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

Thaer Thaher,Hamouda Chantar,Majdi Mafarja,Hamza Turabieh,Atef Zaguia,Sana Al Azwari,Anmar Abuhamdah,Alaa Sheta,Seyedali Mirjalili

doi:10.3390/app112110237

Abstract

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.

Highlights

Students’ performance prediction (SPP) problem is a common challenge for institutions’ lecturers and decision-makers to develop the best educational strategies for students.To perform such a prediction, several educational parameters can be employed to evaluate the performance of students, such as exams grades, Grade Point Average (GPA), lecture absenteeism, number of attempts to pass a course or an exam
students’ performance prediction (SPP) problems compared with several machine learning (ML) classifiers such as Decision Trees (DT), KNN, Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Random Forest (RF), plus a set of state-of-the-art methods
We examined the effect of re-sampling and feature selection on the performance of several machine learning classifiers

Summary

Introduction

Students’ performance prediction (SPP) problem is a common challenge for institutions’ lecturers and decision-makers to develop the best educational strategies for students. To perform such a prediction, several educational parameters can be employed to evaluate the performance of students, such as exams grades, Grade Point Average (GPA), lecture absenteeism, number of attempts to pass a course or an exam. Examining a vast amount of educational data and extract their impacts on students’ performances is closely related to educational data mining (EDM) and machine learning (ML) algorithms. EDM is a set of data mining methods that tries to extract hidden and valuable information from educational data to expand our understanding of students’ performance and enhance the learning process [3,4]

Methods

Results

Conclusion