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.
