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Student Project | Electronics & Communication Engineering | India | Volume 13 Issue 11, November 2024 | Rating: 5.6 / 10
Predicting Academic Future Courses for Students Using Machine Learning
Preetham S., Dr. Chandrashekhar M C, Dr M N Eshwarappa
Abstract: "Predicting Academic Future Courses for Students Using Machine Learning" should provide an overview of the research objectives, methodologies, findings, and implications. This study explores the application of machine learning techniques in predicting the most suitable academic future courses for students based on their past academic performances, interests, and career aspirations. The primary objective is to develop a predictive model that helps academic advisors and institutions provide tailored course recommendations, thereby improving students' academic success and satisfaction. Various machine learning algorithms, including decision trees, support vector machines, and neural networks, are evaluated to determine the optimal approach for accurate course predictions. The dataset includes a range of student data, such as grades in prerequisite courses, previous course selection patterns, extracurricular interests, and feedback on prior courses. Feature engineering and data preprocessing are emphasized to handle challenges like imbalanced data and missing values. The research shows that machine learning models can achieve high predictive accuracy, with the neural network model performing best under specific conditions. The findings suggest that machine learning-based course recommendation systems can significantly enhance educational planning by aligning course selections with students' strengths and preferences, ultimately leading to improved educational outcomes. This study contributes to the growing field of educational data mining and demonstrates the potential for AI-driven approaches to personalize academic pathways in higher education.
Keywords: E-learning, Machine Learning, Na?ve Bayes, Evaluation, Predictive modelling, Support vector machines, Data preprocessing
Edition: Volume 13 Issue 11, November 2024,
Pages: 550 - 554