Macro-level engagement in computer engineering MOOC lectures: comparing a high-rated and a low-rated course

Abstract

Although digital advances have enabled hundreds of universities to offer massive open online courses (MOOCs) to millions of students worldwide, the high dropout rate on these MOOCs indicates a disjunct between the potential of the offer and the success of its uptake. Many studies point to issues relating to interpersonal connection as a major factor in this disjunct. Interviews with students support the conclusion that lecturers’ interpersonal tone is crucial to shortening psychological distance in pre-recorded videos. However, it is unclear how the interpersonal tone preferred by students can be realised discursively. This paper captures discursive instances by modelling an engagement framework and identifying particularly useful strategies through comparison of a high-rated and a low-rated engineering MOOC course delivered on Coursera. Findings show that in the high rated course the lecturer consistently anticipated students’ state of mind and in response constructed a discourse that performed several interpersonal functions. On the other hand, in the low-rated course the lecturer mostly anticipated and addressed knowledge gaps and issues of difficulty, but not students’ emotional responses, and so deployed far fewer interpersonal strategies in the lecture discourse. These findings shed light on how lecturers can use interpersonal language in pre-recorded lectures to improve student engagement.