SBI-RAG: Enhancing Math Word Problem Solving for Students through
  Schema-Based Instruction and Retrieval-Augmented Generation
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
6711e3d35550f581fcbf99a6
Prakhar Dixit
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2024-10-18T08:09:44.032Z
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Prakhar Dixit
user
pdx97
6711e3d35550f581fcbf99a7
Tim Oates
SBI-RAG: Enhancing Math Word Problem Solving for Students through
  Schema-Based Instruction and Retrieval-Augmented Generation
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
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.
schema-based instruction large language model SBI-RAG GSM8K dataset reasoning score