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The Journal of Deaf Studies and Deaf Education logoLink to The Journal of Deaf Studies and Deaf Education
. 2017 Aug 22;22(4):393–401. doi: 10.1093/deafed/enx029

Achievement, Language, and Technology Use Among College-Bound Deaf Learners

Kathryn Crowe 1,2,*, Marc Marschark 2,3, Jesper Dammeyer 4, Christine Lehane 4
PMCID: PMC5881277  PMID: 28961872

Abstract

Deaf learners are a highly heterogeneous group who demonstrate varied levels of academic achievement and attainment. Most prior research involving this population has focused on factors facilitating academic success in young deaf children, with less attention paid to older learners. Recent studies, however, have suggested that while factors such as early cochlear implantation and early sign language fluency are positively associated with academic achievement in younger deaf children, they no longer predict achievement once children reach high school age. This study, involving data from 980 college-bound high school students with hearing loss, examined relations between academic achievement, communication variables (audiological, language), and use of assistive technologies (e.g., cochlear implants [CIs], FM systems) and other support services (e.g., interpreting, real-time text) in the classroom. Spoken language skills were positively related to achievement in some domains, while better sign language skills were related to poorer achievement in others. Among these college-bound students, use of CIs and academic support services in high school accounted for little variability in their college entrance examination scores.

Understanding Deaf Learners’ Outcomes

For well over 30 years, investigators have recognized our inability to account for most of the variability in the academic outcomes of deaf learners, as indicated by either standardized achievement testing or level of degree attainment (Carlberg & Kavale, 1980; Dammeyer & Marschark, 2016; Kluwin & Moores, 1985; Leigh & Crowe, 2015; Leigh & Marschark, 2016; Rydberg, Gellerstedt, & Danermark, 2009; Stinson & Kluwin, 2011). It now has been shown that factors like hearing thresholds, language modality, school placement, and other child and family characteristics can be important contributors to deaf children's educational progress, but no one of them alone is sufficient to predict the academic outcomes of individual deaf learners or groups of deaf learners. This does not make those factors any less interesting or worthy of investigation. Instead, it suggests that rather than evaluating the extent to which any single variable (e.g., cochlear implant [CI] use or preferred language modality) is significantly related to academic functioning, a broader understanding of deaf learners’ academic outcomes might be gained through multifactorial investigations of variables that co-occur among deaf learners in educational settings (e.g., CI use and preferred language modality).

Given the acknowledged diversity in the deaf population with regard to hearing thresholds, language fluencies, modality preferences, socio-cultural backgrounds, and so on, our understanding of contributors to those outcomes is well-served by investigations that involve relatively large, and relatively diverse samples of deaf individuals. Small, homogeneous samples, consisting of, for example, native signers in a single community, students in a bilingual classroom, or successful CI users from a single implant center, can be informative in guiding further research, but they are not very enlightening with regard to the larger issue of predicting or understanding academic outcomes of deaf learners at large. In an effort to move toward a broader understanding of academic outcomes for deaf learners, the present study was designed as a large sample, multi-factor study following from findings with regard to two factors generally assumed to be potent predictors of academic outcomes among deaf learners: language modality and the use of technology, including CIs and hearing aids, in education settings.

Benefits to Deaf Learners’ Academic Outcomes: Now You See ‘em Now You Don't

Variability in the benefits to spoken language for young deaf children who receive CIs is “notoriously high” (Niparko et al., 2010, p. 1498). Nevertheless, a variety of studies has documented benefits of pediatric cochlear implantation for early reading achievement (Damen, van den Oever-Goltstein, Langereis, Chute, & Mylanus, 2006; Geers, 2003; Vermeulen, Van Bon, Schreuder, Knoors, & Snik, 2007; Nittrouer, & Caldwell-Tarr, 2016). “Early” is an important qualifier here, because such benefits are primarily evident during the elementary school years (age 6–11 years), and several large-sample studies have indicated that these early benefits are greatly attenuated or absent by the high school (age 15–18 years) and college/university years (age 18 and over) (Convertino, Marschark, Sapere, Sarchet, & Zupan, 2009; Geers, Tobey, Moog, & Brenner, 2008; Marschark, Shaver, Nagle, & Newman, 2015).

Gaps in reading achievement between deaf children who use implants and their hearing peers typically become larger with age, just as they do among deaf children who do and do not use CIs (Geers et al., 2008; Harris & Terlektsi, 2011; Thoutenhoofd, 2006). Thoutenhoofd (2006) aptly suggested that as more deaf children receive CIs at younger ages, that pattern might change. Comparison of recent findings to his snapshot of CI users and nonusers in the years 2000–2004, however, indicates that the change has not yet occurred. Nevertheless, while such findings have been obtained in the majority of studies and for the majority of deaf children, some deaf learners do attain reading levels similar to those of their hearing peers, or at least are not as far behind, as do deaf peers who do not use CIs (Easterbrooks & Beal-Alvarez, 2012; Fitzpatrick et al., 2012; Nittrouer & Caldwell-Tarr, 2016). Among high school and college students as well as adults, CI use is generally found to be unrelated to level of degree attainment (Dammeyer & Marschark, 2016), classroom learning (Convertino et al., 2009), vocabulary and world knowledge (Convertino, Borgna, Marschark, & Durkin, 2014), and academic achievement across the curriculum (Marschark et al., 2015).

A similar pattern of early benefits to academic achievement being attenuated or absent among older deaf learners appears to be present in the literature concerning sign language abilities. Studies by Strong and Prinz (1997), Padden and Ramsey (2000), and others have shown significant advantages in reading among young, native-signing deaf children of deaf parents compared to deaf children of hearing parents. However, children in those studies have been predominantly of elementary school age. A study by Nover, Andrews, Baker, Everhart, and Bradford (2002) of 179 8- to 18-year-olds, approximately one-third of whom had deaf parents, found an advantage in reading for deaf children in a bilingual (American Sign Language and English) deaf education program compared to deaf children at large, according to U.S. norms. Although statistically significant, the advantage was small (about 1%) and held only for 8- to 12-year-olds. Similarly, Lange, Lane-Outlaw, Lange, and Sherwood (2013) studied a sample of deaf children in which 95% were of elementary school age, and they reported no advantage for deaf children of deaf parents over deaf children of hearing parents in math or reading skills or in academic growth. Other studies examining reading (Convertino et al., 2009; Marschark et al., 2015; Miller et al., 2012; Miller, Kargin, & Guldenoglu, 2015) as well as achievement in mathematics and science (Convertino et al., 2009; Marschark et al., 2015) have failed to find significant advantages associated with sign language abilities or parental hearing status among deaf children of deaf parents once those students reach high school and college age. Others have found negative associations of sign language and reading (DeLana, Gentry, & Andrews, 2007; Sarchet et al., 2014). Meanwhile, there also has been little evidence for long-term benefits to academic outcomes of bilingual deaf education, as indicated by achievement test scores (usually in reading) or level of degree attainment (Bagga-Gupta, 2004; Dammeyer & Marschark, 2016; Rydberg et al., 2009) beyond the elementary school years. Bilingual deaf education models therefore are now being phased out in a number of European countries that were among the first to implement them (Swanwick et al., 2014).

Several explanations have been offered for the above findings indicating that early language benefits for children who have effective access to language through CIs (spoken language) or deaf parents (sign language) support academic functioning in the early grades but do not do so as well in the later grades (see Marschark & Knoors, in press, for a review). Most obviously, deaf children who demonstrate better language and academic skills in elementary school might be less likely to attract teacher and school interventions and support services as they get older. The language, materials, and instructional goals in school also become more complex in later grades, requiring higher level language abilities (Archbold, 2015; Chung, 2016; Domínguez, Carrillo, González, & Alegria, 2016) and cognitive abilities (Ansell & Pagliaro, 2006; Blatto-Vallee, Kelly, Gaustad, Porter, & Fonzi, 2007; Knoors & Marschark, 2014). All of these factors contribute to the changing academic demands and outcomes as children get older, affecting different learners in different ways. Meanwhile, differences in teacher methods and expectations, peer interactions, and educational technologies also change as children get older, and deaf learners’ language and cognitive abilities likely affect the speed and extent to which they adjust to those differences.

Diverse Predictors of Achievement in a Diverse Population

The apparent disconnect between factors that predict deaf learners’ academic performance in early school years and the later school years clearly is neither abrupt nor ubiquitous. If all, or even some, of the above factors are involved in the changing of academic “inputs” and “outputs,” variability in them will affect the outcomes of relevant investigations. Hence the importance of the earlier noted emphasis on including diverse segments of the deaf population in educational research, especially those in the later stages of formal education. Rydberg et al. (2009), for example, evaluated the impact of bilingual education in Sweden on academic attainment, comparing data from 2,144 individuals born between 1941 and 1980 who had attended schools for the deaf before and during the bilingual deaf education era there. They found that while academic attainment in the deaf population had increased during the bilingual era, so had that of the general Swedish population, meaning that the deaf-hearing achievement gap persisted. Similar findings recently have been obtained in Denmark (Dammeyer & Marschark, 2016).

Marschark et al. (2015) examined data from approximately 500 deaf high school students randomly selected from around the United States to participate in the National Longitudinal Transition Study 2 (NLTS2). Their results indicated that neither the use of CIs nor sign language was significantly associated with achievement at that level, when other factors were controlled. Having attended only mainstream schools was one of the best predictors of achievement scores in reading, mathematics, social studies, and science. However, the NLTS2 was a study of high school students with disabilities, and the Marschark et al. sample of deaf students included a number who had additional challenges, a factor that was significantly related to achievement. Dammeyer and Marschark (2016) found both CI use and having attended a school for the deaf negatively related to academic attainment. However, their sample of 839 Danish 16- to 64-year-olds varied widely in their use of CIs and their educational backgrounds (including schooling before and after the bilingualism era). Both of these studies thus warrant replication with other, large samples.

The present study had a more restricted focus than the large, omnibus studies described above, insofar as it specifically was aimed at predictors of achievement among deaf high school students who were college-bound. The study had two specific goals. One goal was to replicate previous studies examining associations between primary language modality and/or CI use on academic outcomes. Marschark et al. (2015) found that while neither using sign language nor a CI significantly predicted high school achievement, the ability to use spoken language was a significant predictor of achievement scores across all four achievement domains. Dammeyer and Marschark (2016) found that among those deaf adults with good to very good spoken language abilities, sign language ability was not a significant predictor of educational attainment when other variables were controlled. Among those with good to very good sign language abilities, spoken language was a predictor of educational attainment. In a study that analyzed census data for sign language users in Australia, Willoughby (2011) found that sign language users between 25 and 44 years of age had achieved higher levels of academic attainment than those between 45 and 64 years of age. Willoughby attributed the younger deaf individuals’ completing more schooling to external factors, such as pressure during the 1960s–1980s for deaf people to leave school to take up a trade, contrasting with the later emphasis on school retention in the (Australian) Disability Discrimination Act of 1992. Factors such as language skills and the possible contributions of CIs in spoken language were not considered in the Willoughby study, despite the fact that CI use in Australia has long outpaced the rest of the world.

A second goal of the present study was to examine the impact of relevant technologies on academic achievement. Marschark et al. (2015) failed to find an effect of either hearing aid or CI use on academic outcomes, but other educational technologies were not considered. Dammeyer, Lehane & Marschark (in press) conducted a study of academic support services that included various classroom technologies and interpreting among deaf 16- to 65-year-olds in Denmark. They found that neither use of hearing aids nor CIs was associated with academic attainment (i.e., whether or not a college degree had been obtained). However, they did find that individuals who had achieved college degrees were more likely to have used FM systems, mobile video interpreting, and texting devices. The investigators suggested that that association might be explained by college graduates having gained greater knowledge, experience, and better access to support services and technologies through their college educations. The reverse also may be true, of course: Greater use of technology may have improved access and learning, thus enabling greater academic attainment.

Studies examining the impact of technology use on academic outcomes apparently have not been conducted in the United States, although Stinson (in press) reviewed studies that have found communication technologies to increase deaf students’ participation and the frequency of content-related interactions between deaf and hearing peers in the classroom. Several studies also have examined the impact of real-time text in the classroom. Stinson, Elliot, Kelly, and Liu (2009), for example, compared the benefits to deaf high school and college students’ learning of real-time text as compared to sign language interpreting. High school students retained more information from classroom lectures supported by real-time text rather than by sign language interpreting, but a significant difference was not observed among the college students. Marschark et al. (2006) compared the benefits to learning by 12- to 16-year-olds of real-time text, sign language, and having both available simultaneously. No differences were found among the three types of information delivery either immediately after the lesson or on a delayed test one week later. A similar experiment involving college students, in contrast, found that real-time text alone led to significantly better test performance than either of the other two conditions. Although that finding might appear counterintuitive to those familiar with deaf education, follow-up studies revealed that deaf college students, as a group, consistently learn as much or more from text as they do from sign language, regardless of whether instructors are signing for themselves or using sign language interpreters (Borgna, Convertino, Marschark, Morrison, & Rizzolo, 2011; Marschark, Sapere, Convertino, Mayer, Wauters, & Sarchet, 2009).

In summary, studies examining the benefits of sign language and access technology in deaf education have yielded three general findings of interest here. First, although CIs and spoken language are associated with better academic performance (or at least reading) in younger grades, large-scale studies have found that implant use generally is not significantly related to either academic achievement or academic attainment among deaf individuals from high school onward. Second, studies involving middle school (11–14 years of age) through college-aged deaf students have found classroom learning to benefit as much or more from real-time text as sign language in the classroom, even when such text is as evanescent as signing. This finding may be related to results indicating that at least by high school age, spoken language but not sign language abilities are associated with reading achievement (Perfetti & Sandak, 2000). Third, although there is limited research on the benefits of classroom technologies other than CIs on long-term academic outcomes, a recent large-scale study found the use of such educational technologies in the past to be associated with higher academic attainment in terms of degree completion (Dammeyer et al., in press).

The present study sought to bridge these three areas of research by examining, in a large sample of college-bound deaf learners, possible associations among use of educational and assistive listening technologies, language abilities, and academic achievement. Importantly, the study provided opportunities to determine whether classroom support services (i.e., technologies, interpreting, notetaking) and technology use are associated with academic achievement as well as academic attainment (as previously found by Dammeyer et al., in press) and to replicate findings indicating that spoken language but not CIs or sign language are associated with academic achievement among deaf high school students (Marschark et al., 2015). Based on recent research findings, spoken language skills were expected to be a significant predictor of achievement among the high school students in the present study. Looking ahead, however, all of the high school students in the present sample were going to be attending a university with a specific focus on educating deaf students in a mainstream environment, a setting may be particularly attractive to better-qualified deaf students. Both signed and spoken language abilities thus might be predictors of academic achievement for that population (Marschark et al., 2015).

Method

Data

Anonymous data for this study were drawn from institutional records of 980 deaf high school students.1 Data originally were collected from online questionnaires completed by students applying to college during their last two years of high school. These students went on to enroll as undergraduates at a university in which approximately 9% of the student body has hearing losses sufficient to qualify them for educational support services. Students were from 46 U.S. states; 56% were males, and 37% were CI users. The variables examined from this questionnaire are listed in Table 1 with the number of complete profiles, means, standard deviations, and response ranges.

Table 1.

Means and standard deviations (SD) for variables of interest

N Mean SD Range
Academic variables
ACT Reading score 827 19.77 6.22 9–36
ACT Mathematics score 827 19.75 5.00 11–36
ACT English score 826 16.85 5.90 6–35
ACT Science score 827 20.77 5.10 7–36
SAT Reading score 292 473.60 121.78 200–800
SAT Mathematics score 292 509.86 114.99 210–800
ACT (ACT/SAT) composite 980 19.57 5.07 11–35
Communication variables
PTA (dB) 973 94.93 23.27 31–120
Overall sign language skill 980 3.65 1.37 1.0–5.0
Overall spoken language skill 978 3.32 1.16 0.5–5.0
Age of cochlear implantation 366 6.28 4.66 1.0–28.0
Total language score 978 6.97 1.24 2.5–10.0
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Note. ACT = American College Test; PTA = Pure Tone Average hearing loss averaged over both ears; SAT = Scholastic Assessment Test.

Academic information

In the United States, the American College Test (ACT) and SAT (formerly the Scholastic Assessment Test) are two written, standardized tests of academic qualification used by colleges/universities in admission decisions. ACT English, math, reading comprehension, and science reasoning subtest scores were available for 826 or 827 individuals (Table 1). Scores on the ACT subtests range from 1–36, and the total or composite ACT score is the average of the four subtest scores also ranging from 1–36. SAT reading and math subtest scores were available for only 292 individuals (both ACT and SAT scores were available for 138 individuals).2 The sum of SAT subtests was converted to an equivalent ACT composite score using concordance tables available from the College Board (2009). For individuals who took both the SAT and ACT, the higher of the two scores was used. This provided ACT composite scores for 980 individuals who comprised the dataset for subsequent analyses.

Communication information

As part of the college application process, students requesting support services associated with their hearing loss were required to submit audiograms. Four-frequency, pure tone average hearing thresholds (PTAs) averaged across both ears were available for 98% of individuals in the sample (Table 1). Students also completed the Language and Communication Background Questionnaire (LCBQ) online, which gathers information about communication skills, history, and preferences, and is used to determine service provision. Metz, Caccamise, and Gustafson (1997) found strong positive correlations between self-rated sign language and self-rated speech intelligibility on the LCBQ and formal, independent assessments of sign language proficiency and speech intelligibility for young adults with hearing loss.

On the LCBQ, individuals rated their current communication skills on 5-point Likert scales (Table 1). Sign language skills (“Please rate your sign language skills”) were rated as excellent, good, fair, I understand a little, or I don't know sign language. Expressive spoken communication skills (“How well do you think most hearing people understand your speech?”) were rated as they understand everything I say, almost everything I say, about half of what I say, only a few words that I say, nothing, and I don't use speech. Receptive spoken communication skills (“How well do you understand speech when you both speechread and/or use your hearing?”) were rated as everything people say, almost everything I say, about half, only a few words, and nothing. LCBQ data describing expressive and receptive skills in spoken language were averaged to yield a single overall spoken language score comparable to the overall sign language score. Individuals who indicated that they used sign language were asked the age they began learning sign language, given a choice of 0–5 years old (n = 542, 55.3%), 6–15 years old (n = 196, 20.0%), 16 years or older (n = 106, 10.8%), or I don't know sign language (n = 135, 13.8%). Data were missing for one individual (0.1%).

Devices, aids, and service use

Individuals were asked whether they had received a CI (yes/no) and the age of their (first or only) CI surgery. The mean age of implant surgery was 6.28 years, and the mode 3.00 years (Quartiles 25 = 3.00, 50 = 5.00, 75 = 9.00). This is relatively late by current standards but accurately reflects the current cohort of college-aged students. Information on any subsequent implants was not available. The majority of students reported not using a hearing aid (n = 436, 44.5%), with less reporting that they used a hearing aid all of the time (n = 344, 35.1%), most of the time (n = 82, 8.4%), about half of the time (n = 47, 4.8%), and not often (n = 71, 7.2%). Individuals who reported having a CI generally had higher PTAs averaged across both ears (mean = 111 dB, SD = 10.6, range 53–120 dB), that is, greater levels of hearing loss, than those who did not report having a CI (mean = 86.3 dB, SD = 21.9; range 31–120 dB). Because not all students with greater hearing losses used CIs or hearing aids, those devices and PTAs were considered separately in analyses described below.

As part of the college application process, individuals in the sample indicated in binary choices (yes/no) what kinds of classroom access services were provided to them in their most recent education placement: real-time text, FM systems, sign language interpreting, and/or notetaking (categories similar to Dammeyer et al., in press) (Table 2).

Table 2.

Aid and service use

N Yes No
Did you receive these services in your last school:
 Sign language interpreting 976 588 (60.2%) 388 (39.8%)
 Real-time captioning 976 162 (16.5%) 814 (83.1%)
 FM system 976 291 (29.7%) 685 (69.9%)
 Notetaking 976 411 (41.9%) 565 (57.7%)
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Results

Examination of the extent to which the communication variables of interest predicted academic achievement first was analyzed in a stepwise multiple regression in which ACT composite scores were the criterion variable; predictor variables were whether or not individuals had received a CI, whether they used hearing aids, PTA, self-reported spoken language and sign language skills, and the use of classroom support services (real-time text, sign language interpreting, FM systems, notetaking). All and only those variables accounting for significant portions of the variance are described here. As can be seen in Table 3, better spoken language skill was the best predictor of ACT composite scores, accounting for approximately 10% of the variance, with smaller amounts of additional variance accounted for by the age at which individuals learned sign language (3%; later acquisition associated with higher achievement), use of sign language interpreting in high school (<1%), whether they had received a CI (<1%), and not using FM systems in high school (<1%). Similar analyses were conducted using the same predictors and ACT reading scores (Table 3) and ACT English scores (Table 4), in turn, as criterion variables. When ACT reading scores served as the criterion variable, a regression yielded the same results as the previous analysis, with spoken language skill as the primary predictor of the scores (10%) and small amounts of additional variance accounted for by (later) age of sign language acquisition (2%), the use of sign language interpreting (<1%), and nonuse of FM services (<1%) in high school. With ACT English scores as the criterion variable, spoken language skill was the best predictor, accounting for approximately 11% of the variance, followed by self-rated sign language skills (2%), CI use (1%), and nonuse of FM systems in high school (<1%). Importantly, spoken language skills were a positive predictor of English scores, while sign language skills were a negative predictor.

Table 3.

Regression (final) model results, R2 change and beta weights, predicting college entrance test (ACT) scores

N R2 β F change significance
Composite ACT 956
Spoken language skill .10 .23 p = .000
Age learned to sign .03 .13 p = .000
Interpreting in school .01 .08 p = .004
CI <.01 .08 p = .013
FM system in school <.01 −.07 p = .043
Reading 810
Spoken language skill .10 .25 p = .000
Age learned to sign .02 .14 p = .000
Interpreting in school <.01 .08 p = .016
FM system in school <.01 −.09 p = .017
English 809
Spoken language skill .11 .23 p = .000
Sign language skill .02 −.16 p = .000
CI .01 .10 p = .005
FM system in school <.01 −.08 p = .021
Math 810
Sign language skill .06 −.09 p = .000
Spoken language skill .01 .15 p = .000
Age learned to sign <.01 .10 p = .036
Science 810
Sign language skill .06 −.16 p = .000
Spoken language skill .02 .15 p = .000
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Students’ mathematics and science ACT scores were examined as above. With ACT mathematics scores as the criterion variable, overall sign language skill (6% of the variance) was the primary predictor, followed by overall spoken language skill (1%) and the age at which individuals learned sign language (<1%; see Table 3). Again, whereas spoken language skill was a positive predictor of ACT mathematics scores, sign language skill was a negative predictor, and it was later rather than earlier acquisition of sign language that predicted a small amount of variance. When ACT science scores served as the criterion variable, overall sign language skill (6% of the variance) was the primary predictor, followed by overall spoken language skill (2%; see Table 3). Spoken language skill again was a positive predictor of ACT mathematics scores, and sign language skill was a negative predictor.

While the positive prediction of achievement scores by deaf high school students’ spoken language skills is consistent with previous results, the negative prediction of English, mathematics, and science scores by their sign language skills was a surprising new finding. Those results left open, however, the possibility that deaf students’ academic achievement might be related more to their general language skills (i.e., some combination of sign language and spoken language skills) rather than their skills in any single modality (Convertino et al., 2009; Rinaldi, Caselli, Onofrio, & Volterra, 2014). A total language score therefore was computed for each individual by summing their spoken language and sign language scores. The median total communication score (10 maximum) was 7.0. Using a median split, independent sample t-tests were used to compare scores on the individual ACT subtest and ACT composite scores. None of the t-tests yielded significant results, 0.32 < t < 1.78, and that variable will not be considered further.

Discussion

The present study examined associations between academic achievement, language abilities, listening technologies, and support services in a large cohort of college-bound high school students in the United States. Consistent with previous studies involving high school students, academic achievement was positively related to students’ spoken language skills but, if anything, negatively related to their sign language skills (DeLana et al., 2007; Marschark et al., 2015; Sarchet et al., 2014). Whether or not individuals used CIs or hearing aids contributed little to predicting achievement at the high school level, a finding consistent with U.S. national data from NLTS2 (Marschark et al., 2015). Reported use of support services in high school (but not the extent of such use) also accounted for little variance in achievement scores, with only sign language interpreting and FM contributing any at all (and the latter, negatively). The finding that technology use in academic settings was not associated with high school students’ achievement as measured by college entrance tests contrasts with the finding of (Dammeyer et al., in press) who found previous use of such technologies (at no specific time) related to level of degree attainment among adults. This difference might be explained by Dammeyer and Marschark's (2016) suggestion that different factors might contribute to academic achievement (e.g., parent education level, literacy and mathematic skills) and academic attainment (e.g., effective communication strategies and self-advocacy in obtaining academic support services). Alternatively, Dammeyer et al.'s (in press) results may reflect use of such technologies at university rather than during high school or a difference between their availability and use in Denmark as compared to the United States.

Hearing thresholds might be expected to be related to academic achievement (Karchmer, Milone, & Wolk, 1979), even though they usually are confounded with communication modality (Allen & Anderson, 2010; Wagner, Marder, Blackorby, & Cardoso, 2002). PTAs did not account for significant amounts of variance in achievement in the present study. This finding is in line with previous large-scale studies that found PTAs either unrelated or only weakly related to academic measures from preschool (Dammeyer, 2014; Tymms, Brien, Merrell, Collins, & Jones, 2003) through high school and college age (Convertino et al., 2009; Marschark et al., 2015; Powers, 2003). Among high school students, Marschark et al. (2015) reported mild hearing loss was a significant negative predictor of mathematics achievement, but moderate hearing loss was not a significant predictor of achievement in any of the domains tested.

A related but also possibly counterintuitive finding from this study was the lack of a stronger relation between CI use and achievement, as that variable accounted for only about 1% of the variance in ACT composite scores and English scores, and, most notably, no significant variance in reading scores. As indicated earlier, however, that finding is consistent with previous research involving high school and college students, even if implant use is associated with better reading achievement among young children (Geers et al., 2008; Harris & Terlektsi, 2011; Marschark et al., 2015; Thoutenhoofd, 2006). That finding also is complemented by the finding that students’ sign language skills were not positively related to achievement. That result is consistent with previous, large-scale studies involving both high school students (Marschark et al., 2015) and college students (Convertino et al., 2009). Those two studies failed to find any significant relation between academic outcomes (achievement test scores and either college entrance scores or classroom learning, respectively) and sign language skills or having deaf parents, even though they have been found associated with reading abilities among younger deaf children (Dammeyer, 2014; DeLana et al., 2007; Miller et al., 2012, 2015; Sarchet et al., 2014). The sign language skills obtained through bilingual education programming also have been found largely restricted to the elementary school years, even when large proportions of deaf students in those programs have deaf parents (Dammeyer & Marschark, 2016; Lange et al., 2013; Nover et al., 2002).

As described earlier, all three of these findings point to differences in the language, cognition, materials, and goals involved in academic functioning during versus beyond the elementary school years. They also emphasize that age-appropriate language skills among young deaf children should not be taken as indicators that they no longer need support services as they move into later grades. Rather, we need to recognize that deaf learners will continue to face less than optimal access to communication and language throughout the school years, and perhaps beyond. At the point at which hearing children shift from learning to read to reading to learn (around fourth grade), for example, deaf children might continue to need reading instruction (Wauters, Van Bon, Tellings, & Van Leeuwe, 2006). More generally, the fact that spoken language continues to be a predictor of achievement in high school and beyond while sign language and CI use do not suggest the need for further research into the language abilities of older deaf learners and the ways in which they interact with instructional methods and materials.

The relationship between sign language variables and academic achievement is worthy of note, if only to emphasize the need for further research to clarify underlying relationships and implications. Later, not earlier sign language acquisition was associated with higher achievement scores, and there were negative relations between sign language skill and science and math achievement scores among these college-bound high school students. Marschark et al. (2015) also found use of sign language to be a negative predictor of science and mathematics achievement scores when other factors were controlled (i.e., in multiple regression analyses). It has been suggested that sign language might be more beneficial than spoken language for explaining concepts in science and mathematics (Bauman & Murray, 2010). The extent to which that might be true in any specific content areas remains to be demonstrated, but such descriptions (e.g., the length and width of a garden or Bauman & Murray's description of cell mitosis in a biology class) generally involve gesture rather than sign language per se.

Limitations and Future Research

The use of technology and school support services were not found to impact academic achievement to the degree expected in the present study. This lack of association may be related to nuances that require further investigation with more sensitive measures, for example, the amount of time spent using technology (i.e., was real-time captioning available for all classes?) or at which ages/grades different technologies and support services were used. Information relevant to the use of technology and support options at this level of detail was not available in the current study, and it difficult to see how it could be obtained in any reliable fashion across diverse school settings. In addition to examining such issues over the course of individuals’ academic careers, studies involving various subpopulations of deaf learners also would be useful. The present study involved only college-bound deaf students, who are presumably the highest academically-achieving students of all deaf students, on average, and used their college entrance scores as indicators of achievement. Similar research might compare students who are and are not college-bound, using either secondary school cumulative grade point averages or course rigor as indicators of achievement and workplace readiness.

Although the present findings are consistent with those from the more comprehensive, NLTS2 study (Marschark et al., 2015), future studies also would benefit from the comparison of the impact of the variables examined in this study (e.g., CI use, age of sign language acquisition, language skills) and the inclusion of more student (and family) demographic information than was available for this study. Understanding the effects of such factors at different points in time (and for different subpopulations of deaf learners) as well as interactions among them is an important goal for further research aimed at a better understanding of ways to support academic outcomes of deaf learners.

Conclusion

This study investigated the relationships between college-bound deaf students’ academic achievement, communication characteristics, and use of classroom technology/support. Better self-reported spoken language skills was positively associated to achievement in some domains while better self-reported sign language skills were related to poorer achievement in others. Consistent with other recent findings, use of CIs accounted for very little variance (≤1%) in achievement scores at the high school level. Several other listening technologies and classroom access services surveyed did not contribute significantly at all to the prediction of achievement scores. Further research is needed to explore what kinds of support work for diverse deaf learners in various educational settings.

Acknowledgments

The authors wish to thank Richard Dirmyer and Denise Wellin for their assistance.

Footnotes

1

Approximately 5% of the individuals included in the dataset appear to have taken one or more “gap years” between high school and college. Those individuals were left in the sample, but because it was unclear whether their admission tests were taken during or after high school, age was included in statistical analyses, as appropriate.

2

Missing data were not replaced, hence degrees of freedom in analyses presented later will vary.

Funding

This research was supported in part by grant R01DC012317 from the National Institute on Deafness and Other Communication Disorders and a Fulbright scholarship from the Australian-American Fulbright Commission. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NIDCD or NTID.

Conflict of Interest

No conflicts of interest were reported.

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