Please use this identifier to cite or link to this item:
http://ir.futminna.edu.ng:8080/jspui/handle/123456789/28089| Title: | An LSTM and BILSTM Models for Automated Short Answer Grading: An Investigative Per-formance Assessment |
| Authors: | Nusa, A..M Bashir, S.A. Adepoju, Solomon Adelowo |
| Keywords: | Automated Short Answer Grading Bidirectional LSTM Deep learning; LSTM |
| Issue Date: | Mar-2023 |
| 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 |
| URI: | http://repository.futminna.edu.ng:8080/jspui/handle/123456789/28089 |
| Appears in Collections: | Computer Science |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| AN_LSTM_AND_BiLSTM_MODELS_FOR_AUTOMATED_SHORT_ANSWER_GRADING__AN_INVESTIGATIVE_PERFORMANCE_ASSESSMENT.pdf | 881.87 kB | Adobe PDF | View/Open |
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