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Grading quantitative data analysis in laboratory courses is often inconsistent because each student generates unique experimental data, making it difficult for teaching assistants (TAs) to verify calculations efficiently and accurately. This challenge can result in variable grading and overlooked calculation errors. To address this issue, we developed standardized Excel templates for student data analysis and a Python script that automatically calculates the expected results from each student's experimental data. The script compares the expected values with student submissions, allowing graders to quickly identify discrepancies without manually checking every calculation. This workflow has substantially reduced grading time while improving grading consistency and accuracy across multiple TAs. Students are also allowed to revise and resubmit their analyses after receiving feedback, creating an additional opportunity to identify and correct calculation errors. This approach demonstrates how simple automation can improve assessment quality while supporting student learning in quantitative laboratory courses.
