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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!


Our June-August Nudge experiment was a success! So now we are opening up the experiment again for a much larger test. Read below for details and signup.


Nudge is a prototype service that helps students effectively manage their time and coursework. This service is the first step in a longer term smart messaging vision for Canvas. When enabled in a course, Nudge sends the following messages to students through Canvas:

  • Upcoming Assignments: An assignment is due in the next 24 hours and the student hasn’t turned it in yet. Prompt them to turn it in / finish.
  • Late Assignments: An assignment deadline has passed and the student has not turned it in. Prompt them to check if they can submit late.
  • Course Checking: The student has not checked the course for more than a week. Prompt them to visit the home page for any updates.
  • Weekly Report: Every week send students a list of assignments/quizzes that are due that week.


Because feedback and data are core to our development process, we are reaching out to the community for this pilot. The current version of Nudge is experimental and in early development; iteration is expected as the experiment progresses.


Any course that enables Nudge will provide us with insight into the effect of “nudging” different students in different ways. We also value feedback about the user experience and effectiveness of these reminders from your perspective as an instructor.

Your course may be a good fit for pilot if it fits the following criteria:

  • Blended or fully online courses
  • History of teaching the same course in prior semesters
  • Have a course beginning August/September 2018
  • Make use of multiple assignments throughout a course
  • English is the primary language of instruction
  • Multiple sections of the same course (nice to have, but not necessary)


Privacy Notice: Canvas is committed to keeping you and your student’s personal information private. All participants will be able to opt out at any time. Any and all use of the data from this experiment will be used to make Canvas a better product and not shared publicly without express prior consent.



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


If you successfully signed up you should get a message in Canvas later this week confirming that you are going to be participating. Nudges will then start being sent next Monday (Aug 27th)!


Have questions? Leave them below.

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!