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.
