Students Performance Prediction


Authors : Dona Boby; Megha Madhu; Ann Mary Danty

Volume/Issue : Volume 8 - 2023, Issue 8 - August

Google Scholar : https://bit.ly/3TmGbDi

Scribd : http://tinyurl.com/p52er3tv

DOI : https://doi.org/10.5281/zenodo.8313739

Abstract : Abstract Student performance prediction is an important aspect of education that has gained significance in recent years. Predicting the academic outcomes of students can help educators identify students who are at risk of falling behind and provide them with targeted interventions to improve academic performance. New technologies such as deep learning have revolutionized the way student performance prediction is done. Deep learning algorithms can analyze large amounts of data and identify patterns that would be difficult to detect using traditional statistical methods. In the proposed study, the dataset of students in Portuguese school contains various features such as age, gender, family background, study time, travel time, weekly study time, etc. The deep learning techniques employed in this study include Artificial Neural Networks (ANN), Long Short- Term Memory (LSTM), Convolutional Neural Network(CNN) and Bi-directional LSTM. The performance of these deep learning models was evaluated using metrics such as accuracy, mean squared error (MSE), and mean absolute error (MAE). This study demonstrates the effectiveness of deep learning techniques in predicting student performance and can be used as a basis for developing interventions to improve academic outcomes.

Keywords : Deep Learning;Academic Performance;Early Prediction.

Abstract Student performance prediction is an important aspect of education that has gained significance in recent years. Predicting the academic outcomes of students can help educators identify students who are at risk of falling behind and provide them with targeted interventions to improve academic performance. New technologies such as deep learning have revolutionized the way student performance prediction is done. Deep learning algorithms can analyze large amounts of data and identify patterns that would be difficult to detect using traditional statistical methods. In the proposed study, the dataset of students in Portuguese school contains various features such as age, gender, family background, study time, travel time, weekly study time, etc. The deep learning techniques employed in this study include Artificial Neural Networks (ANN), Long Short- Term Memory (LSTM), Convolutional Neural Network(CNN) and Bi-directional LSTM. The performance of these deep learning models was evaluated using metrics such as accuracy, mean squared error (MSE), and mean absolute error (MAE). This study demonstrates the effectiveness of deep learning techniques in predicting student performance and can be used as a basis for developing interventions to improve academic outcomes.

Keywords : Deep Learning;Academic Performance;Early Prediction.

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