The Maturing of the MOOC

The Maturing of the MOOC
interactions with learning systems. In this context, the MOOC, combined with machine
learning technology, is an enabler of insight on and progress in personalised human
learning.
The argument from technology by Cooper and Sahami tends to suggest that the legacy of
online learning established in the decades prior to the dawn of MOOCs, is the essential
foundation of the affordances of the new format.
MOOCs and data analytics
A MOOC operator’s perspective on data and measurement comes from Diana Oblinger,
editor of the 2012 Educause report From Metrics to Analytics, Reporting to Action:
Analytics’ Role in Changing the Learning Environment written by Linda Baer and John
Campbell 91 . Oblinger adopts a twenty-year perspective on how tools have transformed
data-driven industries from metrics to analytics. She affirms University education is also
subject to this change, under pressure from learners and funders to demonstrate
quantitative improvements in outcomes. Oblinger argues that:
“Leading the institution from metrics to analytics and reporting to action will require
a significant institutional shift […] to develop analytics or “actionable intelligence” in
all institutional areas—particularly in learning analytics.”
Her discussion references several ways in which analytics will significantly improve
success for all students, including simplifying the registration process; accelerating the
time to degree completion; blocking schedules with fixed, predictable classroom meeting
times; forming peer support and learning networks; embedding remediation into the
regular curriculum; and reducing time in the classroom through the use of online
technologies.
Analytics therefore herald a disruptive innovation and a break with current practice and will
deliver new ways to serve the student, faculty, and administrative users in radically
different ways. Innovations to be expected from growing analytics capability in the
Academy include, according to Oblinger,
• Utilising “social” data to better understand student integration into campus. As the
use of social media continues to increase, one could imagine mapping social
connections to determine which students are having difficulty with connecting to the
institution.
• Emergence of adaptive learning. If efforts to use analytics to predict success proved
fruitful, the next significant step would be to use analytics to power adaptive
systems that adapt to the learner’s needs based on behaviours of the individual as
well as of past students’ patterns.
• Mapping to interventions. Analytics can link suggested interventions to the use and
impact of the interventions. If the intervention suggested utilising the “math help
91 From Metrics to Analytics, Reporting to Action: Analytics’ Role in Changing the Learning Environment
written by Linda Baer and John Campbell
94