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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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Issue Published : 113
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| Paper Title: | An Optimized Approach for Automated Short Answer Grading using Hybrid Deep Learning Model with PSO |
| Authors Name: | S Ganga , Prof S.Sameen Fatima |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_185449
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| Published Paper Id: | IJRTI2303017 |
| Published In: | Volume 8 Issue 3, March-2023 |
| DOI: | |
| Abstract: | In recent years, research into automatic grading has accelerated significantly. Right now, there has never been a greater need for an effective ASAG system. The start of the pandemic and the switch to online learning have given the research even more momentum. Several methods have been suggested by authors from around the world to resolve the ASAG task. The purpose of this work is to demonstrate how models based on Transfer Learning and optimization methods using swarm intelligence can be utilized to grade short answers. In the proposed research, we introduce a short answer grading system using BERT, BiLSTM, CNN with PSO optimization. To increase the performance of the scoring systems, we optimize the input features using PSO and then given to the hybrid deep learning based model consisting of the BERT, BiLSTM and CNN to classify the answers as correct, partially correct and incorrect. The model is tested using the base line data set i.e., Mohler data set as well as a newly created Computer Science data set in Indian context (CSDSIC). Various experiments are conducted to test the performance of the model. The performance metrics used for evaluating the model are accuracy and root mean squared error. Model shows an accuracy of 92%. |
| Keywords: | Bidirectional Encoder Representation from Transformers (BERT), Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), Particle Swarm Optimization (PSO), Automated Short Answer Grading (ASAG) |
| Cite Article: | "An Optimized Approach for Automated Short Answer Grading using Hybrid Deep Learning Model with PSO", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 3, page no.107 - 113, March-2023, Available :http://www.ijrti.org/papers/IJRTI2303017.pdf |
| Downloads: | 000205176 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2303017
Registration ID:185449
Published In: Volume 8 Issue 3, March-2023
DOI (Digital Object Identifier):
Page No: 107 - 113 Country: Secunderabad, Telangana, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2303017 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2303017 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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