# Analyzing Examinee Comments using DistilBERT and Machine Learning to Ensure Quality Control in Exam Content

arXiv: https://arxiv.org/abs/2504.06465 · HTML: https://mlsanity.com/paper/2504.06465/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 1 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 1 of 1)

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)

## Built on by (most cited first, 1 of 1)

- [Automated item evaluation: Predicting item acceptance and rejection using LLM-generated critiques](https://mlsanity.com/paper/2608.06609/) (2026)

## Abstract (arXiv)

This study explores using Natural Language Processing (NLP) to analyze candidate comments for identifying problematic test items. We developed and validated machine learning models that automatically identify relevant negative feedback, evaluated approaches of incorporating psychometric features enhances model performance, and compared NLP-flagged items with traditionally flagged items. Results demonstrate that candidate feedback provides valuable complementary information to statistical methods, potentially improving test validity while reducing manual review burden. This research offers testing organizations an efficient mechanism to incorporate direct candidate experience into quality assurance processes.