Contract Grading is an alternative grading format in which course grades are based on students’ fulfillment of a predetermined set of learning activities rather than solely on their performance on those activities. Two good examples of Arts & Science faculty use of contract grading can be found in Angela Zito’s Course Exemplar Design Gallery and in the 2022 Teaching Innovation Award project by Expository Writing faculty members Nate Mickelson, Gita DasBender, and Leah Souffrant.
This approach to grading emphasizes depth of student engagement in a semester-long learning process over single assessment events, and it’s become an increasingly attractive option with the rise of generative AI tools like Chat GPT that make it easier than ever for students to get unauthorized assistance on assignments. Contract grading offers instructors a way shift the assessment focus in their courses to promote authentic, sustained engagement over time, making it much less tenable for students to use products from generative AI tools to completely replace their own effort.
While contract grading is often described as “student-centered”, it’s important to note that students as well as instructors can have a negative initial reaction to it. The removal of the standard evaluative measure, grades, that tells students how well they have accomplished learning goals on the assignment and course levels, can be anxiety-producing. Instructors worry that without the threat of bad grades or failure, students will not do quality work. Students worry that without feedback in the form of a grade, they won’t know how their work measures up against the learning goals for the class.
However, contract grading comes in different forms. While “labor-based” contract grading largely derives student grades from the amount of work submitted, other hybrid forms allow instructors to use a mixture of traditional performance-based grades and “edit to mastery” processes in which students revise assignments based on instructor feedback until the work meets a designated standard. Hybrid contract grading formats can ensure that students meet or exceed the learning goals for the course but with less anxiety about individual grading events for both instructors and students. With an “edit to mastery” process in place, instructors can ensure that students leave with a solid understanding of concepts and a set of skills because they have watched these develop over the course of the semester rather than spot-checked with several assessment events that students may have used unauthorized aid to complete.
An “edit to mastery” approach also addresses the frequent complaint from instructors that students ignore feedback and just look at their grades on assignments, thereby short circuiting any learning processes the instructor hoped to engage them in. Removing the grade from the equation can discourage this behavior, but in its place instructors must give timely, high quality feedback as well as an articulated process for how students should implement it (ie, a revision or a response paper or exam corrections).
Therein lies an understandable point of concern sometimes raised by instructors considering adoption of contract grading. Process-focused grading systems put an emphasis on students creating a lot of items for instructor feedback. This added assessment load, intended to alleviate student anxiety, can actually increase instructor anxiety because of the realistic constraints on the amount and quality of feedback it is possible for them to give.
However, use of a rubric tool can allay these fears by giving instructors an automated means of providing high quality, detailed feedback. For those concerned that rubric grading can seem too depersonalized and machine-generated, the Brightspace rubrics tool actually allows for customizable feedback, giving instructors the ability to mark assignments quickly while also being able to tailor their feedback to a student’s work. The Gradescope platform provides a similar workflow to deliver automated, personalizable feedback.
Technology has brought us to a pivotal moment in teaching and learning, but it is important to see that it offers solutions as well as disruptions. There is no denying that generative AI has, almost overnight, shaken confidence in the accuracy of traditional assessment methods. But this destabilization is also motivating educators to try new assessment practices, such as “edit to mastery” contract grading, that may actually promote deeper and more sustained learning. And perhaps most importantly, instructors now have at their fingertips a selection of robust, user-friendly ed tech tools, such as rubrics and automated feedback, that can make implementing alternative assessment practices feasible.
If you are interested in learning more about contract grading, reach out to your department liaison or email our team at fas-edtech-group@nyu.edu for a consultation.