Instruction that adapts to individual learner characteristics is often more effective than instruction that treats all learners as the same. A practical approach to making MOOCs adapt to learners may be by integrating frameworks for intelligent tutoring systems (ITSs). Using the Learning Tools Interoperability standard (LTI), we integrated two intelligent tutoring frameworks (GIFT and CTAT) into edX. We describe our initial explorations of four adaptive instructional patterns in the PennX MOOC "Big Data and Education." The work illustrates one route to adaptivity at scale.

Learning-by-doing in MOOCs may be enhanced by embedding intelligent tutoring systems (ITSs). ITSs support learning-by-doing by guiding learners through complex practice problems while adapting to differences among learners. We extended the Cognitive Tutor Authoring Tools (CTAT), a widely-used non-programmer tool kit for building intelligent tutors, so that CTAT-built tutors can be embedded in MOOCs and e-learning platforms. We demonstrated the technical feasibility of this integration by adding simple CTAT-built tutors to an edX MOOC, "Big Data in Education." To the best of our knowledge, this integration is the first occasion that material created through an open-access non-programmer authoring tool for full-fledged ITS has been integrated in a MOOC. The work offers examples of key steps that may be useful in other ITS-MOOC integration efforts, together with reflections on strengths, weaknesses, and future possibilities.

Intelligent Tutoring Systems (ITS) can both help improve student learning and serve as useful platforms for experiments in learning science [1,2]. But the difficulty of building or customizing ITSs has hindered their acceptance among educators and researchers [3]. The Cognitive Tutor Authoring Tools (CTAT) project aims to provide a suite of authoring tools that make tutor development more affordable by leveraging human-computer interaction and artificial intelligence techniques. Previous efforts on CTAT added the capability for nonprogrammers to create exampletracing tutors via a programming-by-demonstration technique that requires no coding [4]. While exampletracing tutors provide a student experience similar to that of the more general cognitive tutors, they also require that an author demonstrate and fully annotate each individual problem to be presented.