# Tuned Models of Peer Assessment in MOOCs

arXiv: https://arxiv.org/abs/1307.2579 · HTML: https://mlsanity.com/paper/1307.2579/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2013.

## Built on by (most cited first, 10 of 10)

- [Strategyproofing Peer Assessment via Partitioning: The Price in Terms of Evaluators' Expertise](https://mlsanity.com/paper/2201.10631/) (2022)
- [Peer Selection with Noisy Assessments](https://mlsanity.com/paper/2107.10121/) (2021)
- [Evaluating LLM-Contaminated Crowdsourcing Data Without Ground Truth](https://mlsanity.com/paper/2506.06991/) (2025)
- [Better Peer Grading through Bayesian Inference](https://mlsanity.com/paper/2209.01242/) (2022)
- [Manipulation and Peer Mechanisms: A Survey](https://mlsanity.com/paper/2210.01984/) (2022)
- [Stochastically Dominant Peer Prediction](https://mlsanity.com/paper/2506.02259/) (2025)
- [Linear Regression under Missing or Corrupted Coordinates](https://mlsanity.com/paper/2509.19242/) (2025)
- [High-Dimensional Gaussian Mean Estimation under Realizable Contamination](https://mlsanity.com/paper/2603.16798/) (2026)
- [Polarity in the Classroom: A Case Study Leveraging Peer Sentiment Toward Scalable Assessment](https://mlsanity.com/paper/2108.10068/) (2021)
- [Improving Peer Assessment with Graph Convolutional Networks](https://mlsanity.com/paper/2111.04466/) (2021)

## Abstract (arXiv)

In massive open online courses (MOOCs), peer grading serves as a critical tool for scaling the grading of complex, open-ended assignments to courses with tens or hundreds of thousands of students. But despite promising initial trials, it does not always deliver accurate results compared to human experts. In this paper, we develop algorithms for estimating and correcting for grader biases and reliabilities, showing significant improvement in peer grading accuracy on real data with 63,199 peer grades from Coursera's HCI course offerings --- the largest peer grading networks analysed to date. We relate grader biases and reliabilities to other student factors such as student engagement, performance as well as commenting style. We also show that our model can lead to more intelligent assignment of graders to gradees.