As a teacher, I do gather data about my students within the context of the course, and I've written about my data process here: The Value of SMALL Data and Microassignments. I'm not really able to use any of the Canvas data analytics for my purposes because Instructure has zero understanding of how my courses work and what the data mean. The meaninglessness of most of the data that Instructure collects is one of my many concerns about the viability of the whole data analytics project, but that's a topic for a separate post.
What I want to write about here is the use of student data beyond the limitations of a course. My impression is that students do not think about the way an LMS company could be using their data for commercial purposes (not selling it necessarily, but using the data to create new commercial products), and my impression is also that students are not aware of how their own schools are using the data that is collected in the LMS. At least at my school, no information has been provided to faculty members about what happens to our Canvas course data, so I'm assuming that no information has been provided to students either. I know that final-grade data is used for institutional reporting purposes, and that grade data is available in the SIS (we use Banner). But what about all the other data that Canvas records? Is my school using that for its own data analytics projects? I have no idea, and that is very concerning; we need to know.
My hope is that the sudden eruption of data analytics onto the higher ed scene is going to lead to lots of conversations, and I feel that one of my duties as an online instructor is to find out what I can about how our course data is being used and relay that back to the students.
In addition, I also see it as my duty to find out what the students think about that and relay that information about out to the discussions that are starting to take place about this. For example, Cristina Colquhoun is leading discussions with Instructure right now in order to review Instructure's privacy and data use policies and practices. She has done a fantastic job of soliciting feedback from faculty Canvas users via Twitter and social media. That is a great way for her to reach faculty and administrators, but student voices need to be part of that discussion also. So, in order to create a space where students can make their voices heard, I set up an anonymous poll which I shared with my students via the class announcements, and today I wrote up interim results of the poll for Cristina to see before the next Instructure data meeting, which is later this week. You can see the interim results of the poll here: Interim Canvas Poll Results.
I don't want to generalize about the numbers because it is just a small, self-selecting group of students who have filled out the poll. But what I do want to call attention to are the extremely thoughtful comments that the students made in the open-ended questions (as a general rule, it is always the open-ended non-numeric data that carries the most meaning for me personally). One of my teaching mantras is ASK THE STUDENTS. You can't know if you don't ask... and if you do ask, you may learn things you never expected.
To share what I'm hearing from students, I'm going to paste in just a few selections from their comments so far. You can see from the depth and detail of what they wrote that the idea of predictive analytics is one that they are very concerned about, and with good reason (see all comments here). I have separated out the longer comments into separate statements, so some of these comments below come from the same student:
I don't like being under any kind of surveillance. I feel like it's invasive.
Past performance does not equal future performance. This will cause students to fulfill their own prophesies.
If the students have a low prediction, they might feel defeated or unsuccessful in the class before it has even begun. Even if a student has high predictions, they might not try as hard than they would if they weren't given a good prediction.
If people see predictions they treat them as facts and that will bias how they view the students.
personally i have already had alot of issues with teachers and advisors judging my academic decisions, and i feel that allowing them to see my grade predictions would only exacerbate issues.
The most important thing is that the student is given a choice and an easy way to opt out.
My advice would be to NOT use the data, just delete it.
Some people might have gotten bad grades because of external circumstances like working jobs, having children, or suffering from major illnesses. Any data prediction model might not be smart enough or have the right information to account for external factors.
I would NOT want professors or advisors to see this information. I think it would create a bias for professors when grading.
Depending on the student, this could motivate them or depress them. I know for me, if something is telling me I am doing bad, I am going to try to prove it wrong.
People could become discouraged at the predictive analysis and either drop out of a class before trying or feel defeated before it really has begun.
This could bias professors or other educators or advisers as they could form opinions about students before knowing them, or could change their opinions about a former student. Even if we try not to be biased, this information can unconsciously affect our decisions.
If this feature is to be used at all it should include explicit warnings about what the predictions are based on, what limitations the predictions have and what the predictions are to be used for.
If canvas is predicting that you are not going to do too well in a class, then maybe that tells you that you may need to put a little more time an effort into that class that others.
I think the good side of using data in this way is that we would be able to choose classes based on the predictions. But I also think it could be bad because when it comes to classes we have to take, if Canvas predicts we are going to make a bad grade it could get into our heads and cause us to end up doing badly.
I don't want to be put in a box based on past performance.
Don't put me in a box
based on past performance.
