title: SBI-RAG: Enhancing Math Word Problem Solving for Students through
    Schema-Based Instruction and Retrieval-Augmented Generation
summary: 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
paper_authors._id: 6711e3d35550f581fcbf99a6
paper_authors.name: Prakhar Dixit
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-18T08:09:44.032Z
paper_authors._id: 659a0143ac728bc3039a82c5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/659a0143ac728bc3039a82c5/JGf3SXs8AeOqC8i1fRyLu.jpeg
paper_authors.fullname: Prakhar Dixit
paper_authors.type: user
paper_authors.user: pdx97
paper_authors._id: 6711e3d35550f581fcbf99a7
paper_authors.name: Tim Oates
paper_title: SBI-RAG: Enhancing Math Word Problem Solving for Students through
    Schema-Based Instruction and Retrieval-Augmented Generation
paper_summary: 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
paper_ai_summary: A schema-based instruction retrieval-augmented generation framework using LLMs improves reasoning clarity and problem-solving accuracy in math word problems compared to GPT-4 and GPT-3.5 Turbo.
paper_ai_keywords: schema-based instruction large language model SBI-RAG GSM8K dataset reasoning score