Posner Lecture: The Online Revolution: Learning without Limits – We are at the cusp of a major transformation in higher education. In the past year, we have seen the advent of MOOCs – massively open online classes (MOOCs) – top-quality courses from the best universities offered for free. These courses exploit technology to provide a real course experience to students, including video content, interactive exercises with meaningful feedback, using both auto-grading and peer-grading, and rich peer-to-peer interaction around the course materials. We now see MOOCs from dozens of top universities, offering courses to millions of students from every country in the world. The courses start from bridge/gateway courses all the way through graduate courses, and span a range of topics including computer science, business, medicine, science, humanities, social sciences, and more. In this talk, I’ll discuss this far-reaching experiment in education, including some examples and preliminary analytics. I’ll also discuss why we believe this model can support an improved learning experience for on-campus students, via blended learning, and provide unprecedented access to education to millions of students around the world.
Optimizing Instructional Policies – Psychologists are interested in developing instructional policies that boost student learning. An instructional policy specifies the manner and content of instruction. For example, in the domain of concept learning, a policy might specify the nature of exemplars chosen over a training sequence. Traditional psychological studies compare several hand-selected policies, e.g., contrasting a policy that selects only difficult-to-classify exemplars with a policy that gradually progresses over the training sequence from easy exemplars to more difficult (known as fading). We propose an alternative to the traditional methodology in which we define a parameterized space of policies and search this space to identify the optimum policy. For example, in concept learning, policies might be described by a fading function that specifies exemplar difficulty over time. We propose an experimental technique for searching policy spaces using Gaussian process surrogate-based optimization and a generative model of student performance. Instead of evaluating a few experimental conditions each with many human subjects, as the traditional methodology does, our technique evaluates many experimental conditions each with a few subjects. Even though individual subjects provide only a noisy estimate of the population mean, the optimization method allows us to determine the shape of the policy space and identify the global optimum, and is as efficient in its subject budget as a traditional A-B comparison. We evaluate the method via two behavioral studies, and suggest that the method has broad applicability to optimization problems involving humans in domains beyond the educational arena.