Opportunities for Adaptive Experiments to Enable Continuous Improvement
  that Trades-off Instructor and Researcher Incentives
Ilya Musabirov Angela Zavaleta-Bernuy Pan Chen Michael Liut Joseph Jay Williams
Randomized experimental comparisons of alternative pedagogical strategies
could provide useful empirical evidence in instructors' decision-making.
However, traditional experiments do not have a clear and simple pathway to
using data rapidly to try to increase the chances that students in an
experiment get the best conditions. Drawing inspiration from the use of machine
learning and experimentation in product development at leading technology
companies, we explore how adaptive experimentation might help in continuous
course improvement. In adaptive experiments, as different arms/conditions are
deployed to students, data is analyzed and used to change the experience for
future students. This can be done using machine learning algorithms to identify
which actions are more promising for improving student experience or outcomes.
This algorithm can then dynamically deploy the most effective conditions to
future students, resulting in better support for students' needs. We illustrate
the approach with a case study providing a side-by-side comparison of
traditional and adaptive experimentation of self-explanation prompts in online
homework problems in a CS1 course. This provides a first step in exploring the
future of how this methodology can be useful in bridging research and practice
in doing continuous improvement.