Multi-Faceted Question Complexity Estimation Targeting Topic Domain-Specificity

• URL: http://arxiv.org/abs/2408.12850v1
• Date: Fri, 23 Aug 2024 05:40:35 GMT
• Title: Multi-Faceted Question Complexity Estimation Targeting Topic Domain-Specificity
• Authors: Sujay R, Suki Perumal, Yash Nagraj, Anushka Ghei, Srinivas K S,
• Abstract summary: 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. A model trained on these features demonstrates the efficacy of our approach in predicting question difficulty.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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