This workshop is geared towards grad students, instructors, researchers who strive to learn how to compare multiple alternative course content more rapidly, through quantitative, qualitative measures and data analysis from A/B comparisons. After our hands-on workshop, you should be able to understand experimental design that can be applied to enhances real world learning materials, and the use of machine learning algorithms for adaptive experimentation that use data for rapid, continuous improvement.

In the three hours, you will gain knowledge about the value of adaptive A/B experimentation in educational settings, you will be able to have hands-on practice on statistical package and tools for machine learning algorithms for adaptive experimentation.

You will be exposed to case studies and provided with contacts from a collaborative and sustainable community for adaptive experimentation, developed as part of a five-year NSF grant on software and tools for adaptive experimentation (https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209819). The community brings together instructors, education researchers, statisticians, and machine learning researchers. This support will be available after the workshop through wikis, discussion forums, mailing lists, online workshops, and smaller topic-focused group meetings.

Aside from bringing your own laptop, there are no equipment requirements from you. We will ask and strongly recommend that all participants preview the online materials before the workshop, and provide introductions about themselves and the people they would like to connect with.