and validity, static profiles that overlook context shifts, and limited usefulness in producing practical study guidance. To address this gap, this study proposes, develops, and evaluates a Web-Based AI Tool for dynamic learning-style detection and personalized study-strategy recommendation. Rather than depending on questionnaires, the tool continuously and non- intrusively infers learning preferences using machine learning on authentic behavioral data within a web learning platform. Indicators include content format choices (text, video, simulations), time-on-task patterns, navigation paths, interactions with learning elements, and performance differences across assessment types. These data generate a probabilistic, continuously updated learning style profile based on observable behavior, reducing self- report bias and accommodating context-dependent changes. A recommendation engine then translates the AI-inferred profile into concrete, actionable study strategies delivered through the platform or a user dashboard. For example, visually oriented learners may receive guidance on concept mapping and color- coded summaries, while active learners may be encouraged to engage in peer discussion and to practice frequently. The system integrates user feedback (explicit ratings or implicit adoption signals) to refine both detection accuracy and recommendation relevance over time. Using a design-based research methodology, the tool is iteratively designed and tested in authentic settings. Mixed-methods evaluation examines detection validity, user acceptance, effects on metacognitive awareness and study habits, and initial links to learning outcomes, alongside ethical safeguards for privacy and bias. Preliminary results suggest the tool is feasible, well-received, and supports more intentional study practices, offering a practical bridge between identifying preferences and improving learning strategies.6 0Item Restricted Understanding and Supporting Users in Visual Network Exploration(University of Edinburgh, 2024) AlKadi, Mashael; Bach, BenjaminNetwork visualization tools are used in numerous domains to explore data. Various network visualizations have been designed to explore data from different perspectives. However, few resources investigate how analysts create and interact with the visualizations to explore networks in the wild. By analysts, we denote users varying in their background and level of expertise. Being in the wild signifies that analysts work outside controlled settings where no pre-designed tasks are set. This thesis focuses on studying how to understand and support analysts in the process of network visual exploration. In such a process, analysts need to learn about the concepts, the tool(s), the processes, the visualization(s) and interactions, and the workflow. They also need to ensure that they can apply what they have learned accumulatively on their data toward their goal(s). Thus, to understand and support analysts, we have applied and collected data through mixed methods: interaction logging, mini-questionnaire, and visualization's state annotation, running an intensive 6-week course, designing an analytical dashboard, and implementing a coaching program. We ran our studies using the Vistorian a web-based tool that offers 4 types of interactive network visualizations. This multimethod research led to the following contributions. Interaction logging allowed identifying 4 types of users based on their tool usage and advancement in the visual exploration process: demo users, data strugglers, single-session and multi-session explorers. To examine user types further, we designed a utility to capture and annotate visualizations' states, which we call bookmarks. We identified eight barriers that might cause analysts to struggle through the visual exploration process. We designed an analytical dashboard by specifying Key Performance Indicators (KPIs) and analyzing interaction logs accordingly. Those KPIs informed the assessment of the tool, the visualizations, the help resources, and the users' exploration. To support analysts, we designed a self-regulated guide for network visual exploration, which we call a roadmap. The roadmap describes step-by-step the processes of network visual exploration and associated activities. We also described 16 exploration strategies analysts follow, classified into three categories. We found 4 distinguished analyst groups based on how they aim to explore whether with/without research goals and/or data, which we call roadmap pathways. We evaluated the roadmap through a coaching program and found that it plays a crucial role in teaching networks visual exploration. Those findings have implications for the network's visual exploration through enhancing the design of its tools and associated educational efforts, mitigating barriers within, and supporting various user types.17 0