Traditional grading methods have long been used in education as the norm, systematically embedded in how education is designed and delivered, as well as managed within administrative systems (Stommel 2023). These traditional approaches rely on familiar evaluation scales such as assigning letter grades, numerical grades, percentages or point scores, or word-based scales (e.g. Pass/Fail).
While such grading methods have their place, research from as early as the 1980s and 1990s suggests that these methods have the potential to not only decrease the quality of a student’s thinking, as well as their interest in the associated topic, but also encourage an atmosphere of conformity. This kind of atmosphere can then limit the students’ opportunity to demonstrate their learning in a personalised, and possibly more successful, manner (Kohn 2011; Schinske & Tanner 2014, cited in Lauricella 2022). Additionally, grades can shift a student’s focus away from teacher feedback, instead encouraging them to prioritize the final product rather than the whole learning process (Hirsch 2024).
As a counter to traditional grading is the concept of alternative grading. While the underlying ideas are not entirely new, alternative grading has gained more attention over the past decade, as more educators have begun to question the overall effectiveness of conventional assessment (Onufer 2022).
What is alternative grading?
Alternative grading is an umbrella term for a wide variety of non-traditional assessment methods. These approaches are designed to give students more agency over their learning, redirect their attention towards teacher feedback, and help build intrinsic motivation (Onufer 2022; Hirsch 2024). While some alternative systems replace traditional letter grades completely, others adapt or minimize conventional scales alongside non-traditional practices.
Below are a few widely used alternative grading frameworks.
Specifications grading (also known as specs grading)
In specifications grading, the course content is structured so that students complete tasks and assessments that correspond to the grade they aim to receive for the course. Higher grades require students to complete more content and demonstrate a more comprehensive and complex understanding, whereas lower grades require less content completion and demonstration of fundamental basics only. (Hall 2018)
Minimal grading
Minimal grading involves the instructor simplifying the grading scales used in the course down to only a few performance levels only (for example, developing, satisfactory, and exemplary), rather than using points systems, percentages, or detailed scales. The aim is to increase equity and fairness for students, with an increased focus on mastering concepts and instructor feedback. (Onufer 2022)
Ungrading
The ungrading approach emphasizes a more collaborative relationship between students and instructors, with course grades decided upon through discussion and negotiation between the two parties. Assessments for the course may forego the use of grading entirely, with only detailed feedback provided by the instructor. Students are likely expected to complete self-assessments and self-reflections continually throughout the course to help document their learning journey. (Onufer 2022)
Grade-free zones
Grade-free zones are portions of the course in which the instructor does not assign grades or points to a student’s work, providing feedback only instead. This differs with the ungrading approach, which focuses on a course-wide grade-free system, as grade-free zones could be used for some parts of a course while the remaining parts could still use traditional grading methods. (Onufer 2022)
Ultimately, alternative grading is not about removing accountability or standards, rather it is about realigning assessment with learning. As higher education continues to evolve, rethinking assessment is an essential piece of the puzzle. For LAB teachers, these approaches don’t have to mean an all-or-nothing redesign of courses – even making small adjustments to existing tasks and courses could help to encourage student motivation. While no single grading method fits every discipline or course format, exploring these frameworks opens a valuable space for us at LAB to discuss assessment equity and meaningful student growth.
AI has been used to improve the conciseness of the text and clarity of some phrases.