Grade Prediction

Grade prediction, the task of estimating a numerical or categorical grade based on available data, is a rapidly evolving field with applications spanning education, industrial processes, and medical imaging. Research focuses on improving prediction accuracy using various machine learning models, including deep neural networks, random forests, and physics-informed neural networks, often incorporating techniques like graph representation learning to leverage relational data. Addressing algorithmic bias and optimizing for both ranking and grade prediction accuracy are key challenges, with recent work demonstrating the benefits of integrating domain knowledge and handling ordinal grade information. Improved grade prediction methods hold significant potential for automating assessments, optimizing resource allocation, and enhancing decision-making across diverse domains.