Summary of Multi-faceted Question Complexity Estimation Targeting Topic Domain-specificity, by Sujay R et al.
Multi-Faceted Question Complexity Estimation Targeting Topic Domain-Specificity
by Sujay R, Suki Perumal, Yash Nagraj, Anushka Ghei, Srinivas K S
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel framework for domain-specific question difficulty estimation, using NLP techniques and knowledge graph analysis. The authors introduce four key parameters that capture different aspects of question complexity: Topic Retrieval Cost, Topic Salience, Topic Coherence, and Topic Superficiality. These parameters are operationalized through topic modelling, knowledge graph analysis, and information retrieval techniques. A model trained on these features demonstrates the efficacy of this approach in predicting question difficulty. This framework can be used for more effective question generation, assessment design, and adaptive learning systems across diverse academic disciplines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us figure out how hard a question is by looking at what’s going on inside it. It uses special tools to understand topics and how they relate to each other. The authors identify four important things that make questions easy or hard: how much effort it takes to find the topic, how clear the topic is, how well the topic makes sense, and how superficial it is (or not). They show that their method can predict how hard a question is with good accuracy. This could be really helpful for creating better questions, designing tests, and making learning more personalized. |
Keywords
» Artificial intelligence » Knowledge graph » Nlp
