MOOClets: A framework for dynamic experimentation and personalization

Abstract

Randomized experiments in online educational environments are ubiquitous as a scientific method for investigating learning and motivation, but they rarely improve educational resources and produce practical benefits for learners. We suggest that tools for experimentally comparing resources are designed primarily through the lens of experiments as a scientific methodology, and therefore miss a tremendous opportunity for online experiments to serve as engines for dynamic improvement and personalization. We present the MOOClet requirements specification to guide the implementation of software tools for experiments to ensure that whenever alternative versions of a resource can be experimentally compared (by randomly assigning versions), the resource can also be dynamically improved (by changing which versions are presented), and personalized (by presenting different versions to different people). The MOOClet specification was used to implement DEXPER, a proof-of-concept web service backend that enables dynamic experimentation and personalization of resources embedded in frontend educational platforms. We describe three use cases of MOOClets for dynamic experimentation and personalization of motivational emails, explanations, and problems.