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learners with maths so they could gain a place on an employment program, so completion was very important (Pitt et al. 2013).

In this case the course design needs to address the ‘problem’ of ­drop-​­out rates. There might be a number of ways of attempting this: by adding in more feedback, using badges to motivate people, creating support structures, supplementing with ­face-­​­to-​­face study groups, breaking longer courses into shorter ones, etc.

Design for Selection

The second design approach is to decide that completion isn’t an important metric. The course designer accepts the MOOC attrition rates in Figure 7 and designs the experience with that in mind.

In this design approach the designer might break away from the linear course model, to allow people to engage in the ‘newspaper’ type selection that Downes refers to. A course might be structured around themes, for instance, and each one around largely independent activities. In this case course completion really doesn’t matter, since learners take what they want.

As a slight aside, it is likely that MOOC completion rates are being defined in such a way that gives them a low output compared with formal education, largely because the manner in which enrolment is defined is so broad. In formal education there are different ways of defining who has enrolled on a course, but it usually allows a c­ooling-​­off period. Students are not counted as being enrolled if they drop out in the first two weeks or fail to turn up at all. So, taking MOOC enrolment figures to be the number who signed up for a MOOC even if they never come into it is always going to give harsh figures. A better figure might be the number of students active after 1 week. This is the baseline figure as those are the students who have actually started the course.