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

Author :Shilpa Kadam, Pavan Kumar Srungaram, Sai Dheeraj Y, Manish S.S.S.R, P.T.V. Praveen, Sridhar Pappu, Dipak Kumar Satpathi

Article Citation :Shilpa Kadam ,Pavan Kumar Srungaram ,Sai Dheeraj Y ,Manish S.S.S.R ,P.T.V. Praveen ,Sridhar Pappu ,Dipak Kumar Satpathi , (2023 ) " Analysis of Linguistics and Math Features for Classification of Math Word Problems: Insights and Future Direction " , International Journal of Management and Applied Science (IJMAS) , pp. 18-22, Volume-9,Issue-8

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)

Type : Research paper

Published : Volume-9,Issue-8

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