# Leveraging Human Feedback to Scale Educational Datasets: Combining Crowdworkers and Comparative Judgement

arXiv: https://arxiv.org/abs/2305.12894 · HTML: https://mlsanity.com/paper/2305.12894/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 22 May 2023.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/)

## Builds on (most cited first, 5 of 5)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?](https://mlsanity.com/paper/2106.00794/) (2021)
- [Quality Control in Crowdsourcing: A Survey of Quality Attributes, Assessment Techniques and Assurance Actions](https://mlsanity.com/paper/1801.02546/) (2018)
- [Improving Human-Labeled Data through Dynamic Automatic Conflict Resolution](https://mlsanity.com/paper/2012.04169/) (2020)

## Built on by (most cited first, 3 of 3)

- [LitSumm: Large language models for literature summarisation of non-coding RNAs](https://mlsanity.com/paper/2311.03056/) (2023)
- [Beyond Agreement: Rethinking Ground Truth in Educational AI Annotation](https://mlsanity.com/paper/2508.00143/) (2025)
- [How well do Large Language Models Recognize Instructional Moves? Establishing Baselines for Foundation Models in Educational Discourse](https://mlsanity.com/paper/2512.19903/) (2025)

## Abstract (arXiv)

Machine Learning models have many potentially beneficial applications in education settings, but a key barrier to their development is securing enough data to train these models. Labelling educational data has traditionally relied on highly skilled raters using complex, multi-class rubrics, making the process expensive and difficult to scale. An alternative, more scalable approach could be to use non-expert crowdworkers to evaluate student work, however, maintaining sufficiently high levels of accuracy and inter-rater reliability when using non-expert workers is challenging. This paper reports on two experiments investigating using non-expert crowdworkers and comparative judgement to evaluate complex student data. Crowdworkers were hired to evaluate student responses to open-ended reading comprehension questions. Crowdworkers were randomly assigned to one of two conditions: the control, where they were asked to decide whether answers were correct or incorrect (i.e., a categorical judgement), or the treatment, where they were shown the same question and answers, but were instead asked to decide which of two candidate answers was more correct (i.e., a comparative/preference-based judgement). We found that using comparative judgement substantially improved inter-rater reliability on both tasks. These results are in-line with well-established literature on the benefits of comparative judgement in the field of educational assessment, as well as with recent trends in artificial intelligence research, where comparative judgement is becoming the preferred method for providing human feedback on model outputs when working with non-expert crowdworkers. However, to our knowledge, these results are novel and important in demonstrating the beneficial effects of using the combination of comparative judgement and crowdworkers to evaluate educational data.