{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T01:44:30Z","timestamp":1780969470860,"version":"3.54.1"},"reference-count":31,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T00:00:00Z","timestamp":1687305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research at King Khalid University","award":["R.G.P.2\/549\/44"],"award-info":[{"award-number":["R.G.P.2\/549\/44"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Automated scoring systems have been revolutionized by natural language processing, enabling the evaluation of students\u2019 diverse answers across various academic disciplines. However, this presents a challenge as students\u2019 responses may vary significantly in terms of length, structure, and content. To tackle this challenge, this research introduces a novel automated model for short answer grading. The proposed model uses pretrained \u201ctransformer\u201d models, specifically T5, in conjunction with a BI-LSTM architecture which is effective in processing sequential data by considering the past and future context. This research evaluated several preprocessing techniques and different hyperparameters to identify the most efficient architecture. Experiments were conducted using a standard benchmark dataset named the North Texas Dataset. This research achieved a state-of-the-art correlation value of 92.5 percent. The proposed model\u2019s accuracy has significant implications for education as it has the potential to save educators considerable time and effort, while providing a reliable and fair evaluation for students, ultimately leading to improved learning outcomes.<\/jats:p>","DOI":"10.3390\/bdcc7030122","type":"journal-article","created":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T02:30:51Z","timestamp":1687314651000},"page":"122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Empowering Short Answer Grading: Integrating Transformer-Based Embeddings and BI-LSTM Network"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6847-2632","authenticated-orcid":false,"given":"Wael H.","family":"Gomaa","sequence":"first","affiliation":[{"name":"Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni Suef 62511, Egypt"},{"name":"Faculty of Computer Science, 6th of October Campus, MSA University, Giza 12566, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdelrahman E.","family":"Nagib","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, 6th of October Campus, MSA University, Giza 12566, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mostafa M.","family":"Saeed","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, 6th of October Campus, MSA University, Giza 12566, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7556-958X","authenticated-orcid":false,"given":"Abdulmohsen","family":"Algarni","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, King Khalid University, Abha 61421, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3729-5079","authenticated-orcid":false,"given":"Emad","family":"Nabil","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia"},{"name":"Faculty of Computers and Artificial Intelligence, Cairo University, Giza 12613, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Saeed, M.M., and Gomaa, W.H. (2022, January 8\u20139). An Ensemble-Based Model to Improve the Accuracy of Automatic Short Answer Grading. Proceedings of the 2022 2nd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), Cairo, Egypt.","DOI":"10.1109\/MIUCC55081.2022.9781737"},{"key":"ref_2","unstructured":"Sawatzki, J., Schlippe, T., and Benner-Wickner, M. (2021, January 18\u201320). Deep Learning Techniques for Automatic Short Answer Grading: Predicting Scores for English and German Answers. Proceedings of the 2021 2nd International Conference on Artificial Intelligence in Education Technology, Dali, China."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Saha, S., Dhamecha, T.I., Marvaniya, S., Sindhgatta, R., and Sengupta, B. (2018, January 23\u201330). Sentence Level or Token Level Features for Automatic Short Answer Grading?: Use Both. 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