Computational intelligence approaches have proven to be effective in enhancing online learning systems. Although many studies have been conducted to reveal the learners’ satisfaction in online learning platforms, the use of machine learning in the analysis of big datasets for this aim has rarely been explored. In addition, although the analysis of online reviews on courses has been carried out in other fields, there are very few contributions in the area of online learning platforms. This study, therefore, aims to perform learner satisfaction analysis through the use of machine learning. We develop a new method using text mining and supervised learning techniques with the aid of the ensemble learning approach. A boosting approach, AdaBoost, is used in ANN for ensemble learning to improve its performance. We employ Artificial Neural Network (ANN) approach, dimensionality reduction and Latent Dirichlet Allocation (LDA) for textual data analysis. Principal Component Analysis (PCA) is used for data dimensionality reduction. We perform several experimental evaluations on the big datasets obtained from the online learning platforms. The accuracy and computation time of the proposed method are assessed on the obtained dataset. The method is compared with several machine learning approaches to show its effectiveness in big datasets analysis. The results showed that the method is effective in predicting learners’ satisfaction from online reviews. In addition, the proposed method outperform other classifiers, K-Nearest Neighbor (K-NN), Decision Trees (DT), Support Vector Machines (SVM) and Naïve Bayes (NB), in case of accuracy. The results are discussed and research implications from different perspectives are provided for future developments of educational decision support systems.

计算智能方法已被证明在增强在线学习系统方面是有效的。尽管已经进行了许多研究来揭示学习者对在线学习平台的满意度，但很少有人探索使用机器学习分析大数据集来实现这一目标。此外，虽然其他领域已经开展了对课程在线评论的分析，但在在线学习平台领域的贡献却很少。因此，本研究旨在通过使用机器学习来进行学习者满意度分析。我们借助集成学习方法开发了一种使用文本挖掘和监督学习技术的新方法。在 ANN 中使用增强方法 AdaBoost 进行集成学习以提高其性能。我们采用人工神经网络 (ANN) 方法、降维和潜在狄利克雷分配 (LDA) 进行文本数据分析。主成分分析 (PCA) 用于数据降维。我们对从在线学习平台获得的大数据集进行了几次实验评估。在获得的数据集上评估了所提出方法的准确性和计算时间。该方法与几种机器学习方法进行了比较，以显示其在大数据集分析中的有效性。结果表明，该方法可以有效地从在线评论中预测学习者的满意度。此外，在准确性方面，所提出的方法优于其他分类器、K-最近邻 (K-NN)、决策树 (DT)、支持向量机 (SVM) 和朴素贝叶斯 (NB)。