MIT Researchers Develop Model To Predict MOOC Dropouts

Researchers from the Massachusetts Institute of Technology have developed a model that aims to predict when students will drop out of a massive open online course (MOOC).

The model, presented at last week's Conference on Artificial Intelligence in Education, was trained on data from one course and is designed to apply to a wide range of other courses. "The prediction remains fairly accurate even if the organization of the course changes, so that the data collected during one offering doesn't exactly match the data collected during the next," according to a news release.

The study was conducted by 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.

"There's a known area in machine learning called transfer learning, where you train a machine-learning model in one environment and see what you have to do to adapt it to a new environment," said Veeramachaneni, in a prepared statement. "Because if you're not able to do that, then the model isn't worth anything, other than the insight it may give you. It cannot be used for real-time prediction."

Veeramachaneni and Boyer began by compiling a list of variables such as amount of time spent per correct homework item and amount of time spent on learning resources such as video lectures.

"Next, for each of three different offerings of the same course, they normalized the raw values of those variables against the class averages," according to information released by MIT. "So, for instance, a student who spent two hours a week watching videos where the class average was three would have a video-watching score of 0.67, while a student who spent four hours a week watching videos would have a score of 1.33."

That normalized data was then fed to a machine-learning algorithm that looked for correlations between the data and dropouts, referred to as "stopouts" in the MOOC world. Uncovered correlations were used to predict stopouts in the next two course offerings, with the process then repeated for the second course offering.

To improve the already fairly accurate model from there, Veeramachaneni and Boyer then employed importance sampling, which matched students in subsequent offerings of the course with students who most closely matched their variables in a previous offering, giving greater importance to students more closely matched.

That improved the accuracy, but not dramatically.

Moving forward, the team is working on tweaking the weight given to variables and looking to add more for the algorithm to work with.

"One of the variables that I think is very important is the proportion of time that students spend on the course that falls on the weekend," Veeramachaneni said in a news release. "That variable has to be a proxy for how busy they are. And that put together with the other variables should tell you that the student has a strong motivation to do the work but is getting busy. That's the one that I would prioritize next."

About the Author

Joshua Bolkan is contributing editor for Campus Technology, THE Journal and STEAM Universe. He can be reached at jbolkan@gmail.com.

Featured

  • person typing on a touch screen schedule plan calendar

    2025 Tech Tactics in Education Conference Agenda Announced

    Registration is free for this fully virtual May 7 event, focused on "Thriving in the Age of AI" in K-12 and higher education.

  • Three cubes of noticeably increasing sizes are arranged in a straight row on a subtle abstract background

    A Sense of Scale

    Gardner Campbell explores the notion of scale in education and shares some of his own experience "playing with scale" — scaling up and/or scaling down — in an English course at VCU.

  • From Fire TV to Signage Stick: University of Utah's Digital Signage Evolution

    Jake Sorensen, who oversees sponsorship and advertising and Student Media in Auxiliary Business Development at the University of Utah, has navigated the digital signage landscape for nearly 15 years. He was managing hundreds of devices on campus that were incompatible with digital signage requirements and needed a solution that was reliable and lowered labor costs. The Amazon Signage Stick, specifically engineered for digital signage applications, gave him the stability and design functionality the University of Utah needed, along with the assurance of long-term support.

  • laptop and fish hook

    Security Firm Identifies Generative AI 'Vishing' Attack

    A new report from Ontinue's Cyber Defense Center has identified a complex, multi-stage cyber attack that leveraged social engineering, remote access tools, and signed binaries to infiltrate and persist within a target network.