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Automatic and Enhanced Reflexive System for Concise Answer Evaluation Using BERT Model and Bi-LSTM
Abstract
In intelligent tutoring systems (ITS), the automatic and enhanced reflexive system for concise answer evaluation (AERSCAE) components that evaluate students' responses to questions are crucial. However, applying deep learning to AERSCAE encounters difficulties due to the challenges of accurately scoring short answers and the scarcity of training data. Using bidirectional encoder representations from transformers (BERT)-based deep neural network, this system improves short-answer texts' understanding via capturing contextual nuances. A specialized semantic refinement layer, bidirectional long short-term memory (Bi-LSTM), is integrated into the AERSCAE system to enhance its performance. Bi-LSTM's temporal dependencies and bidirectional processing enable the generation of high-precision scores. Integrating BERT and Bi-LSTM deep learning architectures into AERSCAE systems enhances their short-answer evaluation capability.
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