A Collaborative System for Machine Learning-Based Final-Year Projects With Enhanced Dataset Accessibility

Abstract

This chapter explores the transformative impact of information and communication technology (ICT) on pedagogy, specifically focusing on the integration of collaboration tools in final year projects (FYPs). Final year projects (FYPs) represent the ultimate activity in the student's curriculum. They are designed to use, test, and enhance the knowledge students have gained over the years by confronting them with real-world projects. Despite existing systems for FYPs, the chapter identifies gaps, particularly in covering the entire FYP process and in addressing different collaborative aspects. With a focus on the rise of machine learning-based FYPs, this research aims to propose a comprehensive solution based on a proposed collaboration architecture in response to various needs such as communication, coordination, production, and resource sharing. The application is designed for multiple user roles, including students, advisors, and administrative staff, each allocated a personalized workspace. The novelty of the proposed system is its comprehensive coverage of all collaborative aspects mentioned throughout the FYP process, including proposal processing, project assignment, project completion, and evaluation. The research contributes to fostering innovation in machine learning projects by effectively managing and sharing datasets through collaboration tools. The results indicate good scores in improving collaborative aspects with a score of 98% for virtualization in coordination and 96% for communication. The results also showed that surveyed users are positively inclined to use the system as their final year project (FYP) management system, with an attention-to-use score of 90% of advisors and 92.8% of students.