Engagement in Massive Open Online Courses (MOOCs) is based on students who self-organize their participation according to their own goals and interests. Visual materials such as videos and discussion forums are basic ways of engaging students in MOOCs. Student achievement in MOOCs is typically measured using assessments distributed throughout the course. Although there is research on the basic forms of student’s engagement and assessment in MOOCs, little is known about their effect on students’ achievement in the form of students completing a MOOC. Using binomial logistic regression models, this paper addresses this gap in the literature by presenting the degree to which student engagement with videos and forum posts can predict students’ probability of achievement in a MOOC. It also explores the extent to which participation behaviors and their intention to receive the course certification can be used to predict achievement in MOOCs. Using qualitative content analysis, this paper discusses the quality of the forum posts exchanged by participants in this MOOC. The findings from quantitative analysis support MOOC’s pedagogical assumptions, showing that students’ engagement in forums and with videos increases the probability of course achievement. It also shows that intention to certify plays a moderator effect on the number of videos watched, enhancing achievement in MOOCs. The findings from qualitative analysis reveal that most students’ posts in forums display more information acquisition than critical thinking. Implications for practice suggest MOOC designers and MOOC instructors foster engagement in forums by implementing discussion prompts that foster interactions about deep meaning of concepts or application of concepts covered in the MOOC. In regard to videos, implications for practice suggest the creation of interactive videos that promote students’ engagement and control such as inserting guiding questions and segmenting the video content. Future research comprising multiple MOOC cohorts is suggested to validate the empirical model presented in this study.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications