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Improving Evaluations for Multilingual Learners:
A Conversation with
Dr. Sam Ortiz
Evaluating English and Multilingual Learners (EL/MLs) fairly and accurately remains one of the most complex challenges in special education. Yet getting it right is critical—not just for compliance, but for student success. In a recent webinar hosted by Marker Learning, Dr. Sam Ortiz shared research-backed strategies for improving evaluation practices, reducing bias, and supporting equitable outcomes for students from culturally and linguistically diverse backgrounds.
Below are the key takeaways from that conversation.
Why EL/ML Evaluations Are Often Misleading
Traditional evaluation practices often compare EL/ML students to monolingual English-speaking peers, ignoring the fact that multil-language development follows a different trajectory depending on age of acquisition and exposure. That is, age alone does not establish equivalence in language development in a non-monolingual population. This flawed comparison can make it appear that a student has a disability when in fact their performance reflects normal variation in development based on their linguistic and experiential background.
As Dr. Ortiz emphasized, “The key consideration in distinguishing between a difference and a disorder is whether the child’s performance differs significantly from peers with similar experiences” a quote drawn from the work of Wolfram, Adger, and Christian (1999).
The Case for “True Peer” Comparison
To ensure fairness, Dr. Ortiz introduced the concept of “true peer” comparison—measuring a student’s performance against peers who share similar levels of development and exposure in linguistic and cultural experiences. By using tools like the Culture-Language Interpretive Matrix (C-LIM) and the True Peer Estimator (TPE), evaluators can move beyond biased assumptions and toward more accurate, data-driven determinations of test score validity that better support and provide defensibility in disability determinations.
For example, a student may appear to be functioning at the 1st percentile when compared to monolingual English speakers, but land at the 46th percentile when evaluated against an appropriate peer group. This shift has significant implications for both eligibility decisions and instructional planning.
What Doesn’t Work—and Why
Dr. Ortiz cautioned against several common evaluation shortcuts that, while well-intentioned, often lead to invalid conclusions:
- Modified administration and scoring with English tests automatically render test scores invalid and cannot be interpreted
- Nonverbal assessments may miss the cognitive-linguistic dimensions critical to learning such as language which is essential for developing reading and writing skills
- Dominant language testing oversimplifies the bilingual experience and overlooks dual-language development giving a false sense of validity
These methods fail to meet the threshold of evidence-based practice—and may put districts at legal risk if challenged.
Tools That Support Defensible, Fair Evaluation
Dr. Ortiz recommended several free tools evaluators can begin using immediately, including:
- C-LIM v6.1: Assesses the impact of language and culture on cognitive test results
- C-LIM+ATE v2.1: Extends analysis to academic achievement outcomes
- True Peer Estimator (TPE v3.0): Provides assistance in determining developmental differences between an individual and typical, same-age peer normative samples
These tools require no internet connection, don’t collect or store data, and can run on desktop versions of Excel. Their purpose: to bring objectivity and defensibility to the evaluation of EL/ML students by allowing systematic investigation of the degree to which acquisition of language and acculturative knowledge may have affected performance and undermined score validity.
Looking Ahead: Tech, AI, and the Future of Testing
As evaluation continues to evolve, Dr. Ortiz pointed to the rise of AI and digital tools as a way to build greater equity and efficiency into the process. Technologies like AI-supported test selection, adaptive testing, and automated report generation along with the advent of “exposure norms” (norm samples based on both age and language development) have the potential to reduce disproportionality and streamline workflows—if used responsibly and ethically.
The Bottom Line
Improving evaluations for EL/ML students starts with shifting our lens: away from standardized comparisons and toward culturally and linguistically valid methods. With better tools, better data, and better training, school districts can avoid misidentifying students, reduce bias, and provide the support all learners deserve.
Want to learn more? Reach out to us at stefan@markerlearning.com or explore Marker’s solutions for fair, efficient, and research-aligned special education evaluations.
