An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism

The students’ performance prediction (SPP) problem is a challenging problem that managers face at any institution. Collecting educational quantitative and qualitative data from many resources such as exam centers, virtual courses, e-learning educational systems, and other resources is not a simple t...

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Autores principales:	Thaer Thaher, Atef Zaguia, Sana Al Azwari, Majdi Mafarja, Hamouda Chantar, Anmar Abuhamdah, Hamza Turabieh, Seyedali Mirjalili, Alaa Sheta
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	educational data mining (EDM) student performance Whale Optimization Algorithm (WOA) feature selection Sine Cosine Algorithm (SCA) ADASYN Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/8f1120013f07457589ffae2729e3c4c4
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spelling	oai:doaj.org-article:8f1120013f07457589ffae2729e3c4c42021-11-11T15:16:55ZAn Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism10.3390/app1121102372076-3417https://doaj.org/article/8f1120013f07457589ffae2729e3c4c42021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10237https://doaj.org/toc/2076-3417The students’ performance prediction (SPP) problem is a challenging problem that managers face at any institution. Collecting educational quantitative and qualitative data from many resources such as exam centers, virtual courses, e-learning educational systems, and other resources is not a simple task. Even after collecting data, we might face imbalanced data, missing data, biased data, and different data types such as strings, numbers, and letters. One of the most common challenges in this area is the large number of attributes (features). Determining the highly valuable features is needed to improve the overall students’ performance. This paper proposes an evolutionary-based SPP model utilizing an enhanced form of the Whale Optimization Algorithm (EWOA) as a wrapper feature selection to keep the most informative features and enhance the prediction quality. The proposed EWOA combines the Whale Optimization Algorithm (WOA) with Sine Cosine Algorithm (SCA) and Logistic Chaotic Map (LCM) to improve the overall performance of WOA. The SCA will empower the exploitation process inside WOA and minimize the probability of being stuck in local optima. The main idea is to enhance the worst half of the population in WOA using SCA. Besides, LCM strategy is employed to control the population diversity and improve the exploration process. As such, we handled the imbalanced data using the Adaptive Synthetic (ADASYN) sampling technique and converting WOA to binary variant employing transfer functions (TFs) that belong to different families (S-shaped and V-shaped). Two real educational datasets are used, and five different classifiers are employed: the Decision Trees (DT), k-Nearest Neighbors (k-NN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and LogitBoost (LB). The obtained results show that the LDA classifier is the most reliable classifier with both datasets. In addition, the proposed EWOA outperforms other methods in the literature as wrapper feature selection with selected transfer functions.Thaer ThaherAtef ZaguiaSana Al AzwariMajdi MafarjaHamouda ChantarAnmar AbuhamdahHamza TurabiehSeyedali MirjaliliAlaa ShetaMDPI AGarticleeducational data mining (EDM)student performanceWhale Optimization Algorithm (WOA)feature selectionSine Cosine Algorithm (SCA)ADASYNTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10237, p 10237 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	educational data mining (EDM) student performance Whale Optimization Algorithm (WOA) feature selection Sine Cosine Algorithm (SCA) ADASYN Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	educational data mining (EDM) student performance Whale Optimization Algorithm (WOA) feature selection Sine Cosine Algorithm (SCA) ADASYN Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Thaer Thaher Atef Zaguia Sana Al Azwari Majdi Mafarja Hamouda Chantar Anmar Abuhamdah Hamza Turabieh Seyedali Mirjalili Alaa Sheta An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism
description	The students’ performance prediction (SPP) problem is a challenging problem that managers face at any institution. Collecting educational quantitative and qualitative data from many resources such as exam centers, virtual courses, e-learning educational systems, and other resources is not a simple task. Even after collecting data, we might face imbalanced data, missing data, biased data, and different data types such as strings, numbers, and letters. One of the most common challenges in this area is the large number of attributes (features). Determining the highly valuable features is needed to improve the overall students’ performance. This paper proposes an evolutionary-based SPP model utilizing an enhanced form of the Whale Optimization Algorithm (EWOA) as a wrapper feature selection to keep the most informative features and enhance the prediction quality. The proposed EWOA combines the Whale Optimization Algorithm (WOA) with Sine Cosine Algorithm (SCA) and Logistic Chaotic Map (LCM) to improve the overall performance of WOA. The SCA will empower the exploitation process inside WOA and minimize the probability of being stuck in local optima. The main idea is to enhance the worst half of the population in WOA using SCA. Besides, LCM strategy is employed to control the population diversity and improve the exploration process. As such, we handled the imbalanced data using the Adaptive Synthetic (ADASYN) sampling technique and converting WOA to binary variant employing transfer functions (TFs) that belong to different families (S-shaped and V-shaped). Two real educational datasets are used, and five different classifiers are employed: the Decision Trees (DT), k-Nearest Neighbors (k-NN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and LogitBoost (LB). The obtained results show that the LDA classifier is the most reliable classifier with both datasets. In addition, the proposed EWOA outperforms other methods in the literature as wrapper feature selection with selected transfer functions.
format	article
author	Thaer Thaher Atef Zaguia Sana Al Azwari Majdi Mafarja Hamouda Chantar Anmar Abuhamdah Hamza Turabieh Seyedali Mirjalili Alaa Sheta
author_facet	Thaer Thaher Atef Zaguia Sana Al Azwari Majdi Mafarja Hamouda Chantar Anmar Abuhamdah Hamza Turabieh Seyedali Mirjalili Alaa Sheta
author_sort	Thaer Thaher
title	An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism
title_short	An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism
title_full	An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism
title_fullStr	An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism
title_full_unstemmed	An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism
title_sort	enhanced evolutionary student performance prediction model using whale optimization algorithm boosted with sine-cosine mechanism
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8f1120013f07457589ffae2729e3c4c4
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An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism

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