Early Prediction of Student Performance Using an Activation Ensemble Deep Neural Network Model

In recent years, academic performance prediction has evolved as a research field thanks to its development and exploration in the educational context. Early student performance prediction is crucial for enhancing educational outcomes and implementing timely interventions. Conventional approaches frequently struggle on behalf of the complexity of student profiles as a consequence of single activation functions, which prevent them from effectively learning intricate patterns. In addition, these models could experience obstacles such as the vanishing gradient problem and computational complexity. Therefore, this research study designed an Activation Ensemble Deep Neural Network (AcEnDNN) model to gain control of the previously mentioned challenges. The main contribution is the creation of a credible student performance prediction model that comprises extensive data preprocessing, feature extraction, and an Activation Ensemble DNN. By utilizing various methods of activation functions, such as ReLU, tanh, sigmoid, and swish, the ensembled activation functions are able to learn the complex structure of student data, which leads to more accurate performance prediction. The AcEn-DNN model is trained and evaluated based on the publicly available Student-mat.csv dataset, Student-por.csv dataset, and a real-time dataset. The experimental results revealed that the AcEn-DNN model achieved lower error rates, with an MAE of 1.28, MAPE of 2.36, MSE of 4.55, and RMSE of 2.13 based on a training percentage of 90%, confirming its robustness in modeling nonlinear relationships within student data. The proposed model also gained the minimum error values MAE of 1.28, MAPE of 2.97, MSE of 4.77, and RMSE of 2.18, based on a K-fold value of 10, utilizing the Student-mat.csv dataset. These findings highlight the model’s potential in early identification of at-risk students, enabling educators to develop targeted learning strategies. This research contributes to educational data mining by advancing predictive modeling techniques that evaluate student performance.