Covers feature extraction, clustering, and classification methods for high-dimensional datasets and behavioral analysis using PCA, t-SNE, k-means, GMM, and various classification algorithms.
Explores the impact of skipping activities on student success and the use of transition matrices and learning analytics cubes to predict student states.
