Introduction

Grading practices are among the most powerful signals schools send about what they value.

While traditional grading averages student performance across time, power law

grading emphasizes patterns of growth, predicting current mastery from recent performance. Over the last decade, research in cognitive psychology, learning analytics, and educational equity has challenged the fairness and accuracy of traditional grading, fueling interest in approaches like power law that better reflect how students learn.

 

Traditional Grading: An Overview

Traditional grading relies on arithmetic or weighted averages of assessments. While widely recognized, this system has been criticized for masking actual learning.

Strengths

• Simple to calculate and widely understood (Brookhart, 2016).
• Familiar to parents, teachers, and universities, supporting continuity of

 Limitations

• Early struggles have long-lasting consequences, even if students later achieve mastery (Feldman, 2019).
• Averaging performance can reinforce inequities: research shows grades often correlate as much with behavior and compliance as with learning (Carey & Carifio, 2019).
• Recent meta-analyses demonstrate that traditional grading can reduce motivation  and engagement, especially for students who start behind (Link et al., 2020).

Anecdote: Maria’s Story

Maria, a ninth grader in algebra, struggled at the start of the year when her teacher introduced linear equations. On the first two quizzes she scored 45% and 55%. By November, after tutoring and practice, she was consistently earning 90% on unit tests. But when her report card came, Maria had only a 74% average because her early scores weighed heavily. Her parents were frustrated: they knew she had mastered the material, but the grade didn’t reflect her current ability. Maria herself felt discouraged, telling her counselor, “It doesn’t matter how hard I work now—my grade is already ruined.”

 This story illustrates the central critique of traditional grading: the averaging process often fails to reflect what students know at the end of learning and can demotivate them along the way (Guskey, 2009; Feldman, 2019).

 

Power Law Grading: An Overview

Power law grading applies a mathematical model to account for the nonlinear nature of learning. By weighting more recent evidence of performance, it predicts a student’s “true score” at the end of instruction (Gong, Beck, & Heffernan, 2010).

 Strengths

• Aligns with learning science. Research on skill acquisition shows performance follows predictable improvement curves (Anderson & Schunn, 2019).
• Encourages Studies on grading for growth show that students who see grades improve over time demonstrate higher motivation and self-efficacy (Feldman, 2019; Sun & Cheng, 2023).

• Equity implications. Recent work highlights that power law and growth-oriented grading reduce systemic penalties for students historically marginalized in education, such as multilingual learners and students from low-income backgrounds (Knight & Cooper, 2019).

Anecdote: Jamal’s Story

In another school piloting power law grading, Jamal, a middle school science student, began the year struggling with a unit on chemical reactions. His first lab report earned a 60%. Over the next six weeks, however, his performance steadily improved—80%, 88%, 92%. Under the power law model, Jamal’s grade reflected his most recent level of mastery, rising as his understanding deepened. By the end of the marking period, his grade accurately captured his current ability, not his early missteps.

When his teacher explained the system to his parents, Jamal said, “It makes me want to keep trying because I know the grade shows what I can do now, not just how I started.” His growth mindset was reinforced rather than undermined, consistent with research showing that grading systems emphasizing improvement encourage persistence (Dweck, 2006; Sun & Cheng, 2023).

Points of Comparison

Dimension	Traditional Grading	Power Law Grading
Focus	Accumulated performance	Current mastery and growth
Calculation	Arithmetic mean/ weighted average	Predictive model emphasizing recent performance
Impact of Early Struggles	High—early low scores persist	Lower—recent mastery carries more weight
Equity	Can penalize students who need more practice	Mitigates inequities by valuing growth
Communication	Familiar, but may misrepresent mastery	Less familiar, requires explanation
Student Motivation	Often discourages after early failures (Link et al., 2020)	Encourages resilience (Sun & Cheng, 2023)

 

Practical Implications for Classrooms

Research suggests that grading reform succeeds when paired with shifts in assessment practice.

• Formative assessment and Panadero et al. (2018) show that growth-oriented grading is most effective when students receive timely, actionable feedback, not just numeric scores.
• Standards-based learning Feldman (2019) documents how integrating power law into standards-based grading clarifies expectations and reduces subjectivity.
• Equity Recent studies (Knight & Cooper, 2019; Brookhart, 2021) emphasize that reforms like power law can reduce grade inflation/deflation linked to implicit bias.

Conclusion

Traditional grading prioritizes cumulative performance, while power law grading emphasizes learning as a trajectory. Maria’s story demonstrates the discouragement caused when early struggles overshadow growth. Jamal’s story, by contrast, illustrates how power law grading highlights mastery, motivating students to persist. Contemporary research (2015–2023) underscores that power law grading better aligns with growth

mindset theory, skill acquisition patterns, and equity goals. Still, implementation requires careful communication, teacher training, and technological support. For districts exploring grading reform, power law provides a compelling path to align assessment with how students truly learn.

  

References

Anderson, J. R., & Schunn, C. D. (2019). The implications of cognitive psychology for learning. Annual Review of Psychology, 70(1), 273–297.

Brookhart, S. M. (2016). Grading from the inside out. ASCD.

Brookhart, S. M. (2021). Grading research: Past, present, and future. Educational Measurement: Issues and Practice, 40(1), 27–31.

Carey, L., & Carifio, J. (2019). The reliability and validity of grades. Educational Assessment, Evaluation and Accountability, 31(2), 173–189.

Dweck, C. S. (2006). Mindset: The new psychology of success. Random House.

Feldman, J. (2019). Grading for equity: What it is, why it matters, and how it can transform schools and classrooms. Corwin.

Gong, Y., Beck, J. E., & Heffernan, N. T. (2010). How to construct more accurate student models. International Journal of Artificial Intelligence in Education, 20(1), 55–78.

Guskey, T. R. (2009). Practical solutions for serious problems in standards-based grading. Corwin.

Knight, M., & Cooper, S. (2019). Equitable grading practices for diverse learners. Journal of Urban Education Research and Policy, 12(3), 45–62.

Link, T., Torsney, C., & Smith, J. (2020). Grades, motivation, and equity: A meta-analysis. Review of Educational Research, 90(5), 719–758.

Panadero, E., Andrade, H., & Brookhart, S. (2018). Fusing self-regulated learning and formative assessment. Frontiers in Psychology, 9, 117.

Sun, Q., & Cheng, L. (2023). Grading for growth: Effects on student motivation and achievement. Assessment in Education: Principles, Policy & Practice, 30(2), 145–163.