Analytics Provides Better Online Learning - InformationWeek

InformationWeek is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

Data Management // Big Data Analytics
02:06 PM
Connect Directly

Analytics Provides Better Online Learning

MIT researchers are using analytics to identify a way to predict when students are likely drop online courses.

Supercomputers Unleash Big Data's Power
Supercomputers Unleash Big Data's Power
(Click image for larger view and slideshow.)

Millions of people have joined MOOCs -- massive open online courses -- but only a small fraction of these students end up earning certificates of completion. According to educational researcher Katy Jordan, the average completion rate for MOOCs is about 15%.

To help understand why online learners fail to follow through -- which matters to educators, online course designers, policymakers, students, and organizations paying for worker training -- Kalyan Veeramachaneni, a research scientist at MIT's Computer Science and Artificial Intelligence Laboratory, and Sebastien Boyer, a graduate student in MIT's Technology and Policy Program, have developed a technique that can help predict when students will drop an online learning course (an event they call "stopout").

The researchers' paper, "Transfer Learning for Predictive Models in Massive Open Online Courses," was presented last week at the International Conference on Artificial Intelligence in Education, held in Madrid.

While those offering online courses can and do employ various real-time analytic techniques to understand student behavior in a specific course, Veeramachaneni and Boyer focused on transfer learning: Applying data from previous courses to refine a predictive model that can be used in any course. The research represents a way to generalize analytic models Veeramachaneni described last year.

(Image: edX)

(Image: edX)

"Through this study, we are taking the first steps toward understanding different situations in which one can transfer models/data samples from one course to another," the paper states.

In an email, Veeramachaneni explained that the research can help the way educators intervene to encourage learners with reminders, motivational messages, and other personalized feedback. "Because predictive models we develop also give a measure of how confident we are in our prediction, it helps to prioritize such interventions," he said. "At a macro level, it can identify a pattern -- for example, perhaps after a certain video/homework quite a number of learners are disengaging with the course and are likely to stopout/drop."

[Read how IBM and other researchers are looking to create a smarter lake.]

Veeramachaneni said that one of the biggest predictors of "stopout" is "pre-deadline submission time," the length of time between when a student begins work on problems and the deadline. Another valuable predictor is time spent working on weekends. Lack of weekend work on assignments may indicate that a student is busy and less likely to complete the course.

Predictive data of this sort, Veeramachaneni said, has potential to increase feedback from learners, by identifying when to send surveys that will receive a response. Those who drop courses become less likely to respond to surveys as time passes, he said.

Thomas Claburn has been writing about business and technology since 1996, for publications such as New Architect, PC Computing, InformationWeek, Salon, Wired, and Ziff Davis Smart Business. Before that, he worked in film and television, having earned a not particularly useful ... View Full Bio

We welcome your comments on this topic on our social media channels, or [contact us directly] with questions about the site.
Comment  | 
Print  | 
More Insights
InformationWeek Is Getting an Upgrade!

Find out more about our plans to improve the look, functionality, and performance of the InformationWeek site in the coming months.

10 Things Your Artificial Intelligence Initiative Needs to Succeed
Lisa Morgan, Freelance Writer,  4/20/2021
Tech Spending Climbs as Digital Business Initiatives Grow
Jessica Davis, Senior Editor, Enterprise Apps,  4/22/2021
Optimizing the CIO and CFO Relationship
Mary E. Shacklett, Technology commentator and President of Transworld Data,  4/13/2021
White Papers
Register for InformationWeek Newsletters
Current Issue
Planning Your Digital Transformation Roadmap
Download this report to learn about the latest technologies and best practices or ensuring a successful transition from outdated business transformation tactics.
Flash Poll