<![CDATA[This research delves into the profound application of deep learning to predict student academic performance and facilitate personalized interventions. By analyzing comprehensive data, including grades, attendance records, and student participation, various deep learning architectures—such as Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM)—are employed to uncover subtle patterns indicative of potential learning difficulties. The primary objective of this study is to empower educators and school administrators with predictive insights, enabling them to proactively identify student needs. Targeted interventions, such as personalized academic guidance, relevant emotional support, and appropriate enrichment opportunities, can then be effectively implemented. Nevertheless, this research places crucial emphasis on the careful interpretation of predictive outcomes and vigilance against potential biases within the data. Through the synergy between the analytical power of deep learning and the pedagogical sensitivity of educators, we hope to foster a more inclusive and supportive learning environment, ultimately facilitating the maximum potential development and academic success of every student.]]>
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