While data from Massive Open Online Courses (MOOCs) offers the potential to gain new insights into the ways in which online communities can contribute to student learning, much of the richness of the data trace is still yet to be mined. In particular, very little work has attempted fine-grained content analyses of the student interactions in MOOCs. Survey research indicates the importance of student goals and intentions in keeping them involved in a MOOC over time.  Automated fine-grained content analyses offer the potential to detect and monitor evidence of student engagement and how it relates to other aspects of their behavior. Ultimately these indicators reflect their commitment to remaining in the course.  As a methodological contribution, in this paper we investigate using computational linguistic models to measure learner motivation and cognitive engagement from the text of forum posts.  We validate our techniques using survival models that evaluate the predictive validity of these variables in connection with attrition over time. We conduct this evaluation in three MOOCs focusing on very different types of learning materials. Prior work demonstrates that participation in the discussion forums at all is a strong indicator of student commitment. Our methodology allows us to differentiate better among these students, and to identify danger signs that a struggling student is in need of support within a population whose interaction with the course offers the opportunity for effective support to be administered.  Theoretical and practical implications will be discussed.
['Miaomiao Wen', 'Diyi Yang', 'Carolyn Penstein Rosé']
Linguistic Reflections of Student Engagement in Massive Open Online Courses