Abstract
<p>We studied student learning in the MOOC 8.MReV Mechanics ReView, run on the edX.org open source platform. We studied learning in two ways. We administered 13 conceptual questions both before and after instruction, analyzing the results using standard techniques for pre- and posttesting. We also analyzed each week’s homework and test questions in the MOOC, including the pre- and posttests, using item response theory (IRT). This determined both an average ability and a relative improvement in ability over the course. The pre- and posttesting showed substantial learning: The students had a normalized gain slightly higher than typical values for a traditional course, but significantly lower than typical values for courses using interactive engagement pedagogy. Importantly, both the normalized gain and the IRT analysis of pre- and posttests showed that learning was the same for different cohorts selected on various criteria: level of education, preparation in math and physics, and overall ability in the course. We found a small positive correlation between relative improvement and prior educational attainment. We also compared homework performance of MIT freshmen taking a reformed on-campus course with the 8.MReV students, finding them to be considerably less skillful than the 8.MReV students.</p>
Highlights
The recent release of hundreds of free online courses in MOOCs by organizations such as Coursera, edX, and Udacity has been so dramatic that an article in the New York Times proclaimed 2012 the “Year of the MOOC” (Pappano, 2012)
We report an initial study of learning in a MOOC, 8.MReV – Mechanics ReView – offered from June 1 to August 27, 2013 on the open source platform edX.org
To incorporate multiple attempts (IRT), we modeled student ability with an extension of item response theory that accounts for ordered response categories, not just binary responses
Summary
The recent release of hundreds of free online courses in MOOCs (massive open online courses) by organizations such as Coursera, edX, and Udacity has been so dramatic that an article in the New York Times proclaimed 2012 the “Year of the MOOC” (Pappano, 2012). The key feature of the ReView course was that faculty and staff interact with two-person groups of students to help them focus on problem solving using our modeling applied to problem solving pedagogy (Pawl, Barrantes, & Pritchard, 2009) This in-class work required preparing the students for class, a need that RELATE met by developing an online eText and assigning pre-class homework at different levels of difficulty. For the IRT analysis we included only the 1,080 students who attempted more than 50% of the questions in the course, 95% of whom earned certificates Most of those completing less than 50% of the homework and quiz problems dropped out during the course and did not take the posttest, so their learning could not be measured. Most items only needed three or four attempts to accurately model student behavior
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