Assessing understanding gained of Nottingham Caves through Projected Augmented Relief Model (PARM)
Publication Date
December 10, 2024
Creator
Abstract
This dissertation examines the effectiveness of Projected Augmented Relief Models (PARM) in improving the educational understanding of geographical and historical contexts, specifically focusing on the Nottingham caves. The research targets two distinct educational demographics: school children and postgraduate students. By integrating digital and physical modeling aspects, PARM aims to enhance spatial comprehension and facilitate interactive learning (Priestnall et al., 2012). Utilizing a mixed-methods approach, including surveys, observational studies, interviews, and user feedback, this study evaluates PARM's impact compared to traditional 2D maps (Manduca & Mogk, 2006; Wiltshier & Edwards, 2014). Quantitative assessments involve pre-and post-tests to measure knowledge gains (Chen et al., 2018; Ştefan, 2012), while qualitative feedback highlights user engagement and pedagogical effectiveness.
The results demonstrate that PARM significantly enhances excitement and informational retention compared to traditional methods, particularly among school-aged participants. A notable correlation between increased excitement and enhanced understanding suggests that PARM's engaging visualizations effectively promote deeper learning. Feedback from participants underscores the model's ability to simplify complex information, making it accessible and engaging. Substantial improvements in test scores further corroborate PARM's effectiveness as an educational tool.
The study concludes that PARM not only significantly enriches learning experiences but also has the potential to be integrated with advanced technologies like virtual reality further to enhance educational outcomes (Khaitov, 2019). The findings advocate for broader application and continuous development of PARM technologies across various academic levels and settings to maximize its educational benefits and adaptability.
Item Type
ethesis
Thesis Type
MRes
Supervisors
Subjects (LC)
Associated Schools / Departments
Faculty of Engineering
eprints ID
80003
UoN Repository URI
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Final_Dissertatation_Aug_2024.pdf
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Full-text
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