Techniques of randomized testing of massively open online courses (MOOCs) involve generating independent A/B tests on the plurality of individual sections of a MOOC. Along these lines, a MOOC may have many learning modules, with many students enrolled in the MOOC. A course instructor may wish to experiment with different variations of course content in order to discover whether any such variations may improve the MOOC. Rather than perform a single A/B test during the MOOC to obtain results for which the course instructor would have to wait weeks, the instructor submits variations of various individual learning modules of the MOOC to a A/B testing server. The A/B testing server may then assign students in each lecture to different versions of a learning module. The A/B testing server may also evaluate the results of the testing in order to provide a recommendation about the MOOC as a whole.