- Split View
-
Views
-
Cite
Cite
Stephanie W Cawthon, Elizabeth Barker, Johny Daniel, North Cooc, Ana G Vielma, Longitudinal Models of Reading and Mathematics Achievement in Deaf and Hard of Hearing Students, The Journal of Deaf Studies and Deaf Education, Volume 28, Issue 1, January 2023, Pages 115–123, https://doi.org/10.1093/deafed/enac033
- Share Icon Share
Abstract
Deaf and hard of hearing (DHH) students often experience systemic barriers to academic success, especially low expectations of what they know and can do. Longitudinal data analysis is critical to understanding how academic achievement for DHH students progresses over time and where they may need additional support on their academic journey to achieve at the level of their hearing peers. This study provides an analysis of NWEA MAP® Growth™ data from grades 2 to 8 across seven reading and mathematics domains over a period of 5 years. Results indicate that both DHH and hearing students continue to build skills through this period, and that DHH students, contrary to many previously held assumptions, do not necessarily plateau in the elementary grades.
Misinformation about what students is capable of and can achieve often leads to low expectations, inequity, and lack of access (National Deaf Center, 2018). Deaf and hard of hearing (DHH) people have long faced barriers to language, learning, and employment opportunities, leading to significant gaps in postsecondary education and employment outcomes (Garberoglio, et al., 2019; Palmer, Newman, Davidson, & Cawthon, 2020). More specifically, statements such as “deaf people finish high school reading at a fourth-grade level” (e.g., McKeown & McKeown, 2019, p. 507) can still be found throughout the literature. The legacy of early studies (e.g., Conrad, 1979; Furth, 1966) lives on in these claims, with their findings of significant delays in 15–16 year-old DHH children, often at the level of 9-year-old hearing peers. The problem is that this belief is not only damaging in terms of the expectation it sets (Cawthon & Garberoglio, 2017) for what DHH children can achieve—it is also not necessarily true—and leads to an overly simplistic view of academic potential in this population.
Of the roughly 7 million students age 3–21 receiving special education services under the Individuals with Disabilities Education Act (IDEA), ~75,000 are considered deaf or hard of hearing (NCES, 2018). This figure is likely an undercount because it does not include students who are currently on 504 plans, or who have a hearing loss as a secondary disability to a primary used for IDEA Child Count purposes. Relatively small sample sizes make studies tracking academic trajectories of DHH students both challenging to conduct and difficult to draw inferences from to the broader population.
Much of the literature surrounding academic achievement for DHH students uses the synthesis by Qi and Mitchell (2012) as an anchor for further discussion. The Qi and Mitchell synthesis focused on the data collection on the Stanford Achievement Test Series (SAT) over the past three decades prior to the study’s publication (a further discussion of the SAT 9th edition, national norms for DHH students, and performance standards can be found in Traxler, 2000). Although very helpful in providing context for future research, the reality is that students and curriculum were different across that time span, with students having varying opportunities to learn SAT content (Allen et al. 1983; Cawthon, 2007), so the inferences that can be made based this study may differ from current trends. That said, their study highlights two overarching themes about DHH student academic performance relative to the norms based on hearing students: DHH students perform at a lower level for both reading and math, and the gap is larger for reading than for math. With only a few exceptions, the gaps have persisted throughout the time period included in the Qi and Mitchell analysis, particularly in reading.
Beyond a focus on individual student achievement, there are many systemic barriers that contribute to consistent delays in academic achievement for DHH students as a whole: Negative attitudes about what DHH people can achieve (Cawthon & Garberoglio, 2017), lack of qualified, trained professionals, inaccessible learning environments, and resources that are few and far between (National Deaf Center, 2018). In addition, fewer than 10% of DHH children are born to DHH parents, which sometimes results in reduced access to robust language in early childhood because hearing parents need time to learn how to communicate with their child (Humphries et al., 2019; Mitchell & Karchmer, 2004). This language deprivation can have long-lasting effects even into school years, influencing not only brain development and learning outcomes but also critical development in identity, peer relationships, and mental health (Cheng, Halgren, & Mayberry, 2018; Hall, 2017; Hall, Hall, & Caselli, 2019).
Achievement and opportunity gaps for DHH students are a pervasive theme in the discussion regarding academic outcomes. There is unquestionably a disparity in both the opportunity to learn literacy skills and systemic barriers to DHH children in accessing the language, social, and educational contexts that provide the foundation for literacy. On the one hand, there are certainly significant segments of the DHH population that experience academic delays due to early language deprivation, paucity of evidence-based literacy instruction, and inaccessible learning environments. On the other hand, assumptions that draw upon a deficit model of what DHH people can do, and lowered expectations based on those assumptions, have only contributed to long-term disparities in educational outcomes for DHH people. The remainder of this literature review explores what we know about reading and math achievement in DHH students, as well as inferences that may be available with a longitudinal approach to measuring academic achievement trajectories.
Fourth-Grade Reading Achievement for DHH Students: Fact or Fiction?
The field often defaults to the assumption of a significant, 8-year delay in reading outcomes for DHH students when they complete secondary education. However, more recent research debunks the fourth grade “average” as a myth, showing that some DHH people reach much higher than fourth-grade level by their late adolescent years (e.g., Lund, 2020; Sarant, Harris, & Bennet, 2015; Tomblin, Oleson, Ambrose, Walker, & Moeller, 2018). Although this is still a delay and certainly one that needs to be robustly addressed in early childhood education, there are opportunities to stem the gap of 8 years previously predicted for DHH students by the time they leave compulsory education. That said, age level matters when examining potential deficits in reading. Reading achievement levels vary greatly in the early grades across all students (Paris & Hoffman, 2004), and measures of reaching achievement into the elementary and secondary grades are more salient indicators of life-long literacy achievement. This current study seeks to add to the literature that examines developmental trajectories in reading skills for DHH students.
There are several contextual factors to keep in mind when interpreting the practical significance of scores on reading tests. First, it is crucial to remember that literacy is not one skill, but a carefully coordinated set of skills (Webb et al., 2015). Students may be at different grade levels of proficiency in fluency, decoding, spelling, vocabulary knowledge, reading comprehension, and so forth, depending on any number of factors, including additional disabilities (which are up to half of the DHH population, GRI, 2011). There are also many kinds of literacies that we use, not just the academic literacy typically measured on standardized tests and used in research and benchmarks of academic achievement. For example, there is digital literacy, financial literacy, navigation literacy (e.g., transportation routes), and so forth. These different literacies also draw upon the understanding that there is a difference between learning to read and reading to learn. These are two powerful and related processes, now understood to be happening in parallel: both the development of literacy skills and the application of those skills in content areas such as social studies, science, and other text-based delivery of information. Research suggests that how DHH students use the literacy skills they have is as important to secondary and postsecondary success as their scores on standardized assessments of reading ability (Garberoglio, Dickson, Cawthon, & Bond, 2015).
The Case of Mathematics Achievement
Although there is relatively limited research on math achievement in DHH children compared with reading achievement (Qi & Mitchell, 2012), recent work has highlighted critical factors influencing the math performance of DHH students. In addition to language proficiency, Henner, Pagliaro, Sullivan, and Hoffmeister (2021) examined the effects of age, signing status, gender, American Sign Language (ASL) vocabulary, and learning disabilities on the math ability and achievement of 257 DHH students. Results suggested that all five factors were significant predictors of mathematics achievement on the MAP subtest. In addition, researchers found DHH students surpassed the sample’s mean score of age-related hearing students. As a result, these findings call attention to the complexity of multifaceted identities and contextual factors that influence the academic experience of deaf students (see Goldstein, 2018 for an exploration of math identities and the complex impact of classroom context).
Chen and Wang (2021) add to the literature by exploring this relationship between general and specific cognitive abilities and math performance in 198 DHH students to identify predictors and trends within this population. Using numerous processing tasks and computerized tests, these researchers found various underlying factors such as spatial ability and processing speed that uniquely contributed to and significantly predicted a student’s mathematical performance. Furthermore, this evidence suggests that math achievement for DHH students increasingly relies on general cognitive abilities than specific numerical skills. As a result, for DHH students who are at risk of falling behind, strengthening these cognitive abilities may significantly improve their math performance.
Analyzing Growth Trends: Is It a Deficit or a Delay?
In both reading and math, achievement for disabled students increases over time, but growth rate slows down as students reach middle school (Wei, Blackorby, & Schiller, 2011; Wei, Lenz, & Blackorby, 2013). These two studies from SEELS dataset (Special Education Elementary Longitudinal Study) examined reading and math growth trajectories of students with disabilities, including DHH students, with additional factors such as SES, gender, and race. In terms of growth, the achievement across all students increased rapidly during elementary grades but slowed down as students reached middle school, with few significant differences between disability groups and in line with hearing norms. Furthermore, there were interaction effects for gender, race-ethnicity, and SES level, with persistent gaps for students from historically marginalized communities.
In the disability research literature, particularly in deaf education and deaf studies, there is a growing interest in whether the model of academic achievement should be one that predicts a delay or a deficit trajectory relative to hearing peers. In a model that focuses on delay, students are assumed to have the capacity to catch up over time, even if there is a slower, longer trajectory than hearing peers. This delay may be moderated by intensive intervention or other such supports both at school and at home and assumes that the sequence of learning is largely similar for all students. In deaf education, specifically surrounding literacy, this position is based on the Qualitative Similarity Hypothesis (Andrews & Wang, 2015), and assumes that reading development trajectories are structurally the same for DHH children as their hearing peers, and that interventions should target similar strategies.
In contrast to a delay model, a deficit model assumes that a student, no matter what interventions or supports are available, will never be at the level of their typically developing peers due to an inherent limitation on their learning due to their disability and resultant cognitive functioning. Deficit models may be supported by data illustrating persistent, early plateaus in academic domains or subdomains. For DHH students, language delay and language deprivation can have longstanding, devastating effects on a child’s academic development (Hall, 2017), which could suggest a deficit model of literacy development for at least part of this population. In a deficit model, the instructional focus may be on remediation in areas that are thought to benefit from intervention, instead of a comprehensive approach across all competencies. This would assume a different curricular approach than in the general population and an assumption of an overall lower level of achievement as an end point.
Evidence to support different literacy trajectories may be possible using longitudinal data analysis approaches to studying academic achievement. For example, Francis, Shaywitz, Stuebing, Shaywitz, and Fletcher (1996) conducted an early study of the delay versus deficit model for students with learning disabilities using individual growth curve analyses. This study examined student scores from third grade, about the time that many students are identified as having a reading disability, over the course of the remainder of their compulsory education. Analyses controlled for gender, race, and SES status. The authors hypothesized that changes in reading ability would be nonlinear, showing an initial pattern of rapid acquisition with subsequent slowing in the rate of change, a pattern we have seen throughout the literature. Because outcome data for all students revealed a curvilinear pattern, a quadratic model was utilized to determine growth, but modified to allow for a plateau towards the end of the measurement period (instead of a downward trend). Interestingly, students identified as having a learning disability (using IQ discrepancy model) and students without a learning disability both levelled off at about the same age, around age 13. Results from these analyses indicated that students with a disability (and, in addition, in this study, all students with a lower IQ) had a performance over time reflective of a deficit model of reading development. The delays seen in early childhood were persistent, and students did not “catch up” over time. Furthermore, they were seen to plateau at about the same time as their typically developing peers, indicating a slowdown in the window of time where there might be expected growth with a delayed model of achievement.
Paucity of Longitudinal Research With DHH Students
Research methods within the literature have a significant impact on what we know about academic achievement. Due in part to the focus on grade-level achievement and accountability for schools, the cohort approach to tracking academic performance dominates recent research in the field (Cawthon, 2007). Sample sizes within each grade are small, limiting generalizability of analytical approaches under cohort designs. This means that we know very little about how DHH children develop over time, especially during the critical elementary and middle school years. There are few recent longitudinal data sets to be able to draw conclusions about what overall growth in academic achievement looks like, let alone what different pathways and trajectories might be for the diverse subsets of the DHH population.
Antia, Jones, Reed, and Kreimeyer (2009) provided a summary of early longitudinal studies in the field as well as findings from their 5-year analysis of academic progress in 197 DHH students in general education classrooms. Participants were in grades 2–8 at the start of the study. This analysis is unique in that it combined the use of the state standardized academic results (for students in grades required to take them for state accountability) as well as teacher perceptions of student progress. Students also provided self-ratings on their classroom participation and preferred communication modality. Overall, at least half of DHH students performed at the average or above average range each year in reading, with the percentage increasing from 54 to 66% between the 1st and 5th year of the study. Further examinations of mean slopes indicated that students were making above average gains in their language and writing skills over the course of the 5 years. Results for mathematics, with DHH students performing at over 70% average to above average across all five study years, were stronger in terms of overall proficiency, and with equal gains per year as their hearing peers.
In a more recent study, Antia et al. (2020) examined language and reading development of over 300 children in kindergarten, first, and second grades over the course of a single year. This is often a period of rapid growth for children, making this age range particularly important to look at in terms of trajectory for later literacy development. The students in this study were divided into three groups for analysis based on their communication modalities in the classroom: spoken only, sign only, and bimodal (acquiring both). Findings in this study were mixed. First, overall, study participants began behind the hearing norms at the time of the fall assessments in both language (English and ASL) and literacy skills. Measured again at the end of the school year, the study participants made some gains in language, with students in spoken modalities making gains in English syntax and students using sign or bimodal approaches in ASL syntax. Vocabulary gains showed no difference between the groups. Literacy development, however, was not as consistent nor demonstrative of increases over time. There were decreases in comprehension skills from kindergarten to second grade, and mixed achievement between the three study groups. Rather than results clearly supporting either a deficit versus delay model of achievement, this study raised the possibility of a differential model, with trajectories that may vary depending on both individual and contextual (e.g., additional academic supports, home language supports, and peer interaction) factors.
In contrast with the Antia et al. (2020) academic year time frame, Kyle and Harris (2010, 2011) provide a rare example of longitudinal analysis of early literacy development in DHH and hearing readers across multiple years. Their research examined literacy development with a total of 29 DHH children, beginning at ~7–8 years of age and following for 3 years. Students showed delays in their literacy outcomes, delays that grew over time, on average, but that were highly variable within the group. Interestingly, the authors suggest a possible differential trajectory in the relationship between phonological awareness and reading ability, with earlier reading levels predicting later phonological awareness levels in study participants, and not vice versa after controlling for other factors. This is in contrast with the typical pathway of phonological awareness as an early predictor of later reading ability. Although a small sample size is a limitation to this study that may have affected the power to detect true differences, this study contributes to the concepts of consistent themes of high inter-group variability and possible difference in literacy trajectories for DHH students (see also Andrews, Hamilton, Dunn, & Clark, 2016).
The Current Study
One of the greatest barriers to measurement in deaf education research is that summative standardized assessment in educational settings is rarely designed to measure growth. Alternatively, research and intervention studies can include growth measures within their own designs, but this is a step that is not always possible nor desired given the extensive testing already present in school settings (in the USA, at least). The opportunity to leverage an in situ standardized assessment is thus rare, particularly one that spans multiple grades and multiple years and sufficient sample size of DHH students across multiple school sites. Such an opportunity was afforded to this research team by the NWEA MAP® Growth™ Research Database, with 5 years of data for DHH students in seven elementary and middle school grades.
This study has the following objectives:
Compare longitudinal outcomes for reading and mathematics for DHH and hearing students participating in the NWEA MAP® Growth™ data from grades 2 to 8 over a period of 5 years.
Examine possible differences in developmental trajectories within a total of seven reading and mathematics subdomains.
Methods
Data Source
This collaboration represents expertise from contributors in the field of deaf education, special education, and the use of MAP® Growth™ data. MAP Growth is designed to measure progress over a student’s entire school career with a single metric. MAP Growth assesses the students up to 3 times per year (fall, winter, and spring). School districts choose to administer MAP Growth assessments for a variety of purposes, including monitoring student achievement and growth. MAP Growth is a computer adaptive assessment designed for test items to match the ability of the student (Thum & Kuhfeld, 2020). All the items within MAP Growth are calibrated to the same scale using the Rasch one parameter logistic (1PL) item response theory (IRT). When taking a MAP Growth assessment students will receive different items based on the item difficulty and the estimates of the student’s ability level. The result of the final ability estimate is a RIT score or a Rasch Unit (Thum & Kuhfeld, 2020).
We used a unique data set from NWEA Growth Research Database, which examined academic progress over time for 351 2–8 DHH students, primarily students attending schools for the deaf where the MAP Growth assessment tool is used as part of interim assessment. Across the data collection years, the number of schools for which academic data were collected for DHH students ranged from 33 to 94 schools. Although many schools for the deaf include ASL as part of their pedagogy, school-level data about language use in the classroom or student-level data regarding preferred language modalities are not available for this dataset. This data set also did not have delineation between identification of “deaf” versus “hard of hearing” or other further breakdown within this category, such as type of amplification (e.g., hearing aids, cochlear implants, if any) used. In this study, we compared DHH students with a group of peers without disabilities, denoted as “hearing” for the purpose of this analysis. Hearing students attended school in general education settings.
Measures
Table 1 presents the descriptive statistics for the sample of students who were identified as DHH and their hearing peers. Across both subgroups, a little over half the students were males. In addition, just over half of students identified themselves as white in the DHH (58%) and the hearing (41%) subgroups, with similar proportions of Black students (12% and 13% for DHH and hearing, respectively) but lower proportions of Hispanic students (7% and 19% for DHH and hearing, respectively) and higher percentage of American Indian/Alaska Native students (15 and 2% for DHH and hearing, respectively).
Characteristics . | DHH (n = 351) . | Hearing (n = 6,655) . |
---|---|---|
Gender Male Female Not reported | 201 (57%) 150 (43%) NA | 3,566 (54%) 3,074 (46%) 15 (<1%) |
Race White Black Hispanic American Indian/Alaska Native Asian/Pacific Islander Other | 206 (58%) 41 (12%) 23 (7%) 52 (15%) 18 (5%) 11 (3%) | 2,700 (41%) 845 (13%) 1,277 (19%) 124 (2%) 298 (4%) 1,411 (21%) |
Characteristics . | DHH (n = 351) . | Hearing (n = 6,655) . |
---|---|---|
Gender Male Female Not reported | 201 (57%) 150 (43%) NA | 3,566 (54%) 3,074 (46%) 15 (<1%) |
Race White Black Hispanic American Indian/Alaska Native Asian/Pacific Islander Other | 206 (58%) 41 (12%) 23 (7%) 52 (15%) 18 (5%) 11 (3%) | 2,700 (41%) 845 (13%) 1,277 (19%) 124 (2%) 298 (4%) 1,411 (21%) |
Note. DHH = students who are deaf and hard of hearing; Hearing = students without disabilities
Characteristics . | DHH (n = 351) . | Hearing (n = 6,655) . |
---|---|---|
Gender Male Female Not reported | 201 (57%) 150 (43%) NA | 3,566 (54%) 3,074 (46%) 15 (<1%) |
Race White Black Hispanic American Indian/Alaska Native Asian/Pacific Islander Other | 206 (58%) 41 (12%) 23 (7%) 52 (15%) 18 (5%) 11 (3%) | 2,700 (41%) 845 (13%) 1,277 (19%) 124 (2%) 298 (4%) 1,411 (21%) |
Characteristics . | DHH (n = 351) . | Hearing (n = 6,655) . |
---|---|---|
Gender Male Female Not reported | 201 (57%) 150 (43%) NA | 3,566 (54%) 3,074 (46%) 15 (<1%) |
Race White Black Hispanic American Indian/Alaska Native Asian/Pacific Islander Other | 206 (58%) 41 (12%) 23 (7%) 52 (15%) 18 (5%) 11 (3%) | 2,700 (41%) 845 (13%) 1,277 (19%) 124 (2%) 298 (4%) 1,411 (21%) |
Note. DHH = students who are deaf and hard of hearing; Hearing = students without disabilities
Outcome measures are disaggregated across seven subscales:
Math: Algebra
Math: Geometry
Math: Numbers and operations
Math: Measurement and data
Reading: Informational Text
Reading: Literature
Reading: Vocabulary Acquisition
Test Construction
MAP Growth assessments are aligned to 2nd through 10th grade state standards in mathematics, English language arts, and science. The standards are analyzed and reviewed by content experts organizing critical concepts and progressions within the standards into a framework for each assessment. This two-tier framework contains the instructional areas and sub-areas that form the structure of the test. For example, the reading assessment aligned to the Common Core State Standards has three instructional areas: literary text, informational text, and vocabulary. Items in MAP Growth assessments come from the robust NWEA item bank. Items in MAP Growth tests are carefully selected and aligned to individual state standards by NWEA Content Specialists. Items are only aligned to individual standard statements when the content within the item clearly assessed the concept within the standard at the appropriate reading level, difficulty level, and level of cognitive complexity. Each item alignment is reviewed by at least one Content Specialist who was not involved in the initial alignment process. As a result of this process, each item in the assessment item pool has a confirmed alignment to a state standard. Creating tests in this manner means that they align tightly to the standards and provide an accurate measure of student achievement. For more information about the constructs measured within each subscale, see the MAP Growth website: https://www.nwea.org/map-growth/.
Analysis
Missing data were detected primarily in the outcome measures as not all students were tested at each wave. Missingness range varied by measure. Of the total DHH sample, data were unavailable for 12–19% of the sample on various reading measures, and 23–53% unavailable on various math measures. The multilevel model of change, however, handles missing or unbalanced data (Singer & Willet, 2003). That is, every student record contributes the estimation, regardless of how many records are available per student. All analyses were conducted in R (R Core Team, 2017) using the lme4 package (Bates et al., 2015).
Results
Results from the mixed method growth curve modeling analyses are presented in Tables 2 and 3, respectively. As shown in Table 2, controlling for the year data were collected, and students’ linear and quadratic growth rates, hearing students perform significantly better than their DHH peers in comprehension of informational text (B = 1.04, p < .05, d = .03), comprehension of literary text (B = 1.11, p < .05, d = .04), and vocabulary acquisition (B = 1.06, p < .05, d = .03). The one-point difference between hearing and DHH students is equivalent to about .03 to .04 standard deviations, SD—small when compared to racial differences of .50 SD but similar to some intervention effects (Lipsey et al., 2012). Similarly, as shown in Table 3, the results indicate that, controlling for the year data were collected, and students’ linear and quadratic growth rates, hearing students perform significantly better than their DHH peers in algebra (B = 2.89, p < .01, d = .08), geometry (B = 3.14, p < .01, d = .08), measurement and data (B = 2.12, p < .01, d = .06), and numbers and operations (B = 2.15, p < .01, d = .05) related tasks. The standardized differences, .05–.08, between hearing and deaf students in reading are about twice of those in math but remain small.
. | Informational text . | Literary text . | Vocabulary acquisition . |
---|---|---|---|
Coefficient (SE) | Coefficient (SE) | Coefficient (SE) | |
Fixed effect | |||
Intercept | 142.93** (.92) | 142.35** (1.33) | 144.45** (1.34) |
Group (hearing) | 1.04* (.44) | 1.11* (.44) | 1.06* (.45) |
Grade | 6.56** (.36) | 6.84** (.36) | 6.51** (.36) |
Grade2 | −0.23** (.03) | −0.25** (.03) | −.23** (.03) |
Year (2014) | 1.56** (.24) | 1.37** (.25) | — |
Year (2015) | 2.58** (.28) | 2.27** (.30) | 1.33** (.22) |
Year (2016) | 4.12** (.34) | 3.70** (.35) | 2.74** (.28) |
Year (2017) | 5.31** (.42) | 4.76** (.42) | 4.02** (.35) |
Year (2018) | 7.04** (.48) | 6.42** (.49) | 5.74** (.42) |
Random effect | |||
Student τoo | 192.20 | 198.94 | 165.25 |
Student τ01 | 5.27 | 5.21 | 5.19 |
School τ11 | 36.81 | 38.20 | 43.05 |
School τ02 | 1.69 | 1.60 | 1.61 |
Residual (σ2) | 35.81 | 36.73 | 35.40 |
N (Student) | 3,845 | 3,854 | 3,715 |
N (School) | 91 | 90 | 92 |
Observations | 16,897 | 16,747 | 15,549 |
Conditional R2 | .931 | .928 | .931 |
. | Informational text . | Literary text . | Vocabulary acquisition . |
---|---|---|---|
Coefficient (SE) | Coefficient (SE) | Coefficient (SE) | |
Fixed effect | |||
Intercept | 142.93** (.92) | 142.35** (1.33) | 144.45** (1.34) |
Group (hearing) | 1.04* (.44) | 1.11* (.44) | 1.06* (.45) |
Grade | 6.56** (.36) | 6.84** (.36) | 6.51** (.36) |
Grade2 | −0.23** (.03) | −0.25** (.03) | −.23** (.03) |
Year (2014) | 1.56** (.24) | 1.37** (.25) | — |
Year (2015) | 2.58** (.28) | 2.27** (.30) | 1.33** (.22) |
Year (2016) | 4.12** (.34) | 3.70** (.35) | 2.74** (.28) |
Year (2017) | 5.31** (.42) | 4.76** (.42) | 4.02** (.35) |
Year (2018) | 7.04** (.48) | 6.42** (.49) | 5.74** (.42) |
Random effect | |||
Student τoo | 192.20 | 198.94 | 165.25 |
Student τ01 | 5.27 | 5.21 | 5.19 |
School τ11 | 36.81 | 38.20 | 43.05 |
School τ02 | 1.69 | 1.60 | 1.61 |
Residual (σ2) | 35.81 | 36.73 | 35.40 |
N (Student) | 3,845 | 3,854 | 3,715 |
N (School) | 91 | 90 | 92 |
Observations | 16,897 | 16,747 | 15,549 |
Conditional R2 | .931 | .928 | .931 |
Note. Comparison group is DHH students. In 2013, vocabulary acquisition data were not available for the sample.
* p < .05, ** p < .01, and *** p < .001
. | Informational text . | Literary text . | Vocabulary acquisition . |
---|---|---|---|
Coefficient (SE) | Coefficient (SE) | Coefficient (SE) | |
Fixed effect | |||
Intercept | 142.93** (.92) | 142.35** (1.33) | 144.45** (1.34) |
Group (hearing) | 1.04* (.44) | 1.11* (.44) | 1.06* (.45) |
Grade | 6.56** (.36) | 6.84** (.36) | 6.51** (.36) |
Grade2 | −0.23** (.03) | −0.25** (.03) | −.23** (.03) |
Year (2014) | 1.56** (.24) | 1.37** (.25) | — |
Year (2015) | 2.58** (.28) | 2.27** (.30) | 1.33** (.22) |
Year (2016) | 4.12** (.34) | 3.70** (.35) | 2.74** (.28) |
Year (2017) | 5.31** (.42) | 4.76** (.42) | 4.02** (.35) |
Year (2018) | 7.04** (.48) | 6.42** (.49) | 5.74** (.42) |
Random effect | |||
Student τoo | 192.20 | 198.94 | 165.25 |
Student τ01 | 5.27 | 5.21 | 5.19 |
School τ11 | 36.81 | 38.20 | 43.05 |
School τ02 | 1.69 | 1.60 | 1.61 |
Residual (σ2) | 35.81 | 36.73 | 35.40 |
N (Student) | 3,845 | 3,854 | 3,715 |
N (School) | 91 | 90 | 92 |
Observations | 16,897 | 16,747 | 15,549 |
Conditional R2 | .931 | .928 | .931 |
. | Informational text . | Literary text . | Vocabulary acquisition . |
---|---|---|---|
Coefficient (SE) | Coefficient (SE) | Coefficient (SE) | |
Fixed effect | |||
Intercept | 142.93** (.92) | 142.35** (1.33) | 144.45** (1.34) |
Group (hearing) | 1.04* (.44) | 1.11* (.44) | 1.06* (.45) |
Grade | 6.56** (.36) | 6.84** (.36) | 6.51** (.36) |
Grade2 | −0.23** (.03) | −0.25** (.03) | −.23** (.03) |
Year (2014) | 1.56** (.24) | 1.37** (.25) | — |
Year (2015) | 2.58** (.28) | 2.27** (.30) | 1.33** (.22) |
Year (2016) | 4.12** (.34) | 3.70** (.35) | 2.74** (.28) |
Year (2017) | 5.31** (.42) | 4.76** (.42) | 4.02** (.35) |
Year (2018) | 7.04** (.48) | 6.42** (.49) | 5.74** (.42) |
Random effect | |||
Student τoo | 192.20 | 198.94 | 165.25 |
Student τ01 | 5.27 | 5.21 | 5.19 |
School τ11 | 36.81 | 38.20 | 43.05 |
School τ02 | 1.69 | 1.60 | 1.61 |
Residual (σ2) | 35.81 | 36.73 | 35.40 |
N (Student) | 3,845 | 3,854 | 3,715 |
N (School) | 91 | 90 | 92 |
Observations | 16,897 | 16,747 | 15,549 |
Conditional R2 | .931 | .928 | .931 |
Note. Comparison group is DHH students. In 2013, vocabulary acquisition data were not available for the sample.
* p < .05, ** p < .01, and *** p < .001
. | Algebra . | Geometry . | Measurement and data . | Numbers and operations . |
---|---|---|---|---|
Coefficient (SE) | Coefficient (SE) | Coefficient (SE) | Coefficient (SE) | |
Fixed effect | ||||
Intercept | 144.14** (1.46) | 144.15** (1.37) | 143.45** (1.89) | 142.38** (1.81) |
Group (hearing) | 2.89** (.54) | 3.14** (.52) | 2.12** (.62) | 2.15** (.72) |
Grade | 8.37** (.37) | 8.47** (.34) | 9.43** (.35) | 9.14** (.53) |
Grade2 | −.36** (.03) | −.35** (.03) | −.58** (.05) | −.51** (.05) |
Year (2014) | 0.83* (.27) | 0.40 (.24) | - | 0.91** (.29) |
Year (2015) | 1.82** (.31) | 1.30** (.27) | 0.97** (.29) | 1.42** (.36) |
Year (2016) | 2.72** (.36) | 2.10** (.32) | 2.92** (.36) | 3.13** (.43) |
Year (2017) | 3.74** (.42) | 3.26** (.37) | 4.99** (.44) | 4.54** (.50) |
Year (2018) | 6.25** (.48) | 5.68** (.43) | 8.21** (.52) | 7.55** (.58) |
Random effect | ||||
Student τoo | 217.45 | 212.33 | 182.66 | 176.58 |
Student τ01 | 4.81 | 4.75 | 6.39 | 6.18 |
School τ11 | 53.53 | 53.55 | 74.42 | 70.06 |
School τ02 | 1.92 | 1.82 | 4.31 | 3.90 |
Residual (σ2) | 44.69 | 44.68 | 47.41 | 46.72 |
N (Student) | 4,121 | 4,797 | 2,816 | 3,017 |
N (School) | 91 | 97 | 84 | 81 |
Observations | 18,430 | 21,226 | 10,322 | 11,386 |
Conditional R2 | .910 | .909 | .883 | .889 |
. | Algebra . | Geometry . | Measurement and data . | Numbers and operations . |
---|---|---|---|---|
Coefficient (SE) | Coefficient (SE) | Coefficient (SE) | Coefficient (SE) | |
Fixed effect | ||||
Intercept | 144.14** (1.46) | 144.15** (1.37) | 143.45** (1.89) | 142.38** (1.81) |
Group (hearing) | 2.89** (.54) | 3.14** (.52) | 2.12** (.62) | 2.15** (.72) |
Grade | 8.37** (.37) | 8.47** (.34) | 9.43** (.35) | 9.14** (.53) |
Grade2 | −.36** (.03) | −.35** (.03) | −.58** (.05) | −.51** (.05) |
Year (2014) | 0.83* (.27) | 0.40 (.24) | - | 0.91** (.29) |
Year (2015) | 1.82** (.31) | 1.30** (.27) | 0.97** (.29) | 1.42** (.36) |
Year (2016) | 2.72** (.36) | 2.10** (.32) | 2.92** (.36) | 3.13** (.43) |
Year (2017) | 3.74** (.42) | 3.26** (.37) | 4.99** (.44) | 4.54** (.50) |
Year (2018) | 6.25** (.48) | 5.68** (.43) | 8.21** (.52) | 7.55** (.58) |
Random effect | ||||
Student τoo | 217.45 | 212.33 | 182.66 | 176.58 |
Student τ01 | 4.81 | 4.75 | 6.39 | 6.18 |
School τ11 | 53.53 | 53.55 | 74.42 | 70.06 |
School τ02 | 1.92 | 1.82 | 4.31 | 3.90 |
Residual (σ2) | 44.69 | 44.68 | 47.41 | 46.72 |
N (Student) | 4,121 | 4,797 | 2,816 | 3,017 |
N (School) | 91 | 97 | 84 | 81 |
Observations | 18,430 | 21,226 | 10,322 | 11,386 |
Conditional R2 | .910 | .909 | .883 | .889 |
Note. Comparison group is DHH students. 2Quadratic Growth Rate.
*p < .05, * p < .01, and *** p < .001
. | Algebra . | Geometry . | Measurement and data . | Numbers and operations . |
---|---|---|---|---|
Coefficient (SE) | Coefficient (SE) | Coefficient (SE) | Coefficient (SE) | |
Fixed effect | ||||
Intercept | 144.14** (1.46) | 144.15** (1.37) | 143.45** (1.89) | 142.38** (1.81) |
Group (hearing) | 2.89** (.54) | 3.14** (.52) | 2.12** (.62) | 2.15** (.72) |
Grade | 8.37** (.37) | 8.47** (.34) | 9.43** (.35) | 9.14** (.53) |
Grade2 | −.36** (.03) | −.35** (.03) | −.58** (.05) | −.51** (.05) |
Year (2014) | 0.83* (.27) | 0.40 (.24) | - | 0.91** (.29) |
Year (2015) | 1.82** (.31) | 1.30** (.27) | 0.97** (.29) | 1.42** (.36) |
Year (2016) | 2.72** (.36) | 2.10** (.32) | 2.92** (.36) | 3.13** (.43) |
Year (2017) | 3.74** (.42) | 3.26** (.37) | 4.99** (.44) | 4.54** (.50) |
Year (2018) | 6.25** (.48) | 5.68** (.43) | 8.21** (.52) | 7.55** (.58) |
Random effect | ||||
Student τoo | 217.45 | 212.33 | 182.66 | 176.58 |
Student τ01 | 4.81 | 4.75 | 6.39 | 6.18 |
School τ11 | 53.53 | 53.55 | 74.42 | 70.06 |
School τ02 | 1.92 | 1.82 | 4.31 | 3.90 |
Residual (σ2) | 44.69 | 44.68 | 47.41 | 46.72 |
N (Student) | 4,121 | 4,797 | 2,816 | 3,017 |
N (School) | 91 | 97 | 84 | 81 |
Observations | 18,430 | 21,226 | 10,322 | 11,386 |
Conditional R2 | .910 | .909 | .883 | .889 |
. | Algebra . | Geometry . | Measurement and data . | Numbers and operations . |
---|---|---|---|---|
Coefficient (SE) | Coefficient (SE) | Coefficient (SE) | Coefficient (SE) | |
Fixed effect | ||||
Intercept | 144.14** (1.46) | 144.15** (1.37) | 143.45** (1.89) | 142.38** (1.81) |
Group (hearing) | 2.89** (.54) | 3.14** (.52) | 2.12** (.62) | 2.15** (.72) |
Grade | 8.37** (.37) | 8.47** (.34) | 9.43** (.35) | 9.14** (.53) |
Grade2 | −.36** (.03) | −.35** (.03) | −.58** (.05) | −.51** (.05) |
Year (2014) | 0.83* (.27) | 0.40 (.24) | - | 0.91** (.29) |
Year (2015) | 1.82** (.31) | 1.30** (.27) | 0.97** (.29) | 1.42** (.36) |
Year (2016) | 2.72** (.36) | 2.10** (.32) | 2.92** (.36) | 3.13** (.43) |
Year (2017) | 3.74** (.42) | 3.26** (.37) | 4.99** (.44) | 4.54** (.50) |
Year (2018) | 6.25** (.48) | 5.68** (.43) | 8.21** (.52) | 7.55** (.58) |
Random effect | ||||
Student τoo | 217.45 | 212.33 | 182.66 | 176.58 |
Student τ01 | 4.81 | 4.75 | 6.39 | 6.18 |
School τ11 | 53.53 | 53.55 | 74.42 | 70.06 |
School τ02 | 1.92 | 1.82 | 4.31 | 3.90 |
Residual (σ2) | 44.69 | 44.68 | 47.41 | 46.72 |
N (Student) | 4,121 | 4,797 | 2,816 | 3,017 |
N (School) | 91 | 97 | 84 | 81 |
Observations | 18,430 | 21,226 | 10,322 | 11,386 |
Conditional R2 | .910 | .909 | .883 | .889 |
Note. Comparison group is DHH students. 2Quadratic Growth Rate.
*p < .05, * p < .01, and *** p < .001
In both Tables 1 and 2, the growth parameters show a positive instantaneous rate of change and a negative curvature estimate. In other words, the trajectory initially rises and increases but this increase does not persist. Figures 1 and 2 show the growth curve results for reading and math domains, respectively. In reading, all students on average scored approximately six standard points higher each year and their reading-related growth was curvilinear and decelerating. In Figure 2, across different math measures, all students on average scored about eight standard points higher each year as they progressed from grades 2 to 8. As hypothesized, the quadratic term in the models were significant and demonstrated that academic growth across both groups of students was curvilinear and decelerating. Finally, we fitted models to identify if an interaction existed between the two groups and their linear or quadratic growth. The interaction terms were not significant; to present parsimonious models, we dropped the interaction terms from Tables 2 and 3. Box plots of the score distributions at each grade level, for each analysis, are provided in the Appendix.
Discussion
Our key finding is that in both reading and mathematics, we found small but persistent gaps in growth scores between DHH and hearing students. There was, however, steady growth for both groups throughout the time period measured, findings that parallel with Antia et al. (2009) where the majority of students made at least 1 year’s gain in math and reading. Both DHH and hearing students in this sample are making the same relative gains year to year throughout the data analysis period, a finding that holds across all domains in reading and mathematics.
This analysis largely supported a delayed model of achievement for DHH students. Perhaps because DHH students often start with delayed access to both language and learning opportunities, DHH students in this study started with second grade reading and mathematics scores behind their hearing peers. This means that there is more for them to learn within their elementary and middle school grades to reach the same level as their peers. The good news in these data is that, looking at the rate of growth, DHH students are progressing throughout the time period relative to their hearing peers, without a significant plateau that might be indicative of a deficit model of development. MAP Growth data indicates that there is much reason to be optimistic and positive about the progress DHH students are making year to year. This is very powerful information not only for English instruction, but also for subject areas that may rely on text comprehension skills—such as social studies—where students may need to access information through text-based materials.
Our encouragement to students, parents, and educators is this: What is happening within these educational settings seems to be working, and we encourage educators and families to continue to have high expectations for DHH student achievement. Although our findings cannot make a causal link between specific teaching practices and student outcomes, the overall trends indicate that schools are succeeding in supporting DHH students in their growth journey through elementary and middle school grades in the key content areas of reading and math. The assumption of a fourth-grade average reading level for DHH students when they finish high school is inconsistent with the trajectories suggested by these data. Indeed, this assumption is unlikely given that the growth trajectories from grade 2 to 8 for both groups, despite the small gaps, are similar in shape and curve upward over time. However, these results also point to the critical importance of early childhood opportunities for access to the language, social, and academic experiences as DHH children enter formal education. These data indicated that DHH students are starting behind in early elementary grades, and, at least as far as achievement on standardized tests of reading and math, do not fully catch up to their hearing peers. We need to continue to improve access to both linguistically rich environments and high-quality instruction starting from early childhood.
A few limitations need consideration when interpreting the results of this study. First, our sample is not nationally representative even though drawn from schools for the deaf across the country. The majority of DHH students in the USA are enrolled in general education settings, not in the schools and programs for the deaf that were sampled in this dataset. We were also unable to break the findings down by race and ethnicity, additional disability, language modality of instruction, socio economic status, or by other school factors due to either lack of data or insufficient sample size. Previous research has shown that measures of average progress masks what can be very different trajectories for DHH students with different backgrounds and intersectional identities (see slope analysis from Antia et al., 2009; differential trajectories in Garberoglio, Johnson, Sales, & Cawthon, 2021). This study cannot add insight into what kinds of teaching approaches may result in stronger achievement outcomes and/or be a better fit for diverse student populations with different developmental trajectories (Easterbrooks et al., 2015). Finally, this study only included measures of academic achievement and does not include important linguistic and cultural factors that may either predict or co-occur with literacy and mathematics development. There is a growing area of research in the field that seeks to make more direct connections between the socio-linguistic experiences of DHH children and their academic development (e.g., Allen, Letteri, Choi, & Dang, 2014; Andrews et al., 2016). Conclusions about deficit versus delay models of academic achievement for DHH students require comprehensive understanding of the familial, linguistic, social, educational, and community milieu in which development occurs (Humphries et al., 2019).
Conclusion
Results from this study indicate a continued need for robust data collection of student achievement for a diverse set of DHH students, with samples large enough to examine what may be critical disparities in opportunity for growth between student racial and ethnic subgroups that are not available in this analysis (National Deaf Center, 2017). This is a call to larger systems, either at the state or federal level, to design mindful, comprehensive ways to understand learning trajectories of disabled students, including DHH students, in ways that can be tracked over time and analyzed with intersectional identities in mind. Current assessments built into accountability reforms typically are not designed to measure diverse developmental pathways, nor are they publicly available for the kind of analyses that can reveal key patterns and current trends in learning outcomes for students. Assessment systems with multiple data points, important contextual information, and a broad range of outcomes are critical for the building of data-driven policy and practice.
The power of high expectations (Cawthon & Garberoglio, 2017), particularly from parents and teachers, encourages us to be mindful of the potential negative impact of the “fourth-grade achievement level” assumption. Sustainable educational change, at the individual student, group, school levels, remains a significant challenge and essential goal for all school systems. There are still significant disparities in outcomes for DHH students, and systemic barriers to education continue to play a significant role in the lives of DHH people (e.g., Palmer et al., 2020). Data collection and research play an important role in how well the field understands factors that support DHH students in their overall development. Each study contributes a piece of the puzzle—the variety of frameworks and approaches only enriches how we approach DHH learning trajectories.
Conflicts of Interest
No conflicts of interest were reported.
References
Bates, D., Mächler, M., Bolker, B., & Walker, S. (
Cawthon, S. W. (
Conrad, R. (
Furth, H. G. (
McKeown, C., McKeown, J. (
R Core Team. (