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

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.

Predicting and Comparing Students’ Online and Offline Academic Performance Using Machine Learning Algorithms

“…After gathering, difficulties can arise such as missing data, biased data, imbalanced data. Researchers proposed an enhanced whale optimization algorithm (EWOA) framework [ 25 ] that combined the whale optimization algorithm (WOA) with the sine cosine algorithm (SCA) and logistic chaotic map (LCM) to achieve better accuracy and feature understanding. The original WOA had a strong exploration process but a weak exploitation process due to updating the position vector with a randomly chosen search agent instead of the optimal one.…”

Wild Horse Optimizer-Based Spiral Updating for Feature Selection

“…The Sine cosine algorithm (SCA) [45] is combined with WOA and a chaotic logistic map to produce a new enhanced version of WOA [46]. SCA improves the exploitation process of the traditional WOA, which prevents it from sinking in the optimal local area of the solution.…”