Optimising Peer Marking with Explicit Training: from Superficial to Deep Learning

Abstract

[EN] We describe our use of formative assessment tasks measuring superficial learning as explicit training forpeer assessment of a major summative assessment task (report writing), which requires deep learning. COMP1710 at the Australian National University is a first year Web Development and Design course done by over 100 students each year, by many Computing students in their first semester of their first year or at any time prior to graduation; the course also attracts some 25% of its cohort from other academic areas of the University. We found that formative assessment trained peer markers performing a surface learning task can produce peer marks consistent with our expert summative task marker. Weaker students only demonstrating superficial learning were able to reliably assess the reports of the better students capable of the deeper learning required to produce the reports.This significantly increasesthe usefulness of peer marking, and could have use in large online courses such as MOOCs.