A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
Polytechnic Students' Academic Performance Prediction Based On Using Deep Neural Network
2021
Asian Journal of Research in Computer Science
Preserved Fulltext
In this study, we have used the deep neural network for predicting students' academic final marks. The main objective of this paper is to improve students' results. ...
This paper also explains how the prediction deep neural network model can be used to recognize the most vital attributes in a student's academic data namely midterm_marks, class_ test, attendance, assignment ...
’ Academic Performance
Prediction Based On Using Deep Neural Network
S. ...
doi:10.9734/ajrcos/2021/v12i430289
fatcat:oyluhadfg5cwdidgobbhygj22i
Applying Deep Learning Neural Networks in Predicting Students' Cumulative Grades
2020
International Journal of Engineering and Advanced Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
This paper has been designed to extract knowledge describing students' performance in the courses required for graduation, in a way that helps academic advisors in providing academic advice and guidance ...
Academic performance is affected by many factors, so it is necessary to predict student performance to determine the difference between students who are excelling in studies and students who need to exert ...
In general, we were able to build a supervised machine learning model using the deep learning neural networks classifier to correctly predict the students' final grade based on their performance on the ...
doi:10.35940/ijeat.b2084.1210220
fatcat:jj6noqh26rec7li32utsx2blwe
Recent systematic review on student performance prediction using backpropagation algorithms
2022
TELKOMNIKA (Telecommunication Computing Electronics and Control)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
A comprehensive systematic study was carried out in order to identify various deep learning methods developed and used for predicting student academic performance. ...
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. ...
Therefore, the paper's main objectives are to conduct a systematic review on the current deep neural network (DNN) models proposed in the literature for predicting students' academic performance and propose ...
doi:10.12928/telkomnika.v20i3.21963
fatcat:tnid5hhyxfbizef27v4yngdeny
Education 4.0 using artificial intelligence for students performance analysis
2020
Inteligencia Artificial
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Hence, in this paper, Hybridized Deep Neural Network (HDNN) to predict student performance in Education 4.0. ...
The deep neural network monitors predict, and evaluate students' performance in an education 4.0 environment. ...
Hybridized Deep Neural Network (HDNN) In this study, Hybridized Deep Neural Network (HDNN) to predict student performance in Education 4.0. ...
doi:10.4114/intartif.vol23iss66pp124-137
fatcat:bz5mgalhzbexjagtwjehtxff5q
CAREER TRACK PREDICTION USING DEEP LEARNING MODEL BASED ON DISCRETE SERIES OF QUANTITATIVE CLASSIFICATION
2021
Applied Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In this paper, a career track recommender system was proposed using Deep Neural Network model. ...
The result of the study shows that the DNN algorithm performs reasonably well in predicting the academic strand of students with a predic-tion accuracy of 83.11%. ...
in providing financial support in attaining the objectives of the study, the National Research Council of the Philippines, and the Digital Transformation Center of STEER HUB Batangas State University for ...
doi:10.23743/acs-2021-29
doaj:c97d20dd0923434caef1e8913bb0d86f
fatcat:vybi3spzkbe7rkbjr6r7n2rqz4
An Approach to Predict a Student's Academic Performance using Recurrent Neural Network (RNN)
2018
International Journal of Computer Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this paper, Recurrent Neural Network (RNN) is used to predict a student's final result. RNN is a variant of neural network that can handle time series data. ...
In this paper, a comparison based study is also made with Artificial Neural ...
[13] proposed a prediction model for students' performance based on data mining methods. ...
doi:10.5120/ijca2018917352
fatcat:jlvntanisrcgngn2ze7m4vsf6u
Predicting Students' Performance in Final Examination using Deep Neural Network
2022
Asian Journal of Research in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
For analysis of their performance, we can use new techniques Deep Learning, Convolution Neural Networks, Data Clustering, Optimization Algorithms, etc. In machine learning. ...
Using Deep Learning, we will predict the student's performance yearly in the form of CGPA and compare that with the real CGPA. A real dataset can boost the prediction performance. ...
So, according to the above analysis, our model deep neural network is the one with the highest accuracy. So we can say that our model is best for predicting students' performance.
Table 3. ...
doi:10.9734/ajrcos/2022/v14i4306
fatcat:ozb4lopilncppjusodzg44xqeu
Deep Neural Network Model for Identification of Predictive Variables and Evaluation of Student's Academic Performance
2021
Revue d'intelligence artificielle : Revue des Sciences et Technologies de l'Information
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
The proposed model uses Deep Neural Network (DNN) in the process of considering the predictive variables and evaluating student performance using the variables. ...
Multiple predictive variables are taken into account for the assessment of student performance while modelling an efficient template for student performance assessment. ...
Student Performance Analysis System (SPAS) Model is compared to the proposed Deep Neural Network for Evaluating Student Performance Assessments. ...
doi:10.18280/ria.350507
fatcat:dsqikzhhjre5do3uokm5de6sj4
Enhance the Educational Outcome in Higher Educational Institutes through Deep Neural Network
2020
International Journal of Advanced Trends in Computer Science and Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Based on the predicted results student can be monitored to improve his/her abilities. To achieve this, this study proposes a deep multilayer feed forward neural network (MFFNN) model. ...
The proposed model is used to predict the performance of every individual for a single subject or a course with high accuracy. ...
In this step deep neural network namely multilayer feed forward neural network (MFFNN) is proposed for model construction that can predict the students' performance and after predicting the performance ...
doi:10.30534/ijatcse/2020/202952020
fatcat:x57wj7hy2zcfdoqtdiebulifwu
UniNet: Next Term Course Recommendation using Deep Learning
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
As these techniques fail to represent the time-dependent nature of academic performance datasets we propose a deep learning approach using recurrent neural networks that aims to better represent how chronological ...
We have shown that it is possible to obtain a performance of 81.10% on AUC metric using only grade information and that it is possible to develop a recommender system with academic student performance ...
It has been studied that recurrent neural networks show an improvement in performance when compared with artificial neural networks for student prediction task [12] . ...
arXiv:2009.09326v1
fatcat:z7qzhday7zhhnnudjepvztby2m
Admission Prediction in Undergraduate Applications: an Interpretable Deep Learning Approach
[article]
2024
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2024; you can also visit the original URL.
The file type is application/pdf.
Other Versions
2023
2023 Fifth International Conference on Transdisciplinary AI (TransAI)
unpublished
doi:10.1109/transai60598.2023.00040 fatcat:apshq34ca5gc7aors4zvbmmgqi
doi:10.1109/transai60598.2023.00040 fatcat:apshq34ca5gc7aors4zvbmmgqi
In this context, we propose deep learning-based classifiers, namely Feed-Forward and Input Convex neural networks, which overcome the challenges faced by the existing methods. ...
Our models achieve higher accuracy compared to the best-performing traditional machine learning-based approach by a considerable margin of 3.03\%. ...
Figure 2 : 2 Figure 2: Confusion matrices for fully-trained deep learning models, (a) Feed-Forward neural network; (b) Input convex neural network. ...
arXiv:2401.11698v1
fatcat:xq6pg2sl7vdx7lrahjz3kazuxm
Predicting Students' Academic Performance in Educational Data Mining Based on Deep Learning Using TensorFlow
2020
International Journal of Education and Management Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The study was aimed to create a predictive model for predicting students' academic performance based on a neural network algorithm. ...
In the current study, neural network (NN) classification algorithm is implemented to create a predictive model in predicting academic performance of students in a particular academic institution by using ...
In this study, a predictive model is created based on neural network (NN) classification algorithm in predicting academic performance of students by using students' behavioral characteristics and their ...
doi:10.5815/ijeme.2020.06.04
fatcat:rijvfgk33jhadkhlipqbyspode
Early educational performance prediction a deep learning approach
2022
Journal of Science & Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
The data is then fed into a deep neural network to predict students' performance of the 4th year based on their previous years' scores. A promising result of 77% accuracy is achieved. ...
Another challenge for educational performance prediction is that every educational system is complex and unique. Hence, to make a prediction, it often requires a deep understanding of the system. ...
Hence, proposed ANN remains the best model for the particular dataset. ...
doi:10.57001/huih5804.65
fatcat:vvsyeedvubgwfh2n5iabgtwznq
Analysis of Student Academic Performance through Expert systems
2020
International Research Journal on Advanced Science Hub
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
These computing-related techniques and approaches are mainly machine learning techniques, deep learning techniques, Artificial Neural Networks & Neural Networks Convolution, etc. ...
Predicting the performance of students is one of the most important topics required for learning contexts such as colleges and universities, as it helps to design successful mechanisms that boost tutorial ...
A Survey has been done on many Deep Learning based algorithms such as Artificial Neural Networks, Machine Learning, Support Vector Machines , Convolution Neural Network etc to draw conclusions based on ...
doi:10.47392/irjash.2020.158
fatcat:7z2yy6p2rne7bdir7cxnqzsllq
Deep Regressor: Cross Subject Academic Performance Prediction System for University Level Students
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Predicting the academic performance of students has been an important research topic in the Educational field. ...
This paper presents a system which utilizes machine learning techniques to classify and predict the academic performance of the students at the right time before the drop out occurs. ...
GauthamJ Deep Regressor: Cross Subject Academic Performance Prediction System for University level Students Deep Regressor: Cross Subject Academic Performance Prediction System for University Level Students ...
doi:10.35940/ijitee.k1254.09811s19
fatcat:q5276vp54zh2hmgjhdmvt7hnh4
« Previous
Showing results 1 — 15 out of 16,896 results