An LSTM and BILSTM Models for Automated Short Answer Grading: An Investigative Per-formance Assessment

Please use this identifier to cite or link to this item: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/28089

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DC Field	Value	Language
dc.contributor.author	Nusa, A..M	-
dc.contributor.author	Bashir, S.A.	-
dc.contributor.author	Adepoju, Solomon Adelowo	-
dc.date.accessioned	2024-05-07T14:54:25Z	-
dc.date.available	2024-05-07T14:54:25Z	-
dc.date.issued	2023-03	-
dc.identifier.uri	http://repository.futminna.edu.ng:8080/jspui/handle/123456789/28089	-
dc.description.abstract	Automated 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 in terms of training time with lower RMSE and MAPE when compared with BiLSTM	en_US
dc.subject	Automated Short Answer Grading	en_US
dc.subject	Bidirectional LSTM	en_US
dc.subject	Deep learning;	en_US
dc.subject	LSTM	en_US
dc.title	An LSTM and BILSTM Models for Automated Short Answer Grading: An Investigative Per-formance Assessment	en_US
dc.type	Article	en_US
Appears in Collections:	Computer Science

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