Learning Analytics

Units: 3

As the use of digital resources continues expand in education, an unprecedented amount of new data is becoming available to educational researchers and practitioners. In response, Learning Analytics [LA] has emerged over the past decade as an interdisciplinary field encompassing Learning [e.g. educational technology, learning and assessment sciences], Analytics [e.g. visualization, computer/data sciences], and Human-Centered Design [e.g. usability, participatory design]. This course will provide students with an overview of the field, examples of its use in educational contexts, and applied experience with widely adopted tools and techniques for working with and exploring data. As participants gain experience in the collection, analysis, and reporting of data throughout the course, they will be better prepared help educational organizations understand and improve learning and the contexts in which learning occurs.

Offered in Fall Only

Units: 3

This class is meant to teach the practical side of machine learning for applications in mining educational data. There will be a heavy project focus, and when you have completed the course, you should be fully prepared to attack new problems using machine learning in the field of education.

Offered in Fall Only

Units: 3

This course will provide students with an overview of text mining as an analytical approach in education research, examples of its use in educational contexts, and applied experience with widely adopted tools and techniques [e.g. topic modeling and sentiment analysis]. Students develop practical skills in the collection, analysis, and reporting of text data form sources such as Learning Management Systems, social media, and other online sources. Students can complete projects using a programming approach with R, a popular free open source software program for data science, or using non-programming point-and-click tools [i.e., SAS Visual Text Analytics].

Offered in Spring Only

Units: 3

Although social network analysis and its educational antecedents date back to the early 1900s, the popularity of social networking sites like Twitter and Facebook have raised awareness of and renewed interests in networks and their influence. As the use of digital resources continues expand in education, data collected by these educational technologies has also greatly facilitated the application of network analysis to teaching and learning. This introductory course is designed to prepare researchers and practitioners to apply network analysis in order to better understand and improve student learning and the contexts in which learning occurs. This course will provide students with an overview of social network theory, examples of network analysis in educational contexts, and applied experience with widely adopted tools and techniques. As participants gain experience in the collection, analysis, and reporting of data throughout the course, they will be better prepared help educational organizations understand and improve both

Offered in Spring Only