Multi-Faceted Question Complexity Estimation Targeting Topic Domain-Specificity
<|reference_start|>Multi-Faceted Question Complexity Estimation Targeting Topic Domain-Specificity: 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.<|reference_end|>
@article{r2024multi-faceted,
    title={Multi-Faceted Question Complexity Estimation Targeting Topic
  Domain-Specificity},
    author={Sujay R, Suki Perumal, Yash Nagraj, Anushka Ghei and Srinivas K S},
    journal={arXiv preprint arXiv:2408.12850},
    year={2024},
    doi={10.5121/csit.2024.141513},
    archivePrefix={arXiv},
    eprint={2408.12850},
    primaryClass={cs.CL cs.LG}
}