# Multi-Faceted Question Complexity Estimation Targeting Topic Domain-Specificity

arXiv: https://arxiv.org/abs/2408.12850 · HTML: https://mlsanity.com/paper/2408.12850/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2303.13948/) (2023)
- [Contextualized Topic Coherence Metrics](https://mlsanity.com/paper/2305.14587/) (2023)
- [Knowledge Questions from Knowledge Graphs](https://mlsanity.com/paper/1610.09935/) (2016)
- [Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform](https://mlsanity.com/paper/2403.08305/) (2024)
- [Automatic Generation of Multiple-Choice Questions](https://mlsanity.com/paper/2303.14576/) (2023)
- [Prompt-Engineering and Transformer-based Question Generation and Evaluation](https://mlsanity.com/paper/2310.18867/) (2023)

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

- [MAB Optimizer for Estimating Math Question Difficulty via Inverse CV without NLP](https://mlsanity.com/paper/2508.19014/) (2025)

## Abstract (arXiv)

Question difficulty estimation remains a multifaceted challenge in educational and assessment settings. Traditional approaches often focus on surface-level linguistic features or learner comprehension levels, neglecting the intricate interplay of factors contributing to question complexity. This paper presents a novel framework for domain-specific question difficulty estimation, leveraging a suite of NLP techniques and knowledge graph analysis. We introduce four key parameters: Topic Retrieval Cost, Topic Salience, Topic Coherence, and Topic Superficiality, each capturing a distinct facet of question complexity within a given subject domain. These parameters are operationalized through topic modelling, knowledge graph analysis, and information retrieval techniques. A model trained on these features demonstrates the efficacy of our approach in predicting question difficulty. By operationalizing these parameters, our framework offers a novel approach to question complexity estimation, paving the way for more effective question generation, assessment design, and adaptive learning systems across diverse academic disciplines.