Automatic Subjective Answer Evaluation
Vijay Kumari, Prachi Godbole, Yashvardhan Sharma
2023
Abstract
The evaluation of answer scripts is vital for assessing a student’s performance. The manual evaluation of the answers can sometimes be biased. The assessment depends on various factors, including the evaluator’s mental state, their relationship with the student, and their level of expertise in the subject matter. These factors make evaluating descriptive answers a very tedious and time-consuming task. Automatic scoring approaches can be utilized to simplify the evaluation process. This paper presents an automated answer script evaluation model that intends to reduce the need for human intervention, minimize bias brought on by evaluator psychological changes, save time, maintain track of evaluations, and simplify extraction. The proposed method can automatically weigh the assessing element and produce results nearly identical to an instructor’s. We compared the model’s grades to the grades of the teacher, as well as the results of several keyword matching and similarity check techniques, in order to evaluate the developed model.
DownloadPaper Citation
in Harvard Style
Kumari V., Godbole P. and Sharma Y. (2023). Automatic Subjective Answer Evaluation. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-626-2, pages 289-295. DOI: 10.5220/0011656000003411
in Bibtex Style
@conference{icpram23,
author={Vijay Kumari and Prachi Godbole and Yashvardhan Sharma},
title={Automatic Subjective Answer Evaluation},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2023},
pages={289-295},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011656000003411},
isbn={978-989-758-626-2},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Automatic Subjective Answer Evaluation
SN - 978-989-758-626-2
AU - Kumari V.
AU - Godbole P.
AU - Sharma Y.
PY - 2023
SP - 289
EP - 295
DO - 10.5220/0011656000003411