Abstract:
In recent years, advancements in eLearning have underscored the need for more effective assessment management systems. This research introduces Smart Study, a keyword-driven application designed to address prevalent challenges in online education, including ineffective time management, procrastination, and difficulty in identifying key study materials. The application leverages an advanced keyword extraction algorithm integrated with sentence similarity techniques to extract and prioritize essential content, guiding students toward more efficient study plans. Additionally, Smart Study incorporates personalized timelines, burndown charts, and real-time progress tracking to optimize study planning and execution. The system addresses gaps identified in existing platforms such as Moodle and Coursera, which lack personalized timelines and effective learning resource integration. By utilizing an innovative keyword extraction algorithm, Smart Study achieved superior precision, recall, and F1 scores in benchmarking tests, outperforming traditional models such as TF-IDF and spaCy. The enhanced algorithm attained a mean accuracy of 93.33%, substantially surpassing TF-IDF’s 46.67% and spaCy’s 50%. Functional and non-functional testing verified the system’s reliability and usability, with user feedback emphasizing the intuitive interface and robust performance. This research contributes to the field by integrating advanced keyword extraction techniques with personalized time management tools, offering a comprehensive solution for both students and educators in managing assessments. Despite its success, limitations such as the handling of specialized terminologies and the scope of testing suggest potential areas for future improvement. These include expanding the dataset and enhancing algorithm scalability. Overall, Smart Study demonstrates considerable potential to improve academic performance by providing a structured and efficient approach to assessment management in digital learning environments.
