This advanced course introduces students to state-of-the-art computational methods for communication research. Building on foundational skills, students learn to apply advanced machine learning and natural language processing techniques to analyze large-scale communication data. Topics include deep learning, transformer models, network analysis, and ethical considerations in contemporary computational research.
A comprehensive, open-source guide to machine learning tailored for communication science researchers and practitioners.
This AERA Open special topic concerns the large emerging research area of education data science (EDS). In a narrow sense, EDS applies statistics and computational techniques to educational phenomena and questions. In a broader sense, it is an umbrella for a fleet of new computational techniques being used to identify new forms of data, measures, descriptives, predictions, and experiments in education. Not only are old research questions being analyzed in new ways but also new questions are emerging based on novel data and discoveries from EDS techniques. This overview defines the emerging field of education data science and discusses 12 articles that illustrate an AERA-angle on EDS. Our overview relates a variety of promises EDS poses for the field of education as well as the areas where EDS scholars could successfully focus going forward.