Online education is certainly not new. But what about classes with tens of thousands of students, participating from locations all over the world? That’s MOOC (Massive Online Open Education) – and it’s opening new doors for data analytics.
For example: Last fall Stanford University offered three free computer science courses online, and about 100,000 enrolled in each one. With so much data available, analyzing student answers on assignments and tests revealed patterns that would never have been noticed in conventional classes.
In one course, 5,000 test-takers gave exactly the same wrong answer to a question, producing a statistical blip big enough to reveal a point of potential confusion in the course material.
Daphne Koller, a professor in the Stanford Artificial Intelligence Laboratory, explains in a recent interview that “the availability of these really large amounts of data provides us with insights into how people learn, what they understand, what they don’t understand, what are the factors that cause some students to get it and others not that is unprecedented, I think, in the realm of education.” In turn, this information can be used to improve instruction at every level, in every class size.
MOOC is one of the hottest topics in higher education today – and it offers significant opportunities for big data management and analytics. MOOC students interact extensively online, not only with the course material, but also with each other, using social network groups, microblogging, social bookmarking systems, and much more. So students produce a huge number of “data trails” that can yield invaluable information.
In their article about analytics in learning and education, researchers George Siemens and Phil Long offer this assessment: “Undoubtedly, analytics and big data have a significant role to play in the future of higher education. The growing role of analysis techniques and technologies in government and business sectors affirms this trend. In education the value of analytics and big data can be found in their role in guiding reform activities in higher education, and how they can assist educators in improving teaching and learning.”
But, they continue: “Using analytics requires that we think carefully about what we need to know and what data is most likely to tell us what we need to know. Continued growth in the amount of data creates an environment in which new or novel approaches are required to understand the patterns of value that exist within the data.”
Siemens and Long provide a good introduction to this fast-developing area, and explain the similarities and differences between business analytics, academic analytics, and learning analytics. For links to more papers on analytics in higher education, check out this post from the blog Learning and Knowledge Analytics.