International Journal of Telecommunications & Emerging Technologies
Predictive Analytics in E-Learning: Enhancing Student Performance Through Machine Learning Models
1-Student, Department of Electronics and Communication Engineering, Sri Siddhartha Institute of Technology, Tumakuru, Karnataka 572105, India
2-Student, Department of Electronics and Communication Engineering, Sri Siddhartha Institute of Technology, Tumakuru, Karnataka 572105, India
3-Department of Electronics and Communication Engineering, Sri Siddhartha Institute of Technology, Tumakuru, Karnataka 572105, India
Abstract
With the explosion of electronic devices and internet technologies in today’s world, the amount of data we store has grown massively. Data is simply information that has been organized in a way that makes it easy to share or work with. A potent technology that enables computers to learn from data without direct programming is machine learning, which we employ in our research. The focus of this study is on the development of prediction models using machine learning, particularly for e-learning platforms. By analyzing data such as student engagement, quiz scores, and interaction patterns, the model can predict how well students will perform and even identify those who might need extra help or are at risk of falling behind. We investigated several machines learning methods, including neural networks, support vector machines (SVM), and decision trees, to see which is most effective for these predictions. Machine learning helps make e-learning more personalized by figuring out how well students might do and suggesting courses that suit their abilities and interests. It looks at things like study habits and quiz results to spot students who may need extra help. While this makes learning more flexible, there are some challenges, like needing good data, protecting privacy, and making sure the system treats all students fairly. If done carefully, machine learning can make e-learning much better for everyone.
Keywords: E-learning, Machine Learning, Naïve Bayes, Evaluation, SVM
Keywords
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