To tailor a learning system to the student's level and needs, we must consider the characteristics of the learning content, such as its difficulty. While natural language processing allows us to represent text efficiently, the meaningful representation of mathematical formulas in an educational context is still understudied. This paper adopts structural embeddings as a possible way to bridge this gap. Our experiments validate the approach using publicly available datasets to show that incorporating syntactic information can improve performance in predicting the exercise difficulty.

Structural information in mathematical formulas for exercise difficulty prediction: a comparison of NLP representations

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W12: BEA-2022: 17th Workshop on Innovative Use of NLP for Building Educational Applications

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## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
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NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Ekaterina Loginova

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Structural information in mathematical formulas for exercise difficulty prediction: a comparison of NLP representations

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