Designing for quality?

Abstract

There are significant complexities in interpreting and measuring quality in MOOCs. In this chapter, we examine experts’ perceptions of how to measure quality in MOOCs, using empirical data we gathered through conversations with MOOC specialists. In their experience, while data can be helpful in understanding quality, the metrics measured are shaped by underpinning assumptions and biases. In conventional education, it is assumed that the learner wants to follow a course pathway and complete a course. However, this assumption may not be valid in a MOOC. Quality data might not capture the underlying goals and intentions of MOOC learners. Therefore, it is difficult to measure whether or not a learner has achieved his or her goals. We stress the need to explore quality metrics from the learner’s point of view and to encompass the variability in motivations, needs and backgrounds, which shape conceptions of quality for individuals.