Deep Learning Based Knowledge Assessment Systems in Education

This study explores deep learning models for automated student knowledge assessment. Using data from 1,480 STEM students, ANN, CNN, LSTM, and a hybrid CNN-LSTM were evaluated. The hybrid model achieved highest accuracy (94.7%), outperforming baselines. Results highlight the effectiveness of combining temporal and static features for adaptive learning systems and early intervention,

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