COMPUTATIONAL RESEARCH METHODS

LIS 501

Introduces computational methods and tools for processing, analyzing, and understanding text data. Topics include text data preparation and preprocessing, models of text content and meaning, exploratory text analytics, text classification, information extraction from texts, ethical issues in natural language processing (NLP), and related applications in information sciences and other fields. Develops practical skills to design and implement text mining solutions using popular NLP tools and programming packages.

EDPSYCH 560

Presents introductory research procedures in the social sciences, including the exploration of theoretical foundations and practical use of basic tools and programs needed for quantitative and qualitative data analysis. Examines how different methodologies can complement or compete with the other, and showcases how pertinent quantitative and qualitative methods are applied in the field of learning analytics with particular emphasis on data about - and therefore issues in - learning environments such as classrooms, online courses, apprenticeships and internships, museum exhibits, after school programs, and other formal and informal educational contexts.

EDPSYCH 525

Application of data mining techniques to large educational datasets to gain important insights into how people learn. Examines the spectrum of prevalent learning analytics methods and applications, from institutional effectiveness, to classroom-level interventions, to standardized assessments, and beyond.

EDPSYCH 360

Examines the practice of data science in educational contexts, examining how we draw meaning and make decisions on the basis of data related to students, teaching, formal education systems, and informal learning environments. Focuses on developing fundamental skills for actively working with and interpreting educational data related to students, teaching, formal education systems, and informational learning environments., including: transforming, combining, and effectively summarizing data; creating meaningful graphical representations of data; creating formal models based on data; and communicating findings from data analysis. Develop skills by writing computer code to create reproducible data analysis reports. After introducing foundational data science skills, work through a series of real-world case studies that provide an opportunity to practice, expand, and further refine the skills and to make connections to subject-matter knowledge in the context of education.

EDPSYCH 505

Logic and methods of statistics used in the field of learning analytics. Emphasizes concepts and application rather than computational details to build fundamentals in the areas of inferential statistics. Reliability and validity, scalability, and the implications of inference in applied statistical methodology.