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Title: Issues and problems in classification of students with limited English proficiency


1
  • Issues and problems in classification of students
    with limited English proficiency
  • Jamal Abedi
  • UCLA Graduate School of Education Information
    Studies Center for the Study of
    Evaluation National Center for Research on
    Evaluation, Standards, and Student Testing
  • Paper presented at the 2002 Annual Meeting of
    the American Educational Research Association
  • New Orleans, LA

2
1
  • Classification of ELL Students
  • There is a growing concern over the assessment
    and classification of language minority students.
  • However, before developing a valid and reliable
    assessment system, a well-defined, objective
    definition of the term LEP or ELL must be
    obtained.
  • Unfortunately, the criteria for identifying LEP
    students are not used uniformly across the
    nation.
  • In several language background studies conducted
    at UCLA /CRESST (Abedi and Lord, 2001 Abedi,
    Lord, and Plummer, 1995 Abedi, Lord, and
    Hofstetter, 1997, Abedi, Hofstetter, Baker, and
    Lord, 1998), one of the major problems
    encountered was the lack of a commonly acceptable
    definition for limited English proficiency.

3
2
Classification of Students with Limited English
Proficiency There are many different criteria by
which a student can be classified as LEP. Among
the most important of these criteria are being
speaker of a language other than English and
scoring low on the English proficiency tests.
The first criterion, i.e., being a non-native
English speaker, is defined in Los Angeles area
schools based on the information from the Home
Language Survey. The Home Language Survey For
many schools in Los Angles area, the Home
Language Survey is the only source of information
used to determine the need for a student to be
tested for English Proficiency. Recent dialog
over the type of bilingual instruction causes
reporting inaccurately for the purpose of
assuring that their children be treated no
differently from their Anglo classmates. Other
concerns for the student whose parents may have
citizenry issues have led to a more relaxed
treatment of the home surveys than what the
district would prefer. Questions have been
raised about the accuracy of a survey completed
by a parent who is illiterate or who has no
familiarity with written English.
4
3
Assessment of Students Language Proficiency in
English Language proficiency and achievement
tests in English are commonly used for
identification and assessment of LEP students.
According to Hopstock, Bucaro, Fleischman,
Zehler, and Eu (1993) eighty-three percent of
school districts use English language proficiency
tests alone or with other techniques to decide if
a student is LEP. The English proficiency tests
used frequently for such purposes are the
Bilingual Syntax Measure (BSM), the Idea
Proficiency Test (IPT), the Language Assessment
Battery (LAB), the Language Assessment Scales
(LAS), the Maculaitis Assessment Program (MAC),
and the Peabody Picture Vocabulary Test (PPVT).
5
4
Achievement Tests Achievement tests in English
are used by approximately 52 percent of school
districts to help identify LEP students, assign
them to school services,and reclassify them from
LEP status. Commonly used achievement tests are
the California Achievement Test (CAT), Iowa Test
of Basic Skills (ITBS), Metropolitan Achievement
Test (MAT), Stanford Achievement Test (SAT), and
the Comprehensive Test of Basic Skills
(CTBS). Zehler, et al. (1994) did a comprehensive
review of these language proficiency tests and
found major differences in all the content areas
in which the tests were compared. An even more
serious criticism of these language proficiency
and achievement tests is the problem of the
validity and reliability of these tests for LEP
populations and the exclusion of LEP students
from the norming group for these tests. For
example Abedi Leon (1999) found that language
factors may be an additional source of
measurement error in the assessment of LEP which
may reduce the reliability of the tests
considerably. Abedi, Leon Mirocha (2001)
found that language factors in content-based
assessment may seriously undermine the validity
of the tests and may be considered a source of
construct-irrelevant variance (Messick, 1994,
p.14).
6
5
Data Sources Site 1. Site 1 is a large urban
school district. ITBS test data were obtained.
There were 36,065 students in the grade 3 (7,270
bilingual), In grade 6 there were 28,313 students
(3,341 or 11.8 bilingual) and in grade 8, there
were 25,406 students (2,306 or 9.1 were
bilingual). Site 2. There were 414,169 students
in the grade 2 population (125,109 or 30.2 were
LEP), in grade 7 there were 349,581 students
(73,993 or 21.2 LEP). In grade 9 there were
309,930 students (57,991 or18.7 LEP). Stanford
9 test data were obtained for all students in
Grades 2 to 11 for the 1997-1998 academic year.
Site 3. There were 12,919 students in the grade
10 population (431 or 3.3 LEP). In grade 11
there were 9,803 students in the population (339
or 3.5 LEP). Site 4. There were 13,810 students
in the grade 3 (1,065 or 7.7 LEP). In grade 6
there were 12,998 students in the population (813
or 6.3 LEP), in grade 8 there were 12,400
students (807 or 6.5 LEP).
7
  • Findings
  • Relationship between language proficiency test
    scores and LEP classification.
  • Since LEP classification is based on students
    level of language proficiency and because LAS is
    a measure of language proficiency, one would
    expect to find a perfect correlation between LAS
    scores and LEP levels (LEP versus non-LEP).
  • The results of analyses indicated a weak
    relationship between language proficiency test
    scores and language classification codes (LEP
    categories).
  • Table 1. Correlation between LAS rating and LEP
    classification for Site 4
  • Correlation G2 G3 G4 G5 G6
    G7 G8 G9 G10 G11 G12
  • Pearson r .223 .195 .187 .199
    .224 .261 .252 .265 .304 .272 .176
  • Sig (2-tailed) .000 .000 .000 .000
    .000 .000 .000 .000 .000 .000 .000
  • N 587 721 621 1002
    803 938 796 1102 945 782 836

6
8

7
9
  • Table 4. Correlation coefficients between LEP
    classification code and ITBS subscales for Site 1
  • Grade Reading Math Concept
    Math Problem Math

  • Estimation Solving
    Computation Grade 3
  • Pearson r -.160
    -.045 -.076
    .028
  • Sig (2-tailed) .000
    .000 .000
    .000
  • N 36,006
    35,981 35,948
    36,000
  • Grade 6
  • Pearson r -.256
    -.154 -.180
    -.081
  • Sig (2-tailed) .000
    .000 .000
    .000
  • N 28,272 28,273
    28,250
    28,261
  • Grade 8
  • Pearson r -.257
    -.168 -.206
    -.099
  • Sig (2-tailed) .000
    .000 .000
    .000
  • N 25,362 25,336
    25,333 25,342

8
10
9
  • Table 5. Correlation coefficients between LEP
    classification code and Stanford 9 subscales for
    Site 2
  • Grade Reading Language
    Science Math Spelling
    Social Sci
  • Grade 3
  • Pearson r -.415
    -.352 -.299 -.275
    -.305 -.277
  • Sig (2-tailed) .000
    .000 .000 .000
    .000 .000 N
    376,986 373,669
    77,855 386,369 385,699
    62,317
  • Grade 5
  • Pearson r -.443
    -.370 -.339 -.329
    -.358 -.319
  • Sig (2-tailed) .000
    .000 .000 .000
    .000 .000 N
    358,720 366,523
    81,951 370,435 370,689
    73,975
  • Grade 7
  • Pearson r -.450
    -.390 -.363 -.318
    -.403 -.338 Sig
    (2-tailed) .000 .000
    .000 .000 .000
    .000
  • N 336,309
    334,827 102,595 340,094
    341,745 86,894
  • Grade 9
  • Pearson r -.416
    -.346 -.318 -.287
    -.346 -.298
  • Sig (2-tailed) .000
    .000 .000 .000
    .000 .000 N
    293,667 293,320 297,057
    298,558 86,366
    295,022
  • Grade 11
  • Pearson r -.387
    -.334 -.295 -.225
    -.311 -.290
  • Sig (2-tailed) .000
    .000 .000 .000
    .000 .000
  • N 225,113
    223,912 225,671 227,217
    58,354 223,891

11
10
Table 6. Correlation coefficients between LEP
classification code and Stanford 9 subscales for
Site 3 Grade Reading Science
Math Grade 10 Pearson r -.131
-.088 -.029 Sig (2-tailed) .000
.000 .003 N
11,158 10,231 10,301 Grade
11 Pearson r -.140 -.095
.005 Sig (2-tailed) .000 .000
.658 N 8,740 7,900
8,040

12
11
Table 7. Correlation coefficients between LEP
classification code and Stanford 9 subscales for
Site 4 Grade Reading Math
Math Computation
Application Grade 3
Pearson r -.178
-.067 -.120 Sig
(2-tailed) .000
.000 .000 N
14,050 14,282
14,208 Grade 6 Pearson r -.232
-.087
-.142 Sig (2-tailed) .000
.000 .000 N
13,354 13,364
13,299 Grade 8 Pearson r
-.228 -.088
-.125 Sig (2-tailed) .000
.000 .000 N
12,484 12,579
12,337 Grade 10 Pearson r
-.252 NA
-.102 Sig (2-tailed) .000
.000 N
9,499
9,778
13
12
  • Findings and Conclusions
  • For an effective instruction and a valid and
    reliable assessment for English language
    learners, a well-defined, objective definition of
    the term ELL and LEP is needed.
  • The results of studies nationwide suggest,
    however, that such definition is not
    provided.
  • The results of our analyses on large-scale data
    did not show a strong relationship between
    LEP classification and students level of English
    proficiency.
  • Analyses on the distribution of some English
    language proficiency tests showed a
    negatively-skewed distribution suggesting that
    the English proficiency test items did not
    have enough discrimination power.
  • The results of analyses also indicated that for
    grades 3 through 5, low scoring students
    tend to remain classified as LEP in most of these
    districts. As grade level increases,
    however, the variation in agreement among
    districts also increases. There appears to
    be an increasing tendency to reclassify low
    scoring students as grade level increases.
  • The results of our analyses also suggested that
    there was not any single criterion that
    highly correlates with LEP classification code.
    This may be due to psychometric
    characteristics of the measures or due to issues
    on the validity of LEP classification or,
    most likely, a combination of both.
  • Thus, the use of multiple criteria is recommended
    in assessments particularly in high- stakes
    assessments (e.g., in LEP classification).
    However, technical issues in using multiple
    criteria must be considered.
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