SHAPE: Design and Evaluation of a Transformative Model for Engineering Education

Abstract

This study presents the implementation and evaluation of the SHAPE (Student-centered, Holistic, Application-oriented, Personalized, and Engaging) framework integrated with Problem-Based Learning (PBL) in a Pattern and Anomaly Detection course for third-year Artificial Intelligence and Machine Learning students. The course aimed to bridge the gap between theoretical knowledge and practical application in engineering education. Students were organized into 15 groups, focusing on activity recognition in various domains such as human activity, agriculture, and environmental monitoring. The curriculum blended theoretical concepts with hands-on projects using Raspberry Pi kits and IoT sensors. The SHAPE approach created an immersive learning environment centered around real-world problem-solving scenarios. Results showed significant improvements in student performance and engagement. Quantitative assessments revealed a 5% increase in average student performance compared to previous iterations. The course produced 15 live solutions, with one project submitted for patent and eight converted into research publications. Qualitative feedback indicated high levels of student satisfaction, with 85% reporting increased engagement and 90% feeling better prepared for real-world applications. The integration of SHAPE and PBL proved highly effective in enhancing learning outcomes and fostering practical skill development. This innovative model demonstrates potential for broader application in engineering education, addressing the limitations of traditional teaching methods and better preparing students for industry and research roles. Future research should explore the scalability of this approach across different engineering disciplines and assess its long-term impact on students' career trajectories.