Outcome Predictions of Students in Massive Open Online Courses (OPSMOOC)

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This exploratory project seeks to develop methods to evaluate Massive Open Online Course (MOOC) offerings. Designed specifically for interactive study over the web, with considerable flexibility in terms of students' time commitment and scheduling, MOOCs have tremendous potential for improving access to higher education. To date, however, MOOCs have tended to exhibit high attrition rates. With enrollments in the tens or hundreds of thousands, even modest changes that increase completion rates could have a major impact on the absolute numbers of students benefiting from this new form of instruction. This project involves in-depth examination of a highly popular Harvard MOOC, offered through the edX platform. It combines primary data (on participating students' characteristics and their motivations for taking the course) with data pertaining to students' course activities in analyses (including descriptive statistics, hazard modeling, and multiple linear regression) to explore both traditional course outcomes (e.g., measures of student achievement) and persistence/attrition. By focusing on the metric of course completion early in the development of MOOCs, this project has potential to direct attention to those aspects of MOOCs that appear critical to student success and their continuing interest in STEM fields. It is anticipated that both research findings and methods of analyses can be incorporated into the instructional platform to aid instructors in monitoring difficulties and in rapid course improvement and that progress toward more uniform data collection will allow comparisons across a variety of MOOCs, particularly those with similar goals or content domains.