English Language Learners, Learning Disabilities, & Overrepresentation: A Multiple Level Analysis

1. English Language Learners, Learning Disabilities, & Overrepresentation: A Multiple Level Analysis Robert Rueda University of Southern California Michelle Windmueller California State University, Los Angeles NCRESST Research Conference on English Language Learners Struggling To Learn: Emergent Research on Linguistic Differences and Learning Disabilities, Arizona State University, Tempe, AZ, November 2004

2. Background • 3 “big issues” in special ed*: • Continued use of discrepancy model and need for better identification models • Continued debate over programmatic issues (e.g., inclusion v. self-contained) • Overrepresentation of certain groups especially in LD category *Hallaghan, D.P., & Mock, D.R. (2003). A brief history of the field of learning disabilities. In HlL. Swanson, K.R. Harris, & S. Graham (Eds). Handbook of learning disabilities. (pp. 16-29. New York: The Guilford Press.

3. Unpacking Overrepresentation • Early work (Mercer, 1973; National Research Council, 1982) – Latino & African American students in classes for mild retardation • Much work over last two decades documenting the same issue • Changes: more diversity in schools; distribution of students in different categories; legislation and policy changes (school reform, English-only, accountability, reading initiatives, mandated curricula) • Most recent National Research Council (2002) report

4. NRC (2002) • Controversy about indicators (% difference, risk index, composition index, odds ratio) • Problems in national databases • Lack of precision in definitions • Lack of consistency in definitions over time and place • Inaccuracy of self-report data • State to state variations

5. General Findings • “Epidemic” increase for all racial/ethnic groups in LD category • African American have slightly higher rate of placement, Latinos slightly lower than Whites (aggregated data across 12 categories) • No evidence of overrepresentation for any single racial/ethnic group across all categories

6. Unpacking the Data • Other findings suggest that overrepresentation is more problematic when large aggregate data sets are unpacked • > minority numbers in a district = > representation • > the ed. program, > the disproportion • Specific disability and ethnic/racial group variability when examined closely (Artiles & Trent, 1994) • District SES is related to placement patterns (low SES district = > LD and ED placement) (Oswald et al, 1998, 2000) • Grade level, language placement, and English language proficiency matter (Artiles, Rueda, Salazar, & Higareda, 2002, in press)

7. Explanations? • NRC (2002) suggested 2 hypotheses: • Systematic Bias Hypothesis • Achievement Difference Hypothesis • We suggest a third alternative: • Misalignment/Imbalance Hypothesis

8. How Does It Work? • Draws on sociocultural analyses of learning & development, in particular recent extensions by Rogoff and others • 3 Planes of Development • Individual (cognition, motivation, etc.) • Interpersonal (discourse, social, and cultural factors of activity settings and actors as mediators of teaching/learning outcomes) • Community/Institutional (factors such as past & current power relationships among different groups, including how they are embedded in social institutions and perceived and experienced by individuals and their communities – eg, English-only battles, anti-immigrant legislation, SES inequities)

9. Problems with Existing Efforts • Much work in special ed has focused heavily on individual factors • Much less has considered social organizational factors • Little to none has considered community/ institutional perspectives • Virtually no work considers all three • We argue most current efforts are imbalanced (ie, focus exclusively on one level to the exclusion of the others) or misaligned (factors at one level work against factors at another level) • Moreover, the “holy grail” search for scalable, replicable approaches ignores context – the unique dynamics of local settings and communities

10. What to Do? • Realize that education is NOT business or medicine – and models derived from them do not generalize • Realize that overrepresentation is best treated as an indicator of underlying problems rather than as an outcome in and of itself • Commit to a cultural value of collecting, unpacking, and acting on relevant data and indicators is essential • Once problems are identified, systematic action research approaches in general are suited to examining local problems and contexts

11. One Approach • In our paper, we explore a derivative called “gap analysis” – a systematic problem-solving approach from human performance and organizational psychology fields • Involves setting negotiated goals, determining indicators (NOT necessarily test scores), determining gaps between current and desired performance, systematically exploring the causes, and deriving theory-driven and research-based solutions appropriate for this specific context • Mainly used in business, but may have potential to be adapted to educational settings – sensitively!