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Please use this identifier to cite or link to this item: http://ir.futminna.edu.ng:8080/jspui/handle/123456789/27538
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dc.contributor.authorNusa, Aisha Muhammad K.-
dc.contributor.authorBashir, Sulaimon Adebayo-
dc.contributor.authorAdepoju, Solomon-
dc.date.accessioned2024-04-27T23:20:20Z-
dc.date.available2024-04-27T23:20:20Z-
dc.date.issued2023-03-
dc.identifier.citation31. Nusa, A.M.K, Bashir, S.A., & Adepoju, S. (2023). AN LSTM AND BiLSTM MODELS FOR AUTOMATED SHORT ANSWER GRADING: AN INVESTIGATIVE PERFORMANCE ASSESSMENT. Proceedings of the 4th International Engineering Conference Federal University of Technology, Minna Minna, Nigeriaen_US
dc.identifier.urihttp://repository.futminna.edu.ng:8080/jspui/handle/123456789/27538-
dc.description.abstractAutomated Short Answer Grading (ASAG) systems contributes immensely in providing prompt feedback to students which eases the workload of instructors. In this paper, the performance of two deep learning models (LSTM and BiLSTM) were investigated to ascertain their effectiveness in grading short answers. The popular ASAG dataset by Mohler was utilized for the experiment. The dataset contains training samples from Computer Science department with grades between 0-5. The results show that LSTM model performs better interms of training time with lower RMSE and MAPE when compared with BiLSTM.en_US
dc.language.isoenen_US
dc.publisher4th International Engineering Conference Federal University of Technology, Minna Minna, Nigeria.en_US
dc.subjectAutomated Short Answer Grading; Bidirectional LSTM; Deep learning; LSTMen_US
dc.titleAn LSTM And BiLSTM Models for Automated Short Answer Grading: An Investigative Performance Assessmenten_US
dc.typeArticleen_US
Appears in Collections:Computer Science

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