Educators rely on assessment data every day to make decisions about instruction, intervention, and student support. And while Mastery Predictive Assessments have always provided a strong, reliable signal, we’re continually working to make that signal even more precise.That’s why this year, we made an important enhancement to how Mastery Predictive Assessment results are calculated.
We’ve introduced a new scaling method that further improves how student performance levels and scale scores are determined. In simple terms, it gives you an even clearer, more accurate picture of what students actually know, regardless of small differences in test difficulty.
In this post, we’ll walk through what changed, why it matters, and what it means for how you interpret your students’ progress. And if you want to go deeper, you can always explore the full research report here.
What changed and why
This year, we made the move to a new scaling method called linear equating. At its core, linear equating is a research-based method used by leading testing organizations. It adjusts student scores by accounting for differences in how hard a given test is and how students actually responded to each item. That makes the score a more trustworthy reflection of what a student truly knows.
Think of it like this: Imagine grading two different versions of a spelling test that had slightly different word lists. Our previous scaling method would sort students into buckets based on historical class averages. Linear equating adjusts for the fact that one version was harder than the other, so every student's score is compared on a fair, common scale.
So, by making this change, we’re ensuring that student results are measured more consistently across assessments giving you a clearer, more reliable picture of where each student stands.
Evidence for improved accuracy with linear equating
But consistency only matters if it leads to better results.
To evaluate the impact of this change, researchers tested both methods against real state test results across nearly 300,000 students across many states. The findings from this study consistently showed clear improvements when using linear equating for Predictive Assessment scaling and scoring:
- Linear equating achieved an overall classification accuracy for individual students of 85% and up to 89% (in some courses), representing an increase over legacy scaling (84%).
- Performance in English language arts (ELA) saw the most significant gains, especially in Grades 3 and 4, with accuracy increasing by 4% and 5% and more accurate estimates of percent of students proficient at the district level in Grades 6-8 (up to 6% better).
- For high school end-of-course tests, linear equating consistently produced proficiency estimates closer to what students actually scored on state assessments. This includes Algebra 1, Algebra 2, Biology, English 2, and U.S. History — with linear equating's district-level estimates off by just 4.3% on average.
Why ELA accuracy especially matters: Many states — including Tennessee, Mississippi, and North Carolina — have literacy-based promotion laws that hinge on whether students meet reading proficiency benchmarks. More accurate predictions mean you can identify students who need targeted support before it's too late to act.
What this means for you as an educator
Together, these improvements translate into more reliable insights you can act on. Here’s what that means for you as an educator:
- 1. More trustworthy signals: Your MPA results are now more accurate predictors of how students will perform on their state test, especially in ELA. If the assessment flags a student as "below proficient," that signal is now more trustworthy than before.
- 2. Fewer misclassifications: The overall error in predicting students' true proficiency dropped from 18.35 to 15.14 (mean absolute error). In practice, this means fewer students will be misclassified, either falsely flagged as struggling, or missed when they actually need support.
- 3. Stronger instructional decisions: You can use MPA data with even greater confidence when making instructional decisions: grouping students for intervention, adjusting pacing, or prioritizing which skills to revisit before state testing windows.
- 4. Industry-leading reliability: The new method exceeds the 75% industry standard for classification accuracy across all grades and subjects, so the baseline reliability you've come to expect from MPAs remains intact — and in most cases, it's higher.
Ultimately, this change is about giving you greater confidence in your data—so the decisions you make for students are grounded in results you can trust.
