Delves into measuring learning effects in digital education and analytics, covering research questions, variables, experimental design, and bias solutions.
Explores the behavioral motivation and preprocessing of data for nonlinear transformations in variables, emphasizing the importance of interactions between attributes.
Covers the detection and correction of parameter errors in power grids, focusing on statistical properties, error identification, computational efficiency, sensitivity analysis, and robust state estimation.