Soft Grades: A Calibrated and Accurate Method for Course-Grade Estimation that Expresses Uncertainty

In traditional educational settings, students are often summarized by a single number-a final course grade-that reflects their performance.While final grades are convenient for reporting or comparison, they oversimplify a student's true ability and do not express uncertainty.In this paper, we introduce a new item-response model for classroom settings that infers a distribution over student abilities and uses this to represent each student's final grade as a probability distribution.This approach captures the uncertainty that comes from variations in both student performance and grading processes.Practical applications of our approach include enabling teachers to better understand grading confidence, impute missing assignment scores, and make informed decisions when curving final grades.For students, the model offers probabilistic estimates of their final course grades based on current performance, supporting informed academic decisions such as opting for Pass/Fail grading.We evaluate our model using real-world datasets, showing that the Soft Grades model is well-calibrated and surpasses the state-of-the-art polytomous IRT model in accurately predicting future scores.Additionally, we share a web application and Python scripts to make our model available to teachers and students. CCS Concepts Computing methodologies Model verification and validation; Model development and analysis.