TELKOMNIKA Telecommunication, Computing, Electronics and Control
Recent systematic review on student performance prediction using backpropagation algorithms
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Recent systematic review on student performance prediction using backpropagation algorithms
Recent systematic review on student performance prediction using backpropagation algorithms
Subject
Data preprocessing, Deep learning, Deep neural network, Students’ performance, Systematic review
Description
A comprehensive systematic study was carried out in order to identify various deep learning methods developed and used for predicting student academic performance. Predicting academic performance allows for the implementation of various preventive and supportive measures earlier in order to improve academic performance and reduce failure and dropout rates. Although machine learning schemes were once popular, deep learning algorithms are now being investigated to solve difficult predictions of student performance in larger datasets with more data attributes. Deep neural network prediction methods with clear modelling and parameter measurements formulated on publicly available and recognised datasets are the focus of the research. Widely used for academic performance prediction, backpropagation algorithms have been trained and tested with various datasets, especially those related to learning management systems (LMS) and massive open online
courses (MOOC). The most widely used prediction method appears to be the standard artificial neural network approach. The long-short-term memory (LSTM) approach has been reported to achieve an accuracy of around 87 percent for temporal student performance data. The number of papers that study and improve this method shows that there is a clear rise in deep learning-based academic performance prediction over the last few years.
courses (MOOC). The most widely used prediction method appears to be the standard artificial neural network approach. The long-short-term memory (LSTM) approach has been reported to achieve an accuracy of around 87 percent for temporal student performance data. The number of papers that study and improve this method shows that there is a clear rise in deep learning-based academic performance prediction over the last few years.
Creator
Edi Ismanto, Hadhrami Ab Ghani, Nurul Izrin Md Saleh, Januar Al Amien, Rahmad Gunawan
Source
DOI: 10.12928/TELKOMNIKA.v20i3.21963
Publisher
Universitas Ahmad Dahlan
Date
June 2022
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Edi Ismanto, Hadhrami Ab Ghani, Nurul Izrin Md Saleh, Januar Al Amien, Rahmad Gunawan, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Recent systematic review on student performance prediction using backpropagation algorithms,” Repository Horizon University Indonesia, accessed November 21, 2024, https://repository.horizon.ac.id/items/show/4339.
Recent systematic review on student performance prediction using backpropagation algorithms,” Repository Horizon University Indonesia, accessed November 21, 2024, https://repository.horizon.ac.id/items/show/4339.