Applied Methods in Learning Analytics

Catalog description

This course provides a broad overview of the field of learning analytics and education data science. This course covers a wide range of concepts, skills, technologies, and applications critical for understanding and leveraging educational data. Students will explore key principles and methods in data pre-processing, storage, inferential and predictive analytics, supervised and unsupervised machine learning, association rule mining, data visualization, social network analysis, text analytics, and prompt engineering. The course emphasizes the practical application of these methods to real-world educational problems, focusing on enhancing learning outcomes and informing pedagogical design. It is recommended that students have a basic knowledge of statistics (equivalent to undergraduate level of introductory course on statistics) and are comfortable with some basic programming. This course prepares students for advanced studies and careers in educational data science, equipping them with the knowledge and skills needed to improve teaching and learning through data-driven insights.