Massive Open Online Courses (MOOCs) use peer assessment to grade open ended questions at scale, allowing students to provide feedback. Relative to teacher based grading, peer assessment on MOOCs traditionally delivers lower quality feedback and fewer learner interactions. We present the identified peer review (IPR) framework, which provides non-blind peer assessment and incentives driving high quality feedback. We show that, compared to traditional peer assessment methods, IPR leads to significantly longer and more useful feedback as well as more discussion between peers.
The teach-learn-assess cycle in education is broken in a typical massive open online course (MOOC). Without formative assessment and feedback, MOOCs amount to information dump or broadcasting shows, not educational experiences. A number of remedies have been attempted to bring formative assessment back into MOOCs, each with its own limits and problems. The most widely applicable approach for all MOOCs to date is to use peer assessment to provide the necessary feedback. However, unmoderated peer assessment results suffer from a lack of credibility. Several methods are available today to improve on the accuracy of peer assessment results. Some combination of these methods may be necessary to make peer assessment results sufficiently accurate to be useful for formative assessment. Such results can also help to facilitate peer learning, online discussion forums, and may possibly augment summative evaluation for credentialing.