Mohammad Javad Shayegan^*, Rosa Akhtari

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1251-1272, 2024, DOI:10.32604/csse.2024.052587 - 13 September 2024

Abstract After the spread of COVID-19, e-learning systems have become crucial tools in educational systems worldwide, spanning all levels of education. This widespread use of e-learning platforms has resulted in the accumulation of vast amounts of valuable data, making it an attractive resource for predicting student performance. In this study, we aimed to predict student performance based on the analysis of data collected from the OULAD and Deeds datasets. The stacking method was employed for modeling in this research. The proposed model utilized weak learners, including nearest neighbor, decision tree, random forest, enhanced gradient, simple Bayes, 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 - 16 May 2022

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 More >