Analysis of Cloud Computing-Based Education Platforms Using Unsupervised Random Forest

Cloud computing-based online education has played a vital role in enabling uninterrupted learning during crises such as the COVID-19 pandemic. This study explored the key variables associated with cloud computing that can effectively support the operation of online education platforms. By analyzing real data from 63 online learning platforms, the study utilizes the unsupervised random forest (RF) method, ranking the importance of cloud computing-related variables using the Gini index and permutation importance and Boruta. The study's contributions include the application of innovative techniques such as Addcl1 and K-means clustering for data pre-processing, unsupervised RF dissimilarity calculated by partitioning around medoids (PAM) clustering and multidimensional scaling (MDS) plots, and the comparison of RF accuracy (98.4%) with other machine learning techniques using WEKA software. The results highlight variables such as public cloud, commercial sourcing, course number limitations, and synchronized tools as important factors in the successful implementation of cloud computing-based online learning platforms. The theoretical and practical implications of the study results can be used by researchers and practitioners to structure cloud computing-based online learning platforms that could successfully function for distance learning during crises like the COVID-19 pandemic.