TY - GEN
T1 - Natural Language Processing in Automatic Grading of Assessments in Higher Education
T2 - 3rd Pan-African Conference on Artificial Intelligence and Smart Systems Conference, PAAISS 2024
AU - Ofusori, Lizzy
AU - Bokaba, Tebogo
AU - Mhlongo, Siyabonga
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.
PY - 2025
Y1 - 2025
N2 - With the increasing digitization of education and large-scale standardized testing, the use of natural language processing (NLP) for the automatic grading of assessments has surged, leading to the widespread availability and adoption of commercial solutions. Automatic grading is not limited to multiple-choice questions, but also includes essays and short answers, highlighting its potential to automate rapid assessment and feedback in higher education. This study explores various NLP techniques used in automatic grading, investigates research outcomes, identifies recommended NLP approaches, and examines future directions for automated assessment systems. A systematic literature review following PRISMA guidelines was conducted using Scopus data, identifying 32 relevant studies from a pool of 279 peer-reviewed articles. Findings indicate that automatic grading techniques vary based on assessment type and available data. In summary, this study provides an updated overview of NLP applications in assessments, enhancing the understanding of current research and suggesting directions for future research.
AB - With the increasing digitization of education and large-scale standardized testing, the use of natural language processing (NLP) for the automatic grading of assessments has surged, leading to the widespread availability and adoption of commercial solutions. Automatic grading is not limited to multiple-choice questions, but also includes essays and short answers, highlighting its potential to automate rapid assessment and feedback in higher education. This study explores various NLP techniques used in automatic grading, investigates research outcomes, identifies recommended NLP approaches, and examines future directions for automated assessment systems. A systematic literature review following PRISMA guidelines was conducted using Scopus data, identifying 32 relevant studies from a pool of 279 peer-reviewed articles. Findings indicate that automatic grading techniques vary based on assessment type and available data. In summary, this study provides an updated overview of NLP applications in assessments, enhancing the understanding of current research and suggesting directions for future research.
KW - Artificial Intelligence
KW - Assessment
KW - Automatic Grading
KW - Higher Education
KW - Natural Language Processing
UR - https://www.scopus.com/pages/publications/105013283234
U2 - 10.1007/978-3-031-94439-0_5
DO - 10.1007/978-3-031-94439-0_5
M3 - Conference contribution
AN - SCOPUS:105013283234
SN - 9783031944383
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 70
EP - 86
BT - Pan-African Artificial Intelligence and Smart Systems - 3rd Pan-African Conference, PAAISS 2024, Proceedings
A2 - Ngatched, Telex M. N.
A2 - Woungang, Isaac
A2 - Tapamo, Jules-Raymond
A2 - Viriri, Serestina
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 4 December 2024 through 6 December 2024
ER -