Reducing Bias in Student Peer Evaluation: A Variational Inference Approach

Peer evaluation is essential in education, offering students valuable feedback to improve their work, develop critical thinking, and collaborate effectively. For instructors, it provides a scalable way to manage assessments, particularly in large classes or MOOCs where individual feedback is challenging. However, traditional peer evaluation systems often introduce biases from personal relationships, expectations, or presentation styles, impacting evaluation accuracy and failing to reflect true performance. To overcome these limitations, we have developed a peer evaluation system that employs Bayesian statistics, particularly variational inference, to reduce bias and provide a more accurate estimation of each student's true performance. In our system, peer evaluations are treated as probabilistic entities, with the biases and variances of each reviewer modeled as latent variables that can be estimated and corrected over time. Variational inference is used to iteratively refine these parameters based on the observed peer reviews. This allows the system to adjust for the subjective tendencies of individual reviewers, producing an unbiased true score for each student. After demonstrating the effectiveness of variational inference, we propose an ensembling approach that combines it with other state-of-the-art methods. This strategy harnesses the complementary strengths of each method to enhance the accuracy of final evaluations in real-world applications. We tested this peer review system with data obtained from a group of students, including both Ph.D. and M.Sc. scholars, during a two-week series of presentations. The objective of the students was to present their work by interacting with companies and universities and by taking part in a conference. The final adjusted scores were then returned to the students as feedback, providing them with a clearer and more objective evaluation of their work. This peer evaluation system offers a data-driven and scalable solution to address bias in student assessments, particularly in large educational settings. By leveraging Bayesian statistics and variational inference, the system enhances the fairness and objectivity of peer evaluations. This ultimately supports a more transparent learning environment that encourages student development and growth.