UC Berkeley

This study offers a mixed-methods analyses of formal and informal screening tools in L1 and L2 to identify English Language Learners in who are “at risk” for language learning disabilities. It was conducted in Bangalore, India and the sample consisted of 104 participants in Grades 2-5 from low, middle and high-income private schools. Teachers currently use school-based performance scores in English to classify students as persistent low-achievers. The purpose of this study was to provide teachers with a screening tool in both L1 Kannada and L2 English to be able classify two sub-populations of low-achieving students: students who are delayed in the second language acquisition process and students who are at risk for an underlying language learning disability.

Two formal bilingual screening tools were adapted and rendered culturally relevant in both British English and Kannada, namely the Preschool Language Scale 5 Screening Test (Zimmerman, Steiner, & Pond, 2012) and the Clinical Evaluation of Language Fundamentals 5 Screening Test (Semel, Wiig, & Secord, 2013). Both tests were efficacious in assessing general language ability, and there was a statistically significant relationship between the test scores. The PLS 5 was used to compare language competencies across age, as the same test that was developed for 7-year olds was administered to all students in the population, whose ages ranged from 7-10 years. Quantitative analysis revealed a statistically significant difference between 7-8 year olds and 9-10 year olds in their English scores but not in their Kannada scores, suggesting that L2 English was maintained as an academic language while L1 Kannada was not. The CELF 5 Test was used to classify students as “bilingual” (if they passed both tests in L1 and L2), “dominant L1”(if they only passed the Kannada test), “dominant L2”(if they only passed the English test) and “at risk for a language learning disability” (if they did not pass either the L1 or L2 tests). When CELF 5 scores were compared to school-based assessment scores, more than half of the students who were classified as being “at risk” by their teachers turned out to be dominant in their L2 according to their CELF5 classification.

Four informal screening tools were used for the study: Narrative Assessment, Parent Questionnaire, Teacher Interview and Classroom Observation. Students’ narrative skills were assessed using the Narrative Scoring Scheme (Heilmann et al, 2010). A high degree of overlap was observed between the students’ NSS scores and their CELF5 scores. Students who were identified as being “dominant L1 or L2” according to their CELF5 scores, also got an overall “proficient” classification on the NSS and students who were considered “at risk” by the CELF 5, were classified as “minimal” or “emerging” in their narrative skills. Quantitative analysis revealed that the CELF5 English and Kannada scores significantly predicted students’ NSS scores.

The other informal tools, the parent questionnaire, teacher interview and classroom observation checklist were efficacious in pinpointing external factors such as parents’ educational attainment, parents’ income levels, pedagogical practices, and special education resources, that are important when interpreting students’ performance scores across low, middle and high-income schools. Parents’ educational attainment predicted income levels in the low-income school and reading frequency in the middle-high income schools respectively.

Qualitative analyses of the teacher interviews emphasized the differences in language of instruction between low-income and middle-high income schools; whereas teachers in the former school alternated between English and Kannada, teachers in the latter schools used English only. The teacher interviews were also useful in highlighting the special education support at each school site: (a) in the low-income school, teachers treated low-achieving students as one group and they received small group instruction that targeted rote-memorization of the content related to school exams; (b) in the middle-income school, teachers viewed special education as occurring outside the purview of their classrooms, as the school had a moderate-severe special education program on the school site, but no resources for students with mild-moderate disabilities; and (c) in the high-income school, teachers followed an inclusive special education model and had access to a special education department on the school site as well as a consultancy service for assessment and intervention of students with disabilities.

Finally, qualitative analyses of the classroom observation checklist stressed the pedagogical differences across the three schools, with low and middle income schools focusing more on students’ content knowledge and rote memorization skills and high-income schools focusing more on students’ presentation skills and conceptual knowledge.

The study has implications for theoretical and applied issues concerning assessment, differentiation of language learning difference versus disability in ELLs and models and approaches for intervention.