Using Pre-course Survey Responses to Predict Sporadic Learner Behaviors in Advanced STEM MOOCs --Work-in-Progress

摘要

Massive Open Online Courses (MOOCs) attract learners with different learner intentions, background knowledge, and skills as compared to traditional, closed enrollment settings. This level of diversity in learners introduces new factors that impact student persistence and engagement. Previous research has analyzed MOOC learners who are either fully engaged in a course in its entirety or at least consistently accessing course materials. Sporadic users, those who access course content randomly, have not been studied as in depth, even though they comprise the largest number of learners in a highly advanced engineering MOOC. In this study, we use clickstream data and pre-survey data to understand sporadic learners in a highly advanced nanoelectronics MOOC. We used a MOOC on nanotechnology with a total enrolment of close of 10,000 users offered for a duration of 8-weeks. We identified that academic preparedness, learner intentions, and expected time commitment could be used to predict sporadic users. Finally, an effect size analysis was performed.