GradeSense: Smart Grading for Descriptive Assessments

Authors: Sakshi Dnyaneshwar Kshirsagar, Gauri Arun Kshirsagar, Mayuri Balasaheb Thorat, Gautami Sunil Kadam

Country: India

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Abstract: The traditional process of evaluating subjective (descriptive) type exam papers for large numbers of students is labor-intensive and prone to inconsistencies due to human factors such as evaluator fatigue or mood. This manual evaluation method is also time-consuming, often leading to delays in result processing. In contrast, competitive and entrance exams with objective or multiple-choice questions benefit from automated, machine-based evaluation, which is faster, more accurate, and reduces human errors. However, there is currently no efficient system for automating the evaluation of descriptive answers. To address this challenge, we propose an innovative solution where students’ handwritten answer sheets are scanned and uploaded into the system. Using advanced machine learning and natural language processing techniques, the system processes and evaluates the handwritten content, providing consistent and timely assessment. This automated evaluation system aims to streamline the grading process for educational in stitutions, enhancing efficiency, accuracy, and resource management

Keywords: Subjective Exam Evaluation, Handwritten Answer Sheets, Automation, Machine Learning, Natural Language ProCessing (NLP), Grading Efficiency, Consistency, Educational Technology, Resource Management, Automated Assessment System

Paper Id: 232315

Published On: 2025-03-23

Published In: Volume 13, Issue 2, March-April 2025

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