Adaptive Academic Guidance System via Student Activity Performance Analysis and Profiling

Abstract

In the ever-evolving landscape of education driven by rapid technological advancements, e-learning has emerged as a transformative force. However, it faces challenges, notably the difficulty of personalizing education in a digital environment. This paper introduces an innovative adaptive academic guidance system called "StudyMate." The system analyzes student activity, performance, and profiles to enhance the e-learning experience. The objectives encompass extensive research, surveys for primary and secondary data collection, algorithm development for student analysis, foundational database creation, source code development, and online system hosting. StudyMate leverages learning management system (LMS) functionalities to offer tailored learning experiences and demonstrates its efficacy in the digital realm. The literature review explores adaptive e-learning systems, highlighting the need for personalized education and referencing related research projects. The methodology outlines planning, design, implementation, testing, and critical evaluation phases, including sample code snippets and test cases. The findings confirm the successful implementation of adaptive concepts in StudyMate, addressing the academic question effectively. The limitations lie in the system's scope, primarily focusing on student views, leaving room for future enhancements such as lecturer and admin functionalities. The paper concludes with the potential for expanding StudyMate's features and improving its usability, making it a valuable solution for personalized e-learning.