Deep Learning Model For Predicting The Risk Of Learning Loss In Primary School Students: Systematic Literature Review
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Abstract
This research aims to examine the application of deep learning models in predicting the risk of learning loss in elementary school students using a Systematic Literature Review (SLR) approach. With increasing challenges in the world of education, especially due to external factors such as the pandemic and inequality in access to education, artificial intelligence-based methods are needed that are able to accurately identify learning loss risk patterns. This research method is carried out by collecting, analyzing, and synthesizing various studies from leading academic databases to evaluate the effectiveness of deep learning models, such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP), in detecting learning loss. The research results show that deep learning models have high potential in predicting learning loss with a good level of accuracy, although there are still challenges in implementation, such as the availability of quality data and model complexity. Therefore, this research highlights the importance of developing a deep learning-based system that is more adaptive and integrated with the educational environment in order to increase the effectiveness of interventions against learning loss in elementary school students.
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References
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