Full text

Turn on search term navigation

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Educational data generated through various platforms such as e-learning, e-admission systems, and automated result management systems can be effectively processed through educational data mining techniques in order to gather highly useful insights into students’ performance. The prediction of student performance from historical academic data is a highly desirable application of educational data mining. In this regard, there is an urgent need to develop an automated technique for student performance prediction. Existing studies on student performance prediction primarily focus on utilizing the conventional feature representation schemes, where extracted features are fed to a classifier. In recent years, deep learning has enabled researchers to automatically extract high-level features from raw data. Such advanced feature representation schemes enable superior performance in challenging tasks. In this work, we examine the deep neural network model, namely, the attention-based Bidirectional Long Short-Term Memory (BiLSTM) network to efficiently predict student performance (grades) from historical data. In this article, we have used the most advanced BiLSTM combined with an attention mechanism model by analyzing existing research problems, which are based on advanced feature classification and prediction. This work is really vital for academicians, universities, and government departments to early predict the performance. The superior sequence learning capabilities of BiLSTM combined with attention mechanism yield superior performance compared to the existing state-of-the-art. The proposed method has achieved a prediction accuracy of 90.16%.

Details

Title
Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network
Author
Bashir Khan Yousafzai 1   VIAFID ORCID Logo  ; Sher Afzal Khan 1 ; Rahman, Taj 2 ; Khan, Inayat 3 ; Ullah, Inam 4   VIAFID ORCID Logo  ; Ateeq Ur Rehman 5   VIAFID ORCID Logo  ; Baz, Mohammed 6 ; Hamam, Habib 7   VIAFID ORCID Logo  ; Cheikhrouhou, Omar 8   VIAFID ORCID Logo 

 Department of Computer Science, Abdul Wali Khan University, Mardan 23200, Pakistan; [email protected] (B.K.Y.); [email protected] (S.A.K.) 
 Department of Computer Science, Qurtuba University of Science and Information Technology, Peshawar 25000, Pakistan; [email protected] 
 Department of Computer Science, University of Buner, Buner 19290, Pakistan; [email protected] 
 College of Internet of Things (IoT) Engineering, Changzhou Campus, Hohai University (HHU), Nanjing 213022, China 
 Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan; [email protected] 
 Department of Computer Engineering, College of Computer and Information Technology, Taif University, Taif 21994, Saudi Arabia; [email protected] 
 Faculty of Engineering, Moncton University, Moncton, NB E1A3E9, Canada; [email protected] 
 CES Laboratory, National School of Engineers of Sfax, University of Sfax, Sfax 3038, Tunisia; [email protected] 
First page
9775
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
20711050
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2571538208
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.