Using Transformer Language Models to Validate Peer-Assigned Essay Scores in Massive Open Online Courses (MOOCs)

Abstract

Massive Open Online Courses (MOOCs) such as those offered by Coursera are popular ways for adults to gain important skills, advance their careers, and pursue their interests. Within these courses, students are often required to compose, submit, and peer review written essays, providing a valuable pedagogical experience for the student and a wealth of natural language data for the educational researcher. However, the scores provided by peers do not always reflect the actual quality of the text, generating questions about the reliability and validity of the scores. This study evaluates methods to increase the reliability of MOOC peer-review ratings through a series of validation tests on peer-reviewed essays. Reliability of reviewers was based on correlations between text length and essay quality. Raters were pruned based on score variance and the lexical diversity observed in their comments to create sub-sets of raters. Each subset was then used as training data to finetune distilBERT large language models to automatically score essay quality as a measure of validation. The accuracy of each language model for each subset was evaluated. We find that training language models on data subsets produced by more reliable raters based on a combination of score variance and lexical diversity produce more accurate essay scoring models. The approach developed in this study should allow for enhanced reliability of peer-reviewed scoring in MOOCS affording greater credibility within the systems.