Renee Carney

Canvas X: On-Track Predictor, August 2018

Blog Post created by Renee Carney Administrator on Aug 6, 2018

Canvas X is where we seek your input on experiments from our secret pandaworks lab!

It's all about starting small, measuring, and learning our way forward!

 

The Volunteer Window has closed.  Watch the CanvasX space of the Canvas Studio for future summaries and opportunities.

 

Based on the feedback from our June-August experiment of the On Track predictor we have decided to make some changes to the model and try another small cohort. We are looking for 3-5 instructors who are interested in providing qualitative feedback about the usefulness and the accuracy of the On Track predictor. The ideal candidates are instructors who:

  • Are willing and available to give direct feedback about their thoughts on the tool semi-regularly (once every two weeks or so)
  • Make use of Canvas widely to teach their courses (use of multiple features like assignments, modules, discussion etc.)
  • Have a course starting in August/September 2018
  • Have a background in statistics or probability (not necessary, just a bonus)

 

For the rest of the community, we would love to open up a dialog about what an On Track predictor (let’s call it OTP) means for Canvas. So what is the OTP and how does it work?

 

What is it? The OTP is a tool to help instructors discover which students are in danger of doing poorly in the course. Every week instructors will receive a report of all of their students that contains:

  • Are they On/Off Track?
  • How confident are we?
  • What are the primary reasons for the prediction?

The intention is that this will enable instructors to intervene with their students earlier and drive better student outcomes.

 

What does it do? The OTP evaluates a student’s likelihood to succeed in a course based on:

  • Academic History: How have they performed overall in courses previous to this one?
  • Course Interactions: How much do they interact with course tools? (grades, messaging, etc.)
  • Content Interactions: How much do they interact with course content (modules, files etc.)
  • Course Performance: How are they performing in their current course?

Using this information, a probability of successfully completing the course is calculated.

 

Given that brief overview we would love to hear your thoughts. How could you foresee using this? What information would you want/not want to see? What are the types of actions you could take to intervene with students? What’s missing?

 

And if you have more questions about the specifics of how the OTP works, members of the team who worked on the project will be monitoring the comments and will make their best effort to answer your questions. So fire away!

Outcomes