Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

<Previous Article In Volume
Next Article In Volume>

Automatic Grading of Answer Sheets using Machine Learning Techniques

Authors
Kasarapu Ramani1, *, Guggilla Uma Maheswari2, Kattamanchi Prem Krishna2, Sagabala Venkata Meghashyam2, Komirisetty Venkata Pavan Kumar2, Yuvaraj Duraiswamy3
1Professor& Head, Department of CSE (DS), Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupathi, India
2UG Scholar, Department of Computer Science and Systems Engineering, Sree Vidyankethan Engineering College, Tirupati, India
3Professor, Department of Computer Science, Chan University, Duhok, Iraq
*Corresponding author. Email: ramanidileep@yahoo.com
Corresponding Author
Kasarapu Ramani
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_27How to use a DOI?
Keywords
NLP,Machine learning; Naïve Bayes; OCR; XGBoost,. Ridge; Regression and Performance Metrics
Abstract

Automating the grading process for question-answer sheets represents a significant challenge, particularly when dealing with traditional hard copy papers. This initiative aims to reduce the time and expenses associated with manual grading, a task that typically consumes 2–3 days for teachers to complete. Leveraging advanced Natural Language Processing (NLP) and Machine Learning (ML) methodologies, including XGBoost, Ridge Regression, and Naive Bayes, we have developed a system for automatic grading using prepossessed OCR datasets. This system learns from a historical dataset of student question-answers, with a focus on two primary objectives: scoring short-answer questions and providing constructive feedback to students. Additionally, we assess the performance and accuracy of the system using standard evaluation techniques such as Precision, Recall, and F-measure. Our experimental results demonstrate an impressive 89% accuracy in grading student answer sheets.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
978-94-6463-471-6
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_27How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Kasarapu Ramani
AU  - Guggilla Uma Maheswari
AU  - Kattamanchi Prem Krishna
AU  - Sagabala Venkata Meghashyam
AU  - Komirisetty Venkata Pavan Kumar
AU  - Yuvaraj Duraiswamy
PY  - 2024
DA  - 2024/07/30
TI  - Automatic Grading of Answer Sheets using Machine Learning Techniques
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 275
EP  - 284
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_27
DO  - 10.2991/978-94-6463-471-6_27
ID  - Ramani2024
ER  -