SPRAG: a Short Programming Related Answer Grading Dataset

SPRAG: a Short Programming Related Answer Grading Dataset | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article SPRAG: a Short Programming Related Answer Grading Dataset Sridevi Bonthu, S Rama Sree, M. H.M. Krishna Prasad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2195588/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Automated Short Answer Grading (ASAG) is one of the most stud-ied applications of NLP in the education domain. Most of the work in this area focuses on natural language responses. The main contribution of this work is the building and benchmarking of the Short Program-ming Related Answer Grading dataset. The corpus contains questions and answers taken from python programming subjects involving symbols, keywords, and no grammar. We propose data collection, preprocessing, and annotating methods that enable the creation of the corpus. This paper also provides an initial analysis of the corpus. A range of pre-trained sentence-transformer models are evaluated on the dataset for both binary and multi-class classification. Our best fine-tuned model yields an accuracy of 86.16% on binary classification and 56.11% on multiclass classification. We find that fine-tuning the pre-trained models results in the best performance for binary classification. The dataset is freely available to the public. ASAG Corpus Autograding Programming NLP BERT Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2195588","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":263701703,"identity":"9c4cc324-b608-4ec2-a1cc-b34bb051503c","order_by":0,"name":"Sridevi Bonthu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYFACxgYJBgYgusF8AMiTkCFFC1sCSAsPUfZIgMkbPAYgirAW+dmHG298bLNg4Lvd8/nVjRoLHgb2w0c34NNicC6x2XJmmwSD5J2z26xzjgEdxpOWdgOvFh7GNmmeMxIMBjdytxnnsAG1SPCY4dUi3wPU8gesJeeZcc4/IrQwnAFqYagAa2F+nNtGhBaDM4zNlj0VEjySd46ZMef2SfCwEfKLfA/7wxs/DOrk+G43P/6c861Ojp/98DH8DoMCUHSwgSOIjRjlMMD8gRTVo2AUjIJRMHIAAI+yREeXp8VaAAAAAElFTkSuQmCC","orcid":"","institution":"Vishnu Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Sridevi","middleName":"","lastName":"Bonthu","suffix":""},{"id":263701704,"identity":"988c5a14-754f-45e2-a636-ebfc1284f0b5","order_by":1,"name":"S Rama Sree","email":"","orcid":"","institution":"Aditya Engineering College","correspondingAuthor":false,"prefix":"","firstName":"S","middleName":"Rama","lastName":"Sree","suffix":""},{"id":263701705,"identity":"121e7dba-d622-4f03-8d31-26e05f31edeb","order_by":2,"name":"M. 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