Prediction of Students’ Performance Based on the Hybrid IDA‐SVR Model

Abstract

Students’ performance is an important factor for the evaluation of teaching quality in colleges. The aim of this study is to propose a novel intelligent approach to predict students’ performance using support vector regression (SVR) optimized by an improved duel algorithm (IDA). To the best of our knowledge, few research studies have been developed to predict students’ performance based on student behavior, and the novelty of this study is to develop a new hybrid intelligent approach in this field. According to the obtained results, the IDA-SVR model clearly outperformed the other models by achieving less mean square error (MSE). In other words, IDA-SVR with an MSE of 0.0089 has higher performance than DT with an MSE of 0.0326, SVR with an MSE of 0.0251, ANN with an MSE of 0.0241, and PSO-SVR with an MSE of 0.0117. To investigate the efficacy of IDA, other parameter optimization methods, that is, the direct determination method, grid search method, GA, FA, and PSO, are used for a comparative study. The results show that the IDA algorithm can effectively avoid the local optima and the blindness search and can definitely improve the speed of convergence to the optimal solution.

Hyperparameter Optimization of Support Vector Regression Algorithm using Metaheuristic Algorithm for Student Performance Prediction

“…Naumovich Vapnik in 1995 [19]. SVR shows good performance in solving regression problems [11]. SVR applies the Structural Risk Minimization (SRM) method, which is a method with a focus on finding the optimal hyperplane and minimizing errors from the training data and incentive loss function, resulting in a continuous and real-value data output [20].…”

“…As for solving regression problems, SVM is better known as Support Vector Regression (SVR) [10]. SVR has good generalization ability, can be implemented for non-linear data with high dimensions, and has low computational complexity [11]. In addition, other advantages of SVR are overcoming overfitting and making predictions with data that is not too large [12].…”

“…In addition, other advantages of SVR are overcoming overfitting and making predictions with data that is not too large [12]. From these advantages, SVR can be implemented in this study to predict student performance [11]. Problems often experienced by SVR occur in large-scale data, thus making significant computational processes challenging to determine optimal hyperparameter values [11] [13].…”

Research on Abnormal Behavior Prediction by Integrating Multiple Indexes of Student Behavior and Text Information in Big Data Environment

“…Yu et al [44] analyzed the decision tree of the single classification algorithm and the random forest (RF) of the ensemble learning algorithm and constructed an online student achievement prediction model by using the RF algorithm. Xu et al [45] proposed a novel hybrid IDA-SVRbased model to predict student performance, it is an improved decision algorithm (IDA) to optimize support vector regression (SVR). Maksimova et al [46] combined the decision tree and rule model to establish a classification rule set and constructed a learning behavior diagnosis model combining a decision tree and deep neural network, but this model is limited by factors such as lack of data and imbalance.…”

“…The first method is the MTLTR-APP method [10], which is a multitask prediction method based on a learning ranking algorithm, used for learning performance prediction; the second method is CW-LSTM [43], which is a CW-LSTM algorithm based on deep learning theory for prediction; the third method is Bayesian [57], which is a method suitable for small-scale data and can handle multiclassification tasks. The fourth method is Deep Neural Network (DNN) [58], its feature vector reaches the output layer through hidden layer transformation, and the classification result is obtained from the output layer; the fifth method is random forest (RF) [44], random forest is used for learning performance prediction; the sixth method is IDA-SVR [45], an improved decision algorithm (IDA) to optimize support vector regression (SVR), which is a classification method that finds a regression plane so that all data of a set are closest to the plane.…”