Deep Learning Techniques for Automatic Short Answer Grading Article Swipe
Parmar Ashishkumar Jagdishbhai -
,
Nikunj Gamit
,
Jaydev K. Dave
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.71097/ijsat.v16.i2.5299
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.71097/ijsat.v16.i2.5299
Grading brief, subjective responses in classrooms is a labor-intensive and frequently uneven process, especially where distance learning and large-scale online courses are involved. Automated grading systems hold out the prospect of resolving this problem, easing the burden on educators without compromising on consistency and objectivity. This dissertation examines the application of deep learning methods—namely Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and sophisticated transformer models like BERT and its variants—to improve the accuracy and efficiency of Automatic Short Answer Grading (ASAG). The study is done on the Mohler dataset, which contains a rich set of student answers for grading. By using these models on this dataset, the research seeks to enhance semantic comprehension, grading accuracy, and model generalization. The performance of every model is tested on this particular dataset, giving insights into the strengths and weaknesses of every method for ASAG tasks. This work advances the creation of scalable, automated marking systems that are applicable across multiple educational settings towards enabling personalized learning and increasing the efficiency of high-stakes assessment.
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Metadata
- Type
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- Language
- en
- Landing Page
- https://doi.org/10.71097/ijsat.v16.i2.5299
- https://www.ijsat.org/papers/2025/2/5299.pdf
- OA Status
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4410850425Canonical identifier for this work in OpenAlex
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https://doi.org/10.71097/ijsat.v16.i2.5299Digital Object Identifier
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Deep Learning Techniques for Automatic Short Answer GradingWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-05-18Full publication date if available
- Authors
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Parmar Ashishkumar Jagdishbhai -, Nikunj Gamit, Jaydev K. DaveList of authors in order
- Landing page
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https://doi.org/10.71097/ijsat.v16.i2.5299Publisher landing page
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https://www.ijsat.org/papers/2025/2/5299.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://www.ijsat.org/papers/2025/2/5299.pdfDirect OA link when available
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Grading (engineering), Computer science, Artificial intelligence, Natural language processing, Deep learning, Machine learning, Engineering, Civil engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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