SBI-RAG: Enhancing Math Word Problem Solving for Students through
  Schema-Based Instruction and Retrieval-Augmented Generation
Authors: name: Prakhar Dixit, name: Tim Oates

Many students struggle with math word problems (MWPs), often finding it
difficult to identify key information and select the appropriate mathematical
operations.Schema-based instruction (SBI) is an evidence-based strategy that
helps students categorize problems based on their structure, improving
problem-solving accuracy. Building on this, we propose a Schema-Based
Instruction Retrieval-Augmented Generation (SBI-RAG) framework that
incorporates a large language model (LLM).Our approach emphasizes step-by-step
reasoning by leveraging schemas to guide solution generation. We evaluate its
performance on the GSM8K dataset, comparing it with GPT-4 and GPT-3.5 Turbo,
and introduce a "reasoning score" metric to assess solution quality. Our
findings suggest that SBI-RAG enhances reasoning clarity and problem-solving
accuracy, potentially providing educational benefits for students