P. Prabhu¹, P. Valarmathie^2,*, K. Dinakaran³

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2989-3005, 2023, DOI:10.32604/iasc.2023.028659

Abstract Supportive learning plays a substantial role in providing a quality education system. The evaluation of students’ performance impacts their deeper insight into the subject knowledge. Specifically, it is essential to maintain the baseline foundation for building a broader understanding of their careers. This research concentrates on establishing the students’ knowledge relationship even in reduced samples. Here, Synthetic Minority Oversampling TEchnique (SMOTE) technique is used for pre-processing the missing value in the provided input dataset to enhance the prediction accuracy. When the initial processing is not done substantially, it leads to misleading prediction accuracy. This research concentrates on modelling an efficient… More >

Liu Zhang^1,*, Yi-Fei Chen^1,2, Zi-Quan Pei¹, Jia-Wei Yuan², Nai-Qiao Tang¹

Journal on Artificial Intelligence, Vol.4, No.1, pp. 15-26, 2022, DOI:10.32604/jai.2022.027730

Abstract Analyzing and predicting the learning behavior data of students in blended teaching can provide reference basis for teaching. Aiming at weak generalization ability of existing algorithm models in performance prediction, a BP neural network is introduced to classify and predict the grades of students in the blended teaching. L2 regularization term is added to construct the BP neural network model in order to reduce the risk of overfitting. Combined with Pearson coefficient, effective feature data are selected as the validation dataset of the model by mining the data of Chao-Xing platform. The performance of common machine learning algorithms and the… More >

Akram M. Radwan^a,b, Zehra Cataltepe^a,c

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 777-783, 2018, DOI:10.1080/10798587.2017.1337673

Abstract This paper proposes to apply machine learning techniques to predict students’ performance on two real-world educational data-sets. The first data-set is used to predict the response of students with autism while they learn a specific task, whereas the second one is used to predict students’ failure at a secondary school. The two data-sets suffer from two major problems that can negatively impact the ability of classification models to predict the correct label; class imbalance and class noise. A series of experiments have been carried out to improve the quality of training data, and hence improve prediction results. In this paper,… More >