## Monday, June 9, 2014

One of the components that I use in my class is student presentations.

While I like having students present, I had always a hard time grading the presentations. Plus, many students seemed to target the presentation to me, trying to sound too technical and advanced, leaving the audience in the class bored and uninterested.

For that reason, I adopted a peer-grading scheme. Students have to present to the class, and get rated by the class, and not me. (Although, I still reserve a small degree of editorial judgement for assigning the grades.) Here is how my scheme works, after a few years of experience.
1. Rating scale: Students assign a grade from 0 to 10 to the presentations.
2. No self-grading: Students do not grade their own presentations. (Early on, there were students that were assigning 10 to themselves, and lower grade to everyone else. Now they can still grade themselves if they want but the grade is ignored.)
3. Normalization: All assigned grades are normalized, to have a zero mean and one standard deviation. (This normalization was introduced to fight the problem where a student would try to game the system by assigning low grades to everyone else, hoping to lower the average rating of all other students.)
4. Grade assignment: The presentation grade is the average of the assigned normalized scores. Formally, each student $s_i$ assigns to presentation $t$ a grade $z(s,t)$. The overall grade of the presentation is the mean value $E[z(*,t)]$ of the $z(s_i,t)$ grades.
5. Ensuring careful grading by asking students to estimate class rating: One problem with the peer grading scheme was that many students did not take it seriously enough, and assigned random grades (typically, the same grade to everyone). To avoid indifferent grading, I decided to give credit (~10%) based on the correlation of the assigned grades $z(s,t)$ against the mean value $E[z(*,t)]$ (across all presentations $t$). This ensured that students will at least try to figure out what other students will assign to the presentation, and will not assign random grades.
6. Separate assigned and estimated grades: The problem with introducing the requirement to agree with the class was that some students believed to be better assessors than the rest of the class. So, they felt that their own grade was the correct one, and did not like losing credit for assigning their own "true" grade. To address that issue, I now ask students to assign two grades: their own grade $z_p(s,t)$, and an estimate of the class grade $z_c(s,t)$. The personal grade $z_p$ is used to compute $E(z(*,t)]$ in Step 4, and I use the $z_c$ to compute the correlation in Step 5.
7. Examine self-grading: Given that the class-estimate grades are not directly used to grade a presentation, students are also asked to provide an estimate of their own grade as part of Step 6. Effectively, students are encouraged to estimate properly their own grade.
The only thing that I have not tried to far is to modify Step 4 to take into consideration the different correlations from Step 5, effectively weighting each student's grades based on their correlation with the rest of the class. However, most students tend to exhibit the same, moderate agreement with the class (typical correlation values are in the 0.4-0.6 range, after rating 15-20 presentations), so in practice I do not expect to see a difference.

Overall, I am pretty happy with the scheme. Students indeed try to impress the class (and not me), and many presentations are interesting, interactive, and engaging. The grades are also very consistent with the overall feeling that I get for each presentation, so I did not have to practice my "editorial oversight" and adjust the grade very often (only in a couple of cases, where the students ran into technical problems during the presentation). I would be really interested to try this scheme in one of the big MOOC classes that use peer grading, and see if it can instill the same sense of responsibility in peer grading.