Catalytic assessment: Understanding how MCQs and EVS can foster deep learning

Abstract

One technology for education whose adoption is currently expanding rapidly in UK higher education is that of electronic voting systems (EVS). As with all educational technology, whether learning benefits are achieved depends not on the technology but on whether an improved teaching method is introduced with it. EVS inherently relies on the multiple-choice question (MCQ) format, which many feel is associated with the lowest kind of learning of disconnected facts. This paper, however, discusses several ways in which teaching with MCQs, and so with EVS, has transcended this apparent disadvantage, has based itself on deep learning in the sense of focusing on learning relationships between items rather than on recalling disconnected true-false items, and so has achieved substantial learning advantages. Six possible learning designs based on MCQs are discussed, and a new function for (e-)assessment is identified, namely catalytic assessment, where the purpose of test questions is to trigger subsequent deep learning without direct teaching input.