Machine Learning Techniques: A Review of Predicting Student’s Performance - Sharma, Rahul (Journal of Advanced Research in Information Technology, Systems and Management, 2023)
This comprehensive abstract discusses various facets of Machine Learning, focusing on its significance in the context of education and academic performance prediction. The abstract begins by highlighting the data-driven era brought about by the internet and digitization, underscoring how the abundance of data has given rise to the concept of Machine Learning. It emphasizes Machine Learning as a critical aspect of artificial intelligence that empowers systems to learn autonomously from data, without explicit programming. The abstract further delves into the educational sector’s reliance on students’ performance, emphasizing the importance of various factors in assessing academic success. It then introduces the objectives of a proposed systematic review, including identifying gaps in predictive methods and attributes for analyzing students’ performance. Supervised Learning, for instance, is detailed along with its essential components such as labeled data, the training phase, prediction phase, and evaluation. It highlights real-world applications, from spam detection to customer churn prediction, demonstrating the versatility and widespread use of supervised learning in various industries. Subsequently, the abstract transitions to Unsupervised Learning, explaining its role in clustering and association, and how it is used in customer segmentation, image segmentation, and anomaly detection, among other applications. It introduces pre-trained models, fine-tuning, domain adaptation, types of Transfer Learning, and its applications in computer vision, natural language processing, healthcare, autonomous driving, recommendation systems, and more. The abstract further explores the role of Machine Learning in academia. Now a days, most of the educational bodies are used Machine Learning techniques to make improvement in their system. Performance of the students is analyzed by using these techniques and help to students in improving their performance. Therefore, a systematic review of all papers related to this field is needed to understand the Machine Learning techniques in education and how to predict the performance of students.