This dissertation investigates the application of network models in educational assessment across diverse learning settings, encompassing both clinical and classroom environments. As educational paradigms shift and digital tools become more embedded in learning processes, traditional assessment methods often fall short in effectively capturing the complex interdependencies and dynamics of modern educational settings. This research leverages network analysis to provide a nuanced assessment framework capable of visualizing and analyzing the multifaceted relationships inherent in educational processes. The study is structured around two primary case studies. The first explores the assessment of laparoscopic surgical skills in a clinical lab setting, employing network models to analyze electromyography (EMG) data and the NASA Task Load Index (TLX) scores to evaluate trainees' performance and cognitive load. This approach facilitates a more objective assessment of surgical training, highlighting the impact of cognitive and physical demands on performance. The second case study examines the impact of interactive textbooks on student engagement and academic performance in an introductory computing course, comparing online and in-person teaching modalities. This case study focuses on how different teaching modalities influence learning engagement and performance through the use of network models constructed from student interaction data with digital learning resources. The findings from these case studies underscore the effectiveness of network models in enhancing educational assessment. In the clinical setting, the use of network analysis revealed significant patterns of skill acquisition and highlighted the importance of personalized training approaches. In the educational setting, the analysis demonstrated that interactive textbooks significantly enhance student engagement and can equalize the learning outcomes between different teaching modalities. This dissertation contributes to the field of educational assessment by demonstrating the applicability and benefit of network models in diverse educational settings. It offers actionable insights for educators and policymakers looking to implement advanced analytical tools to enhance assessment strategies. Furthermore, it provides a foundation for future research in the area of educational technology and assessment, suggesting new pathways for integrating network theory into practical educational methodologies. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]