Deep Learning Models for Short Answer Scoring
Saurav Kumar
, Ahtesham Farooqui , Sachin Sahu
NLP, Deep Learning, Short Answer Scoring (SAS) Task, Educational Assessment, Automated Scoring.
Automated scoring of descriptive answers is a critical component in educational assessment, leveraging advancements in Natural Language Processing (NLP). Recent years have witnessed substantial growth in NLP, primarily attributed to the transformative impact of deep learning. This research explores the application of deep learning techniques, emphasizing their efficacy in automated scoring, particularly within the realm of short answer scoring tasks. In this study, we systematically compare various common deep learning models for the Short Answer Scoring (SAS) task. The outcomes shed light on the strengths and weaknesses of these models, providing valuable insights for the advancement of automated scoring systems in educational settings.
"Deep Learning Models for Short Answer Scoring", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 1, page no.625 - 630, January-2024, Available :https://ijsdr.org/papers/IJSDR2401089.pdf
Volume 9
Issue 1,
January-2024
Pages : 625 - 630
Paper Reg. ID: IJSDR_209898
Published Paper Id: IJSDR2401089
Downloads: 000347187
Research Area: Computer Science & Technology
Country: Nalanda, Bihar, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
