The Discrete Optimization MOOC: An Exploration in Discovery-Based Learning
The practice of discrete optimization involves modeling and solving complex problems which have never been encountered before and for which no universal computational paradigm exists.
Teaching such skills is challenging: students must learn not only the core technical skills, but also an ability to think creatively in order to select and adapt a paradigm to solve the problem at hand.
This paper explores the question of whether the teaching of such creative skills translates to Massive Open Online Courses (MOOCs). It first describes a discovery-based learning methodology for teaching discrete optimization, which has been successful in the classroom for over fifteen years. It then evaluates the success of a MOOC version of the class via data analytics enabled by the wealth of information produced in the MOOC.