Prediction Model on Student Performance based on Internal Assessment using Deep Learning

KOVALEN (Jurnal Riset Kimia)

View Publication Info
 
 
Field Value
 
Title Prediction Model on Student Performance based on Internal Assessment using Deep Learning
 
Creator Hussain, Sadiq; Dibrugarh University
Muhsion, Zahraa Fadhil
Salal, Yass Khudheir
Theodorou, Paraskevi
Kurtoğlu, Fikriye
Hazarika, G. C.
 
Subject Educational Data Mining; Deep Learning; Classification; academically weak students
 
Description Educational Data Mining plays a crucial role in identifying academically weak students of an institute and helps to develop different recommendation system for them. Students from three colleges of Assam, India were considered in our research which their records were run on deep learning using sequential neural model and adam optimization method. The paper compared other classification methods such as Artificial Immune Recognition System v2.0 and Adaboost, to find out the prediction of the results of the students. The highest classification rate was 95.34% produced by the deep learning techniques. The Precision, Recall, F-Score, Accuracy, and Kappa Statistics Performance were calculated as a statistics decisions to find the best classification methods. The dataset used in this paper was 10140 student records. Directing the student for their future plan comes from discovering the hidden patterns by using Data Mining techniques.
 
Publisher kassel university press GmbH
 
Contributor
 
Date 2019-04-30
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://online-journals.org/index.php/i-jet/article/view/10001
10.3991/ijet.v14i08.10001
 
Source International Journal of Emerging Technologies in Learning (iJET); Vol 14, No 08 (2019); pp. 4-22
1863-0383
 
Language eng
 
Relation https://online-journals.org/index.php/i-jet/article/view/10001/5635
 
Rights Copyright (c) 2019 Sadiq Hussain
 

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us

Twitter

Copyright © 2015-2018 Simon Fraser University Library