ASL vocabulary knowledge eliminates the advantage of
Deaf parents for English reading comprehension
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Hoffmeister, R., Novogrodsky, R., Caldwell-Harris, C., Fish, S., Benedict, R.
The Center for the Study of Communication and the Deaf, Boston University
Background Methodology v In hearing children, age is a very strong predictor of
reading comprehension.
v Vocabulary knowledge in L1 is correlated with reading
comprehension in L2 (Lindsey, Manis & Bailey, 2003;
Proctor, August, Carlo & Snow, 2006, among others).
v A similar relationship is found between signed
languages (L1) and reading comprehension in spoken
languages (L2) despite different modalities
(Chamberlain & Mayberry, 2000; Hermans, Ormel &
Knoors, 2010; Hoffmeister, 2000; Lichtenstein, 1998;
Miller, et al., 2012).
v DCDP have an advantage over DCHP on language
tasks, due to cognitive and linguistic benefits of early
language exposure. (Hermans, Knoors, & Verhoeven,
2008; Novogrodsky, Fish & Hoffmeister, 2014).
v The current study compared the relative strength of
three variables as predictors for reading proficiency in
Deaf students:
1.  Age
2.  L1 (ASL) language proficiency
3.  Parental hearing status
v Participants
v Two groups of Deaf students aged 7-18 (Table 1) years
were tested on an ASL Antonym vocabulary task and
one of two English reading comprehension tests.
v  Procedure
v  The Antonym task is a computer based internet test
(Hoffmeister, et al., 2010).
v  Multiple-choice task with five video screens
v  Students choose one response matching the stimulus
video (see sample below)
v  Students’ responses are automatically recorded in
central server.
v  All data is fed into a database that includes
background information on students
v  Students took two reading tests
v  SAT9-RC Stanford Achievement Test (Reading
Comprehension)
v  MAP-R Reading Measures of Academic Progress test
(Northwest Evaluation Association, 2005).
Sample Antonym Question DAY FIRST WATER Results •  Antonym scores, age, and parental hearing status (Table 2 & 3) as predictors of reading scores Table 2: SAT9-­‐RC Zero-­‐order correlation Stepwise Multiple Regression r p b β
R2 p Antonym .60 <.000 1.21 .54 .35 <.000 Age .41 <.000 6.64 .37 .10 <.000 Parents .22 =.01 Excluded during multiple regression Cumulative .45 <.01 Table 3: MAP Reading Zero-­‐order correlation Stepwise Multiple Regression r p b β
R2 p Antonym .53 <.000 .31 .46 .28 <.000 Age .13 =.07 1.46 .27 .05 <.000 Parents .23 =.001 -­‐11.23 .29 .02 <.031 .35 <.01 Cumulative Discussion Table 1: Participants n
MAP-­‐R
SAT-­‐RC
DCDP
83
37
DCHP
108
101
Total
191
138
Partial funding for this research is provided by USDEd grant R324A100176 to the Trustees of Boston
University. However, this research does not necessarily represent the policy of the USDEd, and you should not
assume endorsement by the Federal Government. We would like to thank the students, teachers, and staff at
the data collection schools, for without their support and participation, this research would not be possible.
NIGHT BRIGHT Disclaimer: The English glosses were added on poster for ease of audience
comprehension & are not on the test itself.
v Vocabulary knowledge in ASL (L1) mediates English reading
comprehension (L2).
v L1 vocabulary knowledge rather than parental hearing status
tended to be the strongest predictor of L2 reading proficiency.
v Intervention strategies for improving L2 reading comprehension
for Deaf students should include enhancement of the L1 (ASL),
as a strong L1 aids not only communication but is also a
necessary foundation for academic achievement.
v Similarly to what has been found for monolingual & bilingual
hearing children, L1 language proficiency is the key for reading
comprehension achievement in bilingual Deaf children.