The paper analyzes the accuracy of predicting students' academic performance using various models: traditional statistical (logistic regression, decision trees) and neural network (MLP, LSTM, Transformer). The features of the application of models to the data of the digital journal of academic performance are noted. Based on the results, it is demonstrated that modern neural networks can provide higher prediction accuracy, but simple models remain in demand due to interpretability and stability.
academic performance, learning analytics, forecasting, neural networks, logistic regression, decision trees, LSTM, Transformer
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