Recent Trends in Answer Script Evaluation – A Literature Survey

DOI

10.2991/ahis.k.210913.014How to use a DOI?

Keywords

Answer Script Grading, Machine Learning (ML), Natural Language Processing (NLP)

Abstract

Assessment of answer scripts is an integral part of an examination and education system. A fair, consistent, unbiased, and correct valuation ensures the integrity of an examination system and is important for all education institutions. Since manual valuation is cumbersome and can be biased or influenced by the perception/mood of the evaluator, automatic grading of scripts has become very relevant. Automatic short answer grading (ASAG) techniques have been widely researched in the last decade and have assumed increased relevance because of online teaching and examinations during the Covid-19 pandemic. This review paper focuses on the recent works in the area of automatic answer grading and compares the techniques, methodologies employed, and the consequent results to evaluate their effectiveness. It discusses the advantages and limitations of the techniques by systematically categorizing the questions into both long/short as well as open-ended/close-ended questions and suggests a new model for improving the grading outcomes.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - A.K.R Maya
AU  - Javed Nazura
AU  - B. L Muralidhara
PY  - 2021
DA  - 2021/09/13
TI  - Recent Trends in Answer Script Evaluation – A Literature Survey
BT  - Proceedings of the 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021)
PB  - Atlantis Press
SP  - 105
EP  - 112
SN  - 2589-4900
UR  - https://doi.org/10.2991/ahis.k.210913.014
DO  - 10.2991/ahis.k.210913.014
ID  - Maya2021
ER  -