Alternative Grading for College Courses

Traditional grading systems have many inherent issues that undermine learning. In this collection of web pages, the issues are explained and four alternative approaches - mastery, competency-based, contract, and specification - are described. If you would like assistance in thinking through how one of these alternative grading systems might work in your course, or how you might address any issues in your current grading approach, please contact an instructional designer assigned to your college.

Traditional Grading Systems

Traditional grading systems have a long history in colleges and universities across the United States. The first grading systems were used by Yale, Harvard, and other institutions as early as the 1880s and included bell-curve, 100-point (percentage), 4-point (GPA style), or letter (A-F) grading systems (Schinske and Tanner 2014, Bowen and Cooper 2022). In these institutions, grades were originally used to sort students for future career fields as grades allowed for a very subjective idea (learning) to be more easily quantified (Schwab et al. 2018).

Two main grading systems are used in traditional grading: Norm-referenced and criterion-referenced grading systems. Norm-referenced grading, also called normative grading or “grading on a curve”, uses the normal distribution (a bell-shaped curve) to rank student performance (Burton 2006). In norm-referenced grading students compete for a limited number of each grade as usually only the top 10% of students can earn an A in the course (Sadler 2005). This type of grading allows for students to be sorted or ranked within a course, but earned grades are usually not directly tied to student learning outcomes (Burton 2006). In most norm-referenced grading systems, assessments are mainly used for testing content knowledge on topics that have definitive answers (Burton 2006).

Criterion-referenced grading systems use a specific requirement to assess student work. The instructor selects a criterion or set of criteria, usually a point-based, percentage-based, or letter grading system, as criteria for grading (Sadler 2005). In most cases, the criteria used are not directly linked to the student learning objectives for the course (Sadler 2005). The criteria used in the course become the rules or requirements used to make judgments on student work and ultimately the overall course grade (Sadler 2005). Criterion-referenced grading is seen as more equitable than norm-referenced grading as student assessment is not based on rank or competition among students (Sadler 2005; see Box 1 for more information on the issues with normative grading practices).

Issues with Traditional Grading

Traditional grading systems have many inherent issues that undermine learning. Traditional grading systems are highly subjective and internalize instructor biases (Sadler 2005, Schwab et al. 2018, Link and Guskey 2019, Feldman 2019). Traditional grading systems pit students and instructors against each other by making grades a commodity that students must negotiate with the instructor (point grubbing), instead of building trusting relationships that allow for students to learn from their mistakes, take risks, and be creative (Feldman 2019). Additionally, these grading systems can increase stress and anxiety in students (Chamberlin et al. 2018) while reducing cooperative learning, critical thinking, creativity, and motivation (Strong et al. 2004, Chamberlin et al. 2018, Schwab et al. 2018, Feldman 2019).

At their core, grades are highly subjective due to a lack of consistency in what and how learning is measured (Sadler 2005, Schinske and Tanner 2014, Buckmiller et al. 2017, Scarlett 2018, Schwab et al. 2018, Link and Guskey 2019, Towsley and Schmid 2020, Zimmerman 2020). Instructors have different criteria for how students earn grades with many using a mixture of effort, achievement, and behaviors to assess student learning (Buckmiller et al. 2017, Schwab et al. 2018, Feldman 2019). When non-content aspects (attendance, participation, late penalties, extra credit, etc.) are included in grades, the grades become inaccurate for determining student learning (Buckmiller et al. 2017, Schwab et al. 2018, Feldman 2019, Link and Guskey 2019). Additionally, because each instructor determines the categories, weights, and other factors included in a final course grade, grades become unreliable indicators of a student’s understanding of the content (Link and Guskey 2019) and are not comparable across instructors or institutions (Schwab et al. 2018). For example, when researchers provided instructors with the same English essay, huge variations in grades were awarded to this single essay (50-98%) depending on the instructor (Schinske and Tanner 2014). The research was repeated with a single geometry paper with similar results indicating that this issue with grading is not restricted to more subjective topics or disciplines (Schinske and Tanner 2014). Further research also showed that if an instructor was asked to grade the same assignment at different times, the grades on this same assignment were significantly different at different times in the semester (Schinske and Tanner 2014). Thus, grades may lack validity due to sampling adequacy, question/prompt quality, marking standards, marking reliability, measurement error or similar issues making grades from traditional grading systems a poor indicator of student learning (Sadler 2005).

Due to the subjectivity of grades, many inequalities can become incorporated in a grade when traditional grading systems are used (Feldman 2019, Link and Guskey 2019). For example, if an instructor uses behaviors in grading (attendance, participation, etc.), then racial or gender biases may become part of the grade (Feldman 2019, Link and Guskey 2019). Research indicates that black students are seen as more disruptive than white students by white instructors (Link and Guskey 2019). Thus, if the behavior is incorporated into the grade, black students can receive lower grades even if they have a better understanding of the content than that grade would depict (Link and Guskey 2019). Similarly, gender biases around behavior can negatively impact men’s grades when grades are based on social skills and conforming behaviors (Link and Guskey 2019). In addition to race and gender, instructor bias can be seen based on the experience level of the instructor, the order the assignment was graded, the penmanship of the author, and other factors unrelated to the actual student work (Schinske and Tanner 2014).

Because grades are a form of extrinsic reward, traditional grading can reduce student motivation for learning (Schinske and Tanner 2014, Chamberlin et al. 2018, Schwab et al. 2018, Feldman 2019). When students are rewarded by points and grades instead of for their improvements in learning, they become reliant on extrinsic rewards (points/grades) over intrinsic desires to learn deeply about interesting topics (Chamberlin et al. 2018, Feldman 2019, Zimmerman 2020). Research has shown that students provided with grades and feedback on their work, ignore the feedback and focus solely on the grade (Schinske and Tanner 2014, Chamberlin et al. 2018). In a study where students were randomly placed in one treatment (given a grade only, feedback only, or a grade and feedback), students provided with only descriptive feedback (no grade) did better on problem-solving tasks than other students (Schinske and Tanner 2014). Further, students that received both descriptive feedback and a grade, did not improve their work either because they fail to read the descriptive feedback or failed to use it in future assessments (Schinske and Tanner 2014). Overall, grades do not increase student motivation or interest in learning (Schinske and Tanner 2014, Schwab et al. 2018) and can have negative impacts on student performance by increasing anxiety, fear of failure, and a fixed mindset (Schinske and Tanner 2014, Chamberlin et al. 2018).

Equitable Grading Practices

Knowing that traditional grading practices are problematic for providing an accurate representation of student learning, it is important to understand the aspects of grading practices that need to be changed to make grades more accurate and equitable. In “Grading for Equity”, Joe Feldman (2019) devised three main areas of grading that need to be addressed to increase equity: accuracy, bias-resistance, and student motivation.


Traditional grading practices rely on unsound use of mathematical calculations and techniques when assessing student learning. Grading systems that use zeros for missing work, 0-100 percentage scales, averaging of points, and similar techniques to calculate a student's grade are heavily weighted toward student failure and do not accurately measure student learning. When instructors use zeros for missing work and then average scores, the missing assignments substantially lower the student’s grade and do not accurately reflect the student's understanding of the content.

Moreover, using the percentage system for grading (90-100% = A; 80-89% = B; 70-79% = C; 60-69% = D; and 0-59% = F) creates a system where failure is mathematically more likely to occur than any other grade. Of the 101 units in the 0–100-point system, 60 units are failing and only 41 units are passing (assuming a D is a passing grade). Once a student has too low a grade, it becomes mathematically impossible for the student to recover and pass the course. Use of grading systems that have equal levels (each letter grade equal to 20% of the 100 percentage scale: 80-100% = A; 60-79% = B; 40-59% = C; 20-39% = D; and 0-19% = F) which occurs naturally when using a 4-point grading system (4 = A; 3 = B, 2 = C, 1 =D; 0 = F) over a 100-point system increases equity in grading. Alternatively, instructors can implement minimum grading (students automatically have 50% on any assignment in a 0–100-point system and therefore each grade is only 10% of the grading scale) or consider recent work more strongly than beginning assignments to reflect student learning and thus create a more equitable grading system.


Everyone has implicit biases that influence how they view the world. In grading, implicit biases are often associated with including non-content aspects of a class into a student’s grade. When attendance, politeness, participation, effort, late penalties, and extra credit are incorporated into a grade, the accuracy of the grade is diminished as instructor biases are added to grades. What constitutes politeness? How much effort is worth an A? How many points are lost for late work even if the student shows they understand the content? Including non-content related methods (usually based on behaviors) for students to earn points, allows students with more resources and privileges to excel even if they don’t understand the content while less privileged students are negatively impacted even if they do know the course content. Thus, instructor biases can create inequitable grading.

Student Motivation

Motivation is a complex aspect of human psychology that includes initial interest, engagement, and persistence in a task. Two main types of motivation can influence an individual’s involvement and completion of a task: intrinsic and extrinsic motivation. Intrinsic motivation includes any internal mechanisms used by an individual to engage in a task while extrinsic motivation requires a reward (or punishment) be associated with the task. Much research on motivation indicates that extrinsic motivation can be used for short-term compliance on rote tasks, but fails to promote the completion of creative, imaginative, original, or complex tasks including deep learning. Grades are extrinsic motivation and have been shown to reduce student motivation to learn and promote a fixed (over a growth) mindset. Using alternative grading approaches can redirect student motivation to learning by de-emphasizing grades and allowing students multiple opportunities to meet standards associated with course content.

Alternatives to Traditional Grading

Standards-based grading is an alternative to traditional grading that uses specific standards (usually student learning objectives) to assess both content and skills-based knowledge (Burton 2006, Buckmiller et al. 2017, Scarlett 2018, Zimmerman 2020). There are six main principles for standards-based grading (Towsley and Schmid 2020):

  • Learning objectives and outcomes are explicitly stated and accessible to students.
  • Instructors use many strategies to facilitate student learning.
  • Students are given flexibility in the methods used to show mastery of learning objectives.
  • Assessments are designed to test students on learning objectives.
  • Course grades are based on students’ ability to demonstrate understanding of learning objectives only (not on behaviors or other non-academic criteria).
  • Strategies are implemented to provide students with additional supports to ensure learning.

In standards-based grading, students are provided detailed information prior to learning material that explicitly links learning objectives to assessments and states the requirements for assignments and assessments (Sadler 2005, Bonner 2016). Student work is then used as evidence for meeting the learning objectives for each assessment (Burton 2006, Buckmiller et al. 2017) with different methods used to provide students with opportunities to resubmit work that does not initially meet the learning objective standards (Scarlett 2018, Zimmerman 2020). Depending on the framework used for standards-based grading, different methods are used to assess coursework and determine course grades. Standards-based grading frameworks include mastery grading, competency-based grading, contract grading, and specifications grading. It is also important to know that different authors and researchers use some of these terms somewhat interchangeably or with the overarching title of “ungrading” or Competency-based Education.

How to Implement Standards-based Grading

Regardless of the framework used, all standards-based grading systems have four common traits incorporated into them (Towsley and Schmid 2020).

Determine the Learning Objectives for the Course

All types of standards-based grading start by using backward design to determine learning objectives and align the objectives to the assessments and assignments (Sadler 2005, Bonner 2016, Towsley and Schmid 2020). When determining the learning objectives for a course, it is important to include learning objectives that cover topics that will be needed for future courses as well as for standardized or other formal exams for the discipline (Lalley and Gentile 2009). Additionally, looking over past course exams can help to determine the topics and skills students will need to acquire before the end of the course. Depending on the course, the learning objectives may be broad (with less than 10 learning objectives in the course) or very specific (in which case the course may have 20 or more learning objectives; Cilli-Turner et al. 2020). Usually, courses that focus on skills and procedural knowledge (solving math problems using specific steps, using a microscope, writing a complete sentence, etc.) have more learning objectives than courses that require higher-level thinking and application of knowledge (writing out a math proof based on an underlying theory, explaining how life evolved from single-celled organisms to multicellular life, writing a persuasive essay; Cilli-Turner et al. 2020). Finally, learning objectives need to be written to be assessable, readable, and understandable by students (for more information on writing learning objectives, read the “Designing your Course” pages in the Teaching@UNL resource) as all forms of standard-based grading provide the learning objectives to students.

Define Mastery for Assessments

Another common trait for all standards-based grading is the use of pre-determined methods for assessing mastery on individual assignments. Mastery can be based on a percentage correct on an assignment, a specific number of correctly answered problems for each learning objective, or a minimum number of “passed” aspects of a rubric for an assignment. Thus, it is important to determine how mastery will be achieved for each type of assessment as this will dictate the types and number of assessments students need to complete (and to what level of proficiency) in the course (Cilli-Turner et al. 2020). For example, an instructor may determine that 80% accuracy in total responses is the mastery level for a quiz that covers a single standard. Alternatively, a project may be graded on multiple standards with each standard requiring 70% mastery based on requirements in a rubric.

Decide how the Course Grade will be Calculated

Instructors also need to determine how the course grade will be calculated. For some alternative grading systems, this requires bundling of assignments while others use individual student contracts or other methods to determine course grades (Towsley and Schmid 2020). The links below provide more information on grading and other aspects of the different types of alternative grading systems:

Incorporate Resubmission and Remediation

Finally, all standards-based grading systems incorporate methods for remediation and resubmission of assessments to allow students opportunities to meet mastery (Towsley and Schmid 2020). It is important to think through the logistics of resubmission of assignments and assessment. Can students resubmit any assignment or is a token system used (allowing each student a specific number of opportunities to resubmit assignments)? Are test retakes done during class or outside of class? Are resubmissions of the same type or are alternative formats given when students resubmit an assignment or assessment? Are assessment retakes and assignment resubmissions incorporated into the design of the course with multiple assessments on the same learning objective? For example, Zimmerman (2020) designed a high-enrollment introductory math course to include a quiz, unit exam, and final exam. Quizzes covered 1-3 learning objectives and unit exams usually covered 3-8 learning objectives while the final exam was cumulative and thus covered all learning objectives (Zimmerman 2020). If students scored higher on a subsequent assessment for a specific standard, that grade replaced the previous grade, but when the score was lower, then the grades were averaged (Zimmerman 2020). Thus, retakes for assessments were built into the course structure with three opportunities at different times in the semester for students to demonstrate their understanding for each learning objective (Zimmerman 2020).

For students to improve between retakes or resubmissions, remediation is necessary. The remediation can come in the form of instructor feedback, peer tutoring, additional assignments (that incorporate scaffolding of ideas to aid in student learning), and other supports specific to the discipline (writing centers, math centers, etc.). Instructor feedback is vital for student remediation but must be descriptive feedback (comments or tools to improve student work) instead of evaluative feedback (grades, judgments, etc.; Schinske and Tanner 2014). Descriptive feedback is most beneficial to student learning and success when students are required to read and use it in future assignments or for resubmissions (Schinske and Tanner 2014). Some important considerations for remediation are thinking about the time required to give feedback, how long students need to relearn and master a topic, and what will occur if a student continues to not meet mastery. Depending on the enrollment size, topics covered, and instructor preference, the types and methods used for remediation and resubmission can be diverse but must be included to qualify as standards-based grading.

When these common characteristics are well designed, standards-based grading frameworks increase fairness in grading by reducing biases and subjectivity (Burton 2006, Buckmiller et al. 2017), increase rigor by requiring students to learn the material in a deeper manner (Bonner 2016, Buckmiller et al. 2017, Scarlett 2018, Zimmerman 2020), and increase student ownership of their learning (Buckmiller et al. 2017, Scarlett 2018). Additionally, students at institutions that used standards-based grading had higher motivation to learn compared to students at institutions that only use traditional grading systems (Chamberlin et al. 2018) and were more likely to complete optional activities, practice self-assessment techniques, and seek help from the instructor (Zimmerman 2020). Overall, standards-based grading refocuses courses and instruction on learning over getting a grade.

Bonner, M. W. (2016). Grading rigor in counselor education: A specifications grading framework. Educational Research Quarterly 39.4: 21-42.

Bowen, R. S. and M. M. Cooper (2022). Grading on a curve as a systemic issue in equity in chemistry education. Journal of Chemical Education 99: 185-194.

Buckmiller, T., R. Peters, and J. Kruse (2017). Questioning points and percentages: standards-based grading (SBG) in higher education. College Teaching 65: 151-157.

Burton, K. (2006). Designing criterion-referenced assessments. Journal of Learning Design 1:73-82.

Chamberlin, K., M. Yasue, and I. A. Chiang (2018). The impact of grades on student motivation. Active Learning in Higher Education DOI: 10.1177/1469787418819728

Cilli-Turner, E., J. Dunmyre, T. Mahoney, and C. Wiley (2020). Mastery grading: Build-a-syllabus workshop. PRIMUS 30: 952-978.

Feldman, J. (2019). Grading for Equity: What it is, why it matters, and how it can transform schools and classrooms. Corwin: A SAGE Company, Thousand Oaks, CA.

Lalley, J. P. and J. R. Gentile (2009). Classroom assessment and grading to assure mastery. Theory Into Practice 48: 28-35.

Link, L. J. and T. R. Guskey (2019). How traditional grading contribute to student inequities and how to fix it. Educational, School, and Counseling Psychology Faculty Publications

Sadler, D. R. (2005). Interpretations of criteria-based assessment and grading in higher education. Assessment & Evaluation in Higher Education 30: 175-194.

Scarlett, M. H. (2018). “Why did I get a C?”: Communicating student performance using standards-based grading. InSight: A Journal of Scholarly Teaching 13:59- 75.

Strong, B., M. Davis, and V. Hawks (2004). Self-grading in large general education classes: a case study. College Teaching 52:52-57.

Schinske, J. and K. Tanner (2014). Teaching more by grading less (or differently). CBE – Life Sciences Education 13: 159-166.

Schwab, K. B. Moseley, and D. Dustin (2018). Grading grades as a measure of student learning. SCHOLE: A Journal of Leisure Studies and Recreation Education 33: 87-95.

Townsley, M. and D. Schmid (2020). Alternative grading practices: An entry point for faculty in competency-based education. Competency-based Education DOI:

Zimmerman, J. K. (2020). Implementing standards-based grading in large courses across multiple sections. PRIMUS 30: 1040-1053.

Alternative Grading for College Courses was written by Michelle Larson. Published to the website January 20, 2023.

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