When grading MLs equitably, we must recognize that a grade denotes what an ML can do independently when provided with the appropriate linguistic scaffolds. Scaffolds are not a crutch or an unfair advantage. Rather, they are the tools MLs need to access and be successful with grade-level instruction and assessment. Scaffolds come in three general categories: instructional materials, instructional practices, and instructional groupings (Staehr Fenner et al., 2024). They are provided based on an ML’s strengths and needs, knowing that these scaffolds will be removed as students gain language proficiency and confidence in demonstrating their content skills. Collaboration between English language development (ELD) and content teachers is crucial for determining appropriate scaffolds for their MLs to meaningfully access content and demonstrate knowledge on an assessment. 

We often hear teachers share frustration as to whether it is “fair” to award an ML an ‘A’ on a task completed with heavy scaffolding, such as a word bank or graphic organizer, while fluent English peers complete the task unassisted. This concern is understandable as we try to balance the ability to determine content mastery while providing linguistic equity for our MLs. So, let’s come back to our ML, Zaynab, and her math lesson. If an ML like Zaynab utilizes a bilingual glossary (Figure 2) to correctly isolate a mathematical constant and formulate an equation, she has fully mastered the grade-level content standard. Her bilingual glossary does not impact whether she can isolate a mathematical constant and formulate an equation, but it does afford her the linguistic knowledge her fluent English peers already come to the task knowing—the language.