Paper Title
Analysis of Linguistics and Math Features for Classification of Math Word Problems: Insights and Future Direction

Abstract
Having math word problems (MWP)of varying difficulty levels can help instructors in identifying the knowledge levels of learners in teaching-learning systems. Given a large database of MWPs instructors spend significant time customizing content to meet learner needs. In this paper,Machine learning (ML) and AI-based methods are proposed to automatically classify math word problems. MWPs involve mathematical equations, symbols, and operators in addition to linguistic complexities. This paper presents various challengesin identifying and extracting relevant linguistics features as well as mathematical features that can aid in the automatic classification of MWPs. Based on our study we found that there is improvement in F1-score for a 3-level difficulty when compared to 5-level difficulty label of MWPs. Our study underscores the importance of further enhancing the feature set and developing appropriate mathematical tokenizersto improve the model performance. Keywords - Math Word Problem (MWP), Difficulty Level of MWP, Adaptive Learning Systems (ALS)