Analysis Of The Efficiency of MathBERT and T5 Embeddings in Classification of Students’ Responses to Algebraic Questions based on their Correctness and Relevance

Automated grading processes helps in saving time for teachers, which can be devoted to more instruction and other essential services. Behavioral analysis of ML models on students’ responses to algebraic questions solve problems that exist with traditional manual grading methods thereby easing the manual assessment process. The study involves the application of machine learning classifiers on the 384 dimensional feature extracted using MathBERT and 768 dimensional features extracted using T5 for each response to the algebraic questions in order to analyse their correctness and relevance. The algorithms are trained over the labeled dataset of a wide variety of algebraic answers and provided with the correct and incorrect answers to learn different patterns. The research aims at analyzing the efficiency of MathBERT and T5, which were trained on LaTeX representations of mathematical expressions and massive text, code and symbols data respectively, in generating embeddings that understand the underlying semantics of mathematical expressions.