Feedback
By Lew Ludwig
If nothing else, I am a grading machine. Students turn in homework every week except for testing weeks in each of my classes – ten weeks of homework, four tests, and a final exam. Yes. I am a grading machine. Or, a retired colleague often said, “They pay me to grade; I teach for free. “But do I necessarily want “grading machine” on my epitaph?
Let’s dig a bit deeper here. Why so much feedback, or better yet – what is feedback? Broadly defined, feedback is “information given to students about their performance that guides future behavior” (Ambrose et al., 2010, p. 125). Effective feedback tells students “what they are or are not understanding, where their performance is going well or poorly, and how they should direct their subsequent efforts” (Ambrose et al., 2010, p. 137). Feedback can take many forms. One way to classify feedback is corrective, epistemic, suggestive, and epistemic + suggestive (Leibold and Schwarz, 2015).
Roughly speaking, corrective feedback is just that, what did you get right and what did you get wrong? As a grading machine, I could quickly determine if something was right – okay, this looks correct: five out of five – or if something went wrong – forgot parenthesis here, so the negative one did not distribute, causing the final answer to be wrong: four out of five. This was even quicker for tests. When I first started, if a student left a question blank, I would assign zero out of five and move on. They could look things up in the solution key.
How was this type of feedback serving my student? Not very well. It was not surprising that students were not “learning from their mistakes.” They often repeated similar errors throughout the course. They would look at the score at the top of the sheet, quickly count up the assigned points to make sure it matched my sum, then move on— with no thought or reflection on the experience. While my process was efficient, it did not provide them with helpful feedback or encourage them to learn from the feedback.
Returning to the types of feedback, epistemic feedback asks students to think more deeply about their work. “Could you say more about x?” Whereas suggestive feedback gives students advice on how to improve upon their work. “This would be clearer if you used [this idea] as an example.” According to Leibold and Schwarz, the combination of epistemic + suggestive feedback can have the most significant impact on learners.
While I am still exploring ways to provide epistemic + suggestive feedback in my computational math courses, I’ve had better luck with this approach in proof-based courses. For example, make comments and suggestions to students’ proofs, which were scored out of five points. However, I do not provide a score on the first round of feedback, just my comments. The students reflect on the comments for 48 hours, then decided if they want to take the final grade based on my feedback, or make revisions based on the feedback and be graded on the second draft. Not surprisingly, very few students ask for their scores. Those who do generally earned a 4.5 or higher.
The students who made revisions for a second draft took the process more seriously. For these students, I would grade the new draft with the same rubric, then provide them with their first draft and second draft scores. For a second draft, a four out of five was more realistic and palatable, considering the first draft score was a two out of five. Yes, this feedback process has also slowed my grade inflation. Moreover, the second round of grading goes much quicker as the solutions are greatly improved.
More recently, in my computational classes, I have tried video grading. Students submit their first homework as a PDF. I then create a voice-over video, grading the assignment. While the grading time is about the same, students get to hear my thinking as I work through their questions. Now, instead of just marking something as five out of five, I can praise the student, saying “I like what you did here. This is a good example, or [content idea] demonstrates [content idea] well – nicely done!” Moreover, if there is a slight issue that might reoccur, I can say something like, “Okay, I see what you did here, but you need to do [this] next time. I will not count off this time, but I expect to see this [idea] corrected for the next homework.” I also video grade the take-home portions of my tests and give students suggestions for improving on the next cumulative test.
Before you rush off to try this on your own, consider what is available to you. Our learning management system (LMS) allows me to collect and create the videos for the submitted work. Before this system, I had to download the PDFs to my tablet, which was connected to my desktop, which was used to record the video, which I then had to send to each student. Yeah, that run-on sentence was intentional, it took some hoop-jumping to make it work. Hopefully, your LMS will allow us to do all of this from within the LSM environment.
The upside
Students are more engaged with the feedback process.
Students are using my feedback to learn from their mistakes and improve on subsequent assignments.
I can provide more holistic feedback, emphasizing what they did correctly and things that still need work.
The downside
Not all students watch the videos or take full advantage of the written feedback.
This type of feedback takes more time, diminishing my title as a grading machine.
Still learning from my misstakes mistakes.
What can the student do?
What can’t the student do?
How does the student’s work compare with that of others?
How can the student do better?
https://ctl.columbia.edu/resources-and-technology/resources/feedback-for-learning/
Corrective
Epistemic
Suggestive
Epistemic + Suggestive