Are Deaf Students' Reading Challenges Really About

Are Deaf Students’ Reading Challenges Really About Reading? Marc Marschark Patricia Sapere Carol M. Convertino Connie Mayer More

American Annals of the Deaf, Volume 154, Number 4, Fall 2009, pp. 357-370 (Article) Published by Gallaudet University Press DOI: 10.1353/aad.0.0111

For additional information about this article http://muse.jhu.edu/journals/aad/summary/v154/154.4.marschark.html

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ARE DEAF STUDENTS’ READING CHALLENGES REALLY ABOUT READING?

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MARC MARSCHARK, PATRICIA SAPERE, CAROL M. CONVERTINO, CONNIE MAYER, LOES WAUTERS, AND THOMASTINE SARCHET MARSCHARK IS PROFESSOR AND DIRECTOR, CENTER FOR EDUCATION RESEARCH PARTNERSHIPS, NATIONAL TECHNICAL INSTITUTE FOR THE DEAF (NTID), ROCHESTER INSTITUTE OF TECHNOLOGY, ROCHESTER, NY. HE IS ALSO A PROFESSOR, MORAY HOUSE SCHOOL OF EDUCATION, UNIVERSITY OF EDINBURGH, AND SCHOOL OF PSYCHOLOGY, UNIVERSITY OF ABERDEEN, SCOTLAND. SAPERE AND CONVERTINO ARE RESEARCH ASSOCIATES AT NTID. MAYER IS AN ASSOCIATE PROFESSOR, FACULTY OF EDUCATION, YORK UNIVERSITY, TORONTO, CANADA. WAUTERS IS COORDINATOR OF THE MASTER’S PROGRAM, INSTITUTE FOR SIGNS, LANGUAGE, AND DEAF STUDIES, UNIVERSITY OF APPLIED SCIENCES, UTRECHT, NETHERLANDS, AND A RESEARCHER AT KENTALIS–PONTEM R&D, SINT MICHIELSGESTEL, NETHERLANDS. SARCHET IS A RESEARCH ASSOCIATE AT NTID.

E A D I N G A C H I E V E M E N T among deaf students typically lags significantly behind hearing peers, a situation that has changed little despite decades of research. This lack of progress and recent findings indicating that deaf students face many of the same challenges in comprehending sign language as they do in comprehending text suggest that difficulties frequently observed in their learning from text may involve more than just reading. Two experiments examined college students’ learning of material from science texts. Passages were presented to deaf (signing) students in print or American Sign Language and to hearing students in print or auditorially. Several measures of learning indicated that the deaf students learned as much or more from print as they did from sign language, but less than hearing students in both cases. These and other results suggest that challenges to deaf students’ reading comprehension may be more complex than is generally assumed.

If there is one overriding concern among educators of deaf students, it is the challenge of reading comprehension (Chamberlain & Mayberry, 2000; Paul 1998; Schirmer, 2001; Schirmer & McGough, 2005). Despite hundreds of studies exploring various subskills of reading and many more theoretical claims over the past 50 years (Luckner & Handley, 2008; Luckner, Sebald, Cooney, Young, & Muir, 2005/2006), there appears to have been relatively little progress toward improving achievement in this domain. Since the beginning of the mainstream diaspora in 1975, for example—a date that also roughly corresponds to a shift toward greater emphasis on the use of natural sign languages in deaf education—the median reading achievement of deaf 18-year-old students in the United States has increased only from that typical of a hearing 8-yearold (grade level 2.7; Allen, 1986) to that

typical of a 9-year-old (grade level 4.0; Traxler, 2000). Unless educators and researchers are prepared to admit that most deaf students will never have literacy skills comparable to those of their hearing age-mates, something has to change. In the present article, we suggest a different approach to understanding and improving reading by deaf students, one that involves broadening rather than narrowing the scope of investigation. Given the considerable resources devoted to them, why has there not been greater improvement in deaf students’ reading abilities? One reason for the lack of progress may be a lack of coherence in relevant research. In a comprehensive review, Luckner and colleagues (2005/2006) identified 964 articles related to literacy and deaf individuals over a 40-year period, a finding that attests, at least, to the importance of the issue. Yet Luckner and colleagues

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DEAF STUDENTS’ READING CHALLENGES found only 22 of those studies sufficiently rigorous, complete, and relevant to be included in a meta-analysis of research results, and reported that “no two studies examined the same dimension of literacy” (p. 443). In short, they found that educators and researchers do not know as much about deaf students’ literacy as they think they do. Some investigators attribute deaf students’ reading challenges to the variability and relative impoverishment in early language experienced by many, if not most, deaf children. Bilingual education is sometimes offered as a solution to this situation (Center for ASL/English Bilingual Education and Research, 2002), but there is little empirical evidence to support its use (Mayer & Akamatsu, 1999, 2003; Rydberg, Gellerstedt, & Danermark, 2009). While having early access to fluent language—for example, through deaf parents—is associated with better reading achievement (Chamberlain & Mayberry, 2000; Padden & Ramsey, 2000), even deaf children of deaf parents do not reach the levels of accomplishment typical of these children’s hearing peers (see Marschark & Wauters, 2008, for a review). Language-rich early environments appear to be necessary for age-appropriate literacy skills, but they do not appear to be sufficient. The issue also is not resolved by attributing reading challenges to the use of sign language rather than spoken language, which more readily maps onto print. Rather, several studies have indicated that deaf children whose early environments include access to both sign language and the print/spoken vernacular develop better literacy skills than those exposed only to one mode or the other (Akamatsu, Musselman, & Zweibel, 2000; Brasel & Quigley, 1977; Padden & Ramsey, 2000; Strong & Prinz, 1997, 2000). Although deaf children with

cochlear implants frequently are found to read better than peers without implants, their mean levels of performance still rarely match those of hearing age-mates (Geers, 2005; Spencer, Tomblin, & Gantz, 1997). Several studies (e.g., Geers, 2002, 2004) have found younger deaf children with implants to read at or near grade level, on average, but with high, unexplained levels of variability. Emerging longitudinal data further suggest that even while such children continue to show language gains, their reading abilities may fall behind those of hearing peers in later grades (Geers, Tobey, Moog, & Brenner, 2008), when schooling demands that they read to learn and there is reduced emphasis on learning to read. A possible exception appears to be those students with implants who have the opportunity to use both spoken and sign language in school, a group that has been found to read at the same level as hearing peers, at least through high school (Spencer, Gantz, & Knutson, 2004). The locus of this finding is still unclear, however, and other investigators have suggested that cognitive development rather than language development, per se, might be a central factor (Marschark, Sarchet, Rhoten, & Zupan, in press; Pisoni, Conway, Kronenberger, Henning, & Anaya, in press). There appears to be a naive assumption that underlies beliefs about reading and children with cochlear implants: that there is a direct relationship between hearing threshold and reading ability. Literacy does seem to be sensitive to hearing loss, but the relationship appears to be one in which even relatively small increases in hearing thresholds can disrupt reading ability, rather than one in which there is a direct link between the two. Reviews by Goldberg and Richburg (2004) of the literature on children with minimal hearing im-

pairment (those with hearing thresholds between 16 and 25 dB) and by Moeller, Tomblin, Yoshinaga-Itano, Connor, and Jerger (2007) on the literature pertaining to children with mild to moderate hearing losses both indicated that even relatively mild hearing losses create significant challenges with regard to reading. Tymms, Brien, Merrell, Collins, and Jones (2003), however, did not find a correspondence between hearing thresholds and composite reading scores among 5- and 6-year-olds. Among older deaf and hard-of-hearing students, Allen (1986) found that degree of hearing loss had little effect on academic achievement, as measured by the Stanford Achievement Test, a finding replicated by Powers (2003) in his reanalysis of a large data set from deaf high school students taking examinations for entry into postsecondary programs in England. Convertino, Marschark, Sapere, Sarchet, and Zupan (2009) similarly failed to find a relationship between hearing thresholds and reading performance in a sample of 568 deaf and hard-of-hearing college students, using students’ scores on either the California Reading Comprehension Test or the Michigan Test of English-Language Proficiency. On the basis of such evidence and convergent findings from other studies, Marschark and Wauters (2008) suggested that educators and researchers need to look beyond the obvious if progress is to be made in improving the reading achievement of deaf and hard-of-hearing students. In particular, Marschark and Wauters argued that one reason for the lack of progress in this area might be that deaf students’ reading challenges are not really specific to reading. The researchers observed that weaknesses exhibited by deaf students in many of the subskills involved in reading are

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likely that discussions of English1 phonology or grammar, or any other text variables, will be sufficient to overcome reading challenges. Rather, a focus on reader variables such as lexical knowledge, metacognition, and information-processing strategies and habits in the context of language at large would be in order. To date, studies of higher-level processes such as metacognition and strategy use among deaf students have been examined only in the context of print, while their application with regard to the comprehension of sign and speech has gone largely unexplored (but see Jeanes, Nienhuys, & Rickards, 2000; Lloyd, Lieven, & Arnold, 2005; and Marschark et al., 2007, for studies of peer-to-peer speech and sign comprehension). R. R. Kelly, Albertini, and Shannon (2001), for example, suggested that, like good and poor hearing readers, deaf readers vary in their application of “metacognitive monitoring” (Strassman, 1997; Thiede, Anderson, & Therriault, 2003) during reading. R. R. Kelly and colleagues conducted two experiments examining the extent to which deaf college students were able to identify the main ideas of academic texts, and these students’ awareness of whether or not passages made sense. Results indicated that both “higher-level” and “lower-level” readers (the researchers’ terms for students reading at the eighth- and ninth- grade levels) performed extremely poorly in both respects. Only about 50% of students in each group were able to identify the main idea of presented passages, and less than 25% correctly responded to questions asking whether they had gleaned key content information from their reading. Ninety percent of the students failed to notice “incongruent” sentences embedded in the texts, a level of performance not significantly improved by training. It is un-

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paralleled by similar weaknesses in understanding sign language, and suggested that both therefore might better be accounted for in terms of more general language-comprehension and cognitive factors. In their view, understanding and improving reading comprehension skills among deaf students will require going beyond the most commonly studied aspects of deaf students’ reading—phonology, vocabulary, morphology, and grammar—and considering differences in higher-level language and cognitive processes. The importance of higher-level processes to reading has long been recognized by educators and investigators working with both hearing and deaf students. With regard to deaf readers, for example, Paul (2003) and Schirmer (2000) discussed the fact that deaf children’s experiences in linguistic and nonlinguistic domains influence their knowledge of the world—both content and procedures—and thus influence learning to read and learning through reading. This notion is captured in Paul’s view of reading as involving interactions among text factors, reader factors, and task/context factors, and in the more general notion of reading as involving both top-down and bottom-up processing (i.e., between what is known and what is on the printed page). As the foregoing suggests, however, in the present article we address the possibility that rather than continue to search for explanations of deaf students’ difficulties with text, it might be more fruitful to examine relations of cognitive processing, language comprehension, and learning, regardless of whether the latter two involve printed words or through-theair communication. In this view, if deaf students have full perceptual access to both print and sign language and demonstrate similar difficulties in learning through those media, it is un-

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clear whether deaf students might need greater and more explicit instruction than hearing peers in recognizing main ideas and thematic coherence in texts, but there is no doubt that these are essential reading comprehension skills that would be presumed to be in the repertoires of university students. R. R. Kelly and colleagues concluded that “the primary question is how to teach metacognitive reading strategies to students and provide sufficient practice so that they independently sustain the use of these strategies to improve and refine their reading comprehension skills” (p. 396). One difficulty in interpreting the results of the study by R. R. Kelly and colleagues (2001) is the lack of a hearing comparison group, although their use of passages written at the 8th-to10th-grade levels might make the inclusion of such a group appear superfluous. Their investigation also did not include any independent measure of comprehension beyond students’ correct identification of those points in the texts regarded by the investigators as most important. Although this would appear to be an appropriate methodology for a reading comprehension study, differences between deaf and hearing college students in their concept knowledge (Marschark, Convertino, McEvoy, & Masteller, 2004), content knowledge (Marschark, Sapere, Convertino, & Seewagen, 2005), executive function (Hauser, Lukomski, & Hillman, 2008), and learning strategies (Strassman, 1997) leave open the possibility that students in the study by R. R. Kelly and colleagues may have approached the content of the passages somewhat differently than the investigators. The following two experiments were designed to further examine these alternatives. The study by R. R. Kelly and colleagues (2001) was replicated and

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DEAF STUDENTS’ READING CHALLENGES extended by using signed as well as printed versions of two of their passages, and by including hearing comparison groups in addition to groups of signing deaf students. Experiment 1 To the extent that performance in the study by R. R. Kelly and colleagues (2001) was a consequence of text factors, such as the vocabulary or grammar of the science-related texts (Paul, 2003), or the application of readingspecific metacognitive strategies, deaf students’ reading comprehension should be poor relative to that of hearing students (i.e., “higher-level” readers) and to their own performance when passages are presented via sign language. Alternatively, if deaf students’ performance in the original study was a function of more general language comprehension or cognitive/metacognitive skills rather than reading-specific skills, similar performance might be observed when passages were signed and read (Marschark et al., 2006).

Method Participants Experiment 1 involved 20 deaf students and 20 hearing students enrolled at the Rochester Institute of Technology. RIT includes the National Technical Institute for the Deaf (NTID) as one of its eight colleges, but deaf participants came from all RIT colleges. Students were recruited via flyers and personal contacts and were paid for their participation. The deaf students varied in their reported communication skills and preferences (see below, under “Results and Discussion”), with all but two reporting moderate to excellent skills in American Sign Language. The sample rated their ASL skills at 4.3 on a 5-point scale ranging from “no skill” to “excellent.” Fourteen of the deaf students re-

ported that they used both speech and sign, although not necessarily together, and the sample rated their Simultaneous Communication (SimCom) skills at 4.4 on the 5-point scale ranging from “no skill” to “excellent.” Convertino and colleagues (2009) found SimCom to be a significant predictor of classroom learning, even though instruction did not involve use of this method. Overall, this group of students had no strong preference for speech or sign (scoring 2.55 on a 5-point scale from speech to sign), but rated themselves extremely flexible in their communication skills. These and the other demographic variables were considered in the analyses described below.

The interpretations also were videotaped. Using the written passages, multiple-choice questions were created for use as posttest questions, eight questions for each passage. Each question had four alternative answers. The questions were designed to tap comprehension of the most important information in the passages. After the tests were prepared, they were vetted against the signed versions of the passages to ensure that the information necessary to answer all the questions was available. Four of the questions from each test were selected for use as a content-specific pretest of prior knowledge to be administered before each passage.

Materials

Procedure

The printed materials, drawn from the study by R. R. Kelly and colleagues (2001), consisted of two sciencerelated passages. The Sea Around Us included 261 words over two paragraphs; Missouri’s Water Snakes . . . A Closer Look included 364 words over three paragraphs. The first was written at a 9th-to-10th-grade reading level and the second at an 8th-to-9thgrade level, both according to the Dale-Chall readability formula (Chall & Dale, 1995). Spoken versions of the passages were videotaped while each was being read in a normal speaking voice by an NTID instructor and former English teacher (one of the original authors of the study by R. R. Kelly and colleagues). Digital videotapes of the spoken versions were given to a nationally certified sign language interpreter with more than 25 years’ experience at RIT. One of the top 3 interpreters at NTID (out of more than 125), she has near-native ASL skills according to students and peers. She produced signed versions of the two passages appropriate for the intended audience, primarily using ASL.

Participants were tested in small groups; deaf and hearing students were tested separately. Instructions were presented in both sign language and spoken language to the deaf students in order to ensure that all understood the task. Each student received two passages, one in print and one via videotape. Hearing students read one passage and saw a videotape of the instructor reading the other passage. Deaf students read one passage and saw a pair of synchronized videotapes, one with the instructor reading the passage (with audio) and the other showing the sign language interpreter. The synchronized videotapes were displayed using side-byside LCD projectors, so that the instructor and interpreter appeared life-size. Prior to each passage, students completed the pretest (four questions). Immediately following each passage, they were first asked to write down, in one or two sentences, “the main idea” of the passage, then completed the eight-question posttest. Passages were balanced over groups. Between the first and second pas-

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• preferred mode of communication, from speech to sign • skill in producing ASL • skill in comprehending ASL • skill in producing English-based sign • skill in comprehending Englishbased sign • skill in comprehending SimCom (sign plus speech) • preferred mode of sign language communication, from ASL to English-based Scoring of the main idea of each passage utilized the system devised by R. R. Kelly and colleagues (2001). According to that system, the main idea of each passage was deemed to be composed of three parts, each assigned one point if included in a response. Responses were scored independently

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sages, hearing students performed an irrelevant interpolated task. Deaf students completed a short questionnaire concerning their communication skills and preferences, reporting their use of assistive listening devices, the hearing status of their parents,2 the age at which they learned to sign, and their responses to questions about their communication skills drawn from the NTID Language and Communication Background Questionnaire (LCBQ, found in Marschark, Sapere, Convertino, Seewagen, & Maltzen, 2004). NTID employs the LCBQ rather than face-to-face communication interviews because it is more efficient and has been found to have a correlation of approximately .80 with interview assessments (Hatfield, Caccamise, & Siple, 1978; McKee, Stinson, & Blake, 1984). The questionnaire was not intended as a definitive, precise assessment of student language skills, but provided estimates sufficient for the purposes of the present study. On a 5-point Likert-type scale, students rated their

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Table 1

Mean Proportions (and Standard Deviations) for Deaf and Hearing Students Reading Science Passages and Seeing Them Signed or Spoken, Respectively, in Experiment 1 Deaf

Hearing

Read Pretest Snakes passage Sea passage Main ideas (points out of 3) Snakes passage Sea passage

Signed

.45 (.24) .56 (.27)

Read

Spoken

.59 (.26) .70 (.22)

1.30 (.82) 1.50 (.85)

1.20 (.92) 1.10 (.99)

1.00 (.82) 1.50 (1.08)

.90 (.74) 1.10 (.88)

.86 (.09) .82 (.13)

.89 (.09) .69 (.15)

.94 (.09) .90 (.14)

.99 (.04) .80 (.16)

Posttest Snakes passage Sea passage

by two investigators who then met to resolve their few discrepancies and reached 100% consensus on a score for each student’s response for each passage.3

Results and Discussion In Experiment 1 and in the following experiment, the alpha level was set at .05. All results significant at that level are reported. Post hoc comparisons all utilized (SPSS-PC) Bonferroni tests, a method based on the Student’s t statistic but which adjusts significance levels for multiple comparisons. Several recent studies have demonstrated that deaf college students’ classroom test performance partly reflects their coming into the classroom with less content knowledge relative to hearing peers (e.g., Marschark, Sapere, et al., 2005). Initial analyses therefore examined pretest performance. Because of the incomplete blocks design (different students received different passages signed or spoken or conveyed via print), the pretest scores (proportions) for the two passages were analyzed separately using one-way analyses of variance. Neither analysis revealed significant differences between deaf and hearing students’ prior knowl-

edge (see Table 1), both Fs(1, 38) < 3.15. It therefore was unnecessary to conduct analyses of covariance controlling for prior knowledge. Analyses of the 4-point (0–3) mainidea scores from R. R. Kelly and colleagues (2001) were performed using separate 2 (hearing status) by 2 (modality: read or signed/spoken) ANOVAs for the two passages. Neither of the analyses yielded any significant main effects or interactions. The scores of the deaf students shown in Table 1 are consistent with those of R. R. Kelly and colleagues, averaging less than 50% for both passages. However, the findings that the deaf students did no better with signed passages than with read passages and that hearing students did not outperform deaf students following reading suggests that deaf students’ reading difficulties may not have been the sole culprit in producing the results found by R. R. Kelly and colleagues. Alternatively, (a) the passages may have been so difficult that neither hearing nor deaf students were able to understand them very well (which resulted in a floor effect), (b) the scoring system may have been insufficiently sensitive to discern group differences in comprehension, or (c) the original investigators’ notions

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DEAF STUDENTS’ READING CHALLENGES of the “main ideas” of the passages may not have coincided with those of college students. These alternatives can be clarified somewhat through analysis of the posttest questions for each passage. Posttest scores (proportions) were analyzed in the same manner as the main-idea scores, using separate 2 (hearing status) by 2 (modality: read or signed/spoken) ANOVAs. On the passage from The Sea Around Us, there was a significant main effect of hearing status, F(1, 36) = 4.14, MSE = 212.24, as hearing students scored higher than deaf students, and a significant effect of presentation modality, F(1, 36) = 6.64, but no interaction. These results reflect the fact that both groups scored higher when they read the passage than when they saw it spoken or signed (see Table 1). On the Snakes passage, there was only a main effect of hearing status, F(1, 36) = 5.65, MSE = 135.42, as hearing students scored higher than deaf students, regardless of the modality of presentation (see Table 1). The lack of significant interactions indicates that although the two passages might not have been exactly comparable (a danger with using real academic texts), neither deaf nor hearing students had any relative advantage as a result. Correlational analyses examined relations between information obtained from deaf students on the communication questionnaire and their performance scores, both for the read and signed passages overall and for the two separate passages. Overall, higher main-idea scores on read passages were associated with significantly higher self-ratings of SimCom production skill, r(18) = .55, and English-based signing production skill, r(18) = .51. These findings are consistent with those of Convertino and colleagues (2009) from a meta-analysis of 10 classroom learning studies con-

ducted with participant samples from the same population. Analysis of the Sea Around Us passage, the one for which deaf students showed better comprehension of the text than of the signed version, yielded only a significant correlation between the number of main ideas recalled and students’ reported SimCom skill, r(9) = .67. (The correlation with reported ASL skill was .24, and that with reported spoken-language skill was .12.) A similar analysis with the passage from Missouri’s Water Snakes yielded only a significant inverse correlation between the posttest scores and reported ASL skill, r(10) = -.77. Finally, posttest scores for read and signed/spoken passages were significantly correlated, r(18) = .41, but their main-idea scores were not, r(18) = .27. Taken together, the above analyses suggest that deaf students’ comprehension of signed passages, as indexed by the main-idea and posttest scores, were relatively independent of their judgments of their sign language skills. Comprehension of read passages was moderately related to self-ratings of signing skills that incorporate English, but that ability appears more related to getting the overall gist of a passage than to extracting important ideas from the texts. The relatively high posttest scores, particularly relative to the main-idea scores, suggest that these findings reflect a true dissociation between students’ assessments of their language skills and their ability to acquire key information through reading or sign language (Marschark, Sapere, et al., 2004). This dissociation could be due to the fact that the passages were written at a high school level (but see Marschark et al., 2006, on the use of middle school, high school, and college-level materials), or an indication of an “unskilled and unaware effect” (Kruger & Dunning, 1999), in this case indicating that relatively poor lan-

guage comprehension skills may leave deaf students with the “double burden” of lesser comprehension and less awareness of it. R. R. Kelly and colleagues (2001) claimed that the inability to identify the main ideas of the passages used in their study (and the present study) reflected deaf students’ tendency not to use metacognitive monitoring during reading. While this tendency might be relatively common among deaf readers (Doran & Anderson, 2003; Strassman, 1997), R. R. Kelly and colleagues examined only a relatively narrow aspect of metacognitive monitoring during reading, and the results of Experiment 1 in the present study suggest that it is unlikely to be a complete explanation of their results. Not only did the deaf college students in Experiment 1 perform no better when the material was presented in sign language rather than print, but their main-idea scores did not differ from those of hearing college students, who would not be expected to demonstrate failures of metacomprehension during the reading of 8th-to-10thgrade science texts. With regard to the posttest questions, which tapped important information contained in the passages, the advantage observed for hearing students over deaf students with spoken/signed materials as well as read materials is consistent with the findings from several studies. Marschark and colleagues (2006, Experiments 1 and 2), for example, found that deaf college students performed no better when they saw lectures signed than when they received the same instruction via real-time text. Their Experiment 1 also included hearing students, whose performance surpassed that of the deaf students both when lectures were spoken/signed and when they were read. Taken together with the present results, it seems most parsi-

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Experiment 2 Perhaps the primary reason for the emphasis on deaf students’ print literacy skills is that in most educational settings, reading represents a primary means of obtaining information. Similarly, a student’s writing or responses to written questions represent a primary means of assessing how much that student has learned. How does this situation affect academic performance of deaf students? For the purposes of the present study, we are not concerned with the extent to which (a) input through audition and output through writing and (b) input through writing and output through speech should be considered “the same” for hearing students. Insofar as most hearing students have ageappropriate reading, writing, and spoken-language fluencies, the matter does not arise. In the case of deaf students, the potential match or mismatch of input and output modalities is more relevant, and thus was examined in Experiment 2. In general, it is safe to assume that most deaf students who rely on spoken language usually are at a relative disadvantage to hearing peers, because

their reading, writing, and spoken-language skills generally are weaker than those of hearing students, and speechprint correspondences, in particular, are of lesser fidelity. Often, however, “oral” deaf students’ good speech skills lead teachers to believe, erroneously, that they hear as well as they speak; consequently, those students’ needs may go unnoticed (Moeller et al., 2007; Zhang & Tomblin, 2000). For deaf students who sign, there is frequently a gratuitous assumption that they are fluent in the sign language vernacular, a situation that is highly unlikely for the vast majority who have hearing parents. Those students often learn to sign after the first few (language-sensitive) years of life, and may never achieve true sign language fluency (Mayberry, in press; Mayberry & Lock, 2003). It even could be argued that most deaf students who have deaf parents may lack true native sign language fluency, because over 95% of those parents would have had hearing parents themselves (Mitchell & Karchmer, 2004), and thus would have learned their first language from non-native signers. Returning to the classroom, even if signing deaf students have access to instruction through sign language interpreting or teachers who sign for themselves, it may be that assessments in the printed vernacular hold a greater bias against them than against other deaf or hearing students. Marschark, Sapere, and colleagues (2004) therefore utilized conditions in which deaf college students saw signed lectures and then were tested using printed multiple-choice tests (Experiment 1) or signed versions of the same tests (Experiment 2). Signed lectures and their corresponding questions were evaluated in both ASL and English transliteration (English word order with characteristics of ASL). Results were essentially the same for the deaf

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monious to conclude that deaf students’ difficulties in learning via reading—at least relative to hearing peers—are not specific to print but represent more general barriers associated with language comprehension in any mode. Yet another possibility is that neither of the indexes used in the present study to assess comprehension provided a good fit with deaf students’ cognitive/learning strategies (Hauser et al., 2008; Marschark, 2006). Experiment 2 therefore was designed to replicate Experiment 1 using an alternative measure of comprehension/learning and eliminate what might have been a confound in the first experiment.

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students regardless of test format or signing mode. In all conditions, their performance was significantly below that of hearing peers who received their tests via print or audition, respectively. To date, however, this finding has not been replicated. In the present study, Experiment 2 provided replications and extensions of both Experiment 1 and the study by Marschark, Sapere, and colleagues (2004). The passages from R. R. Kelly and colleagues (2001) again were presented to deaf students in print and via sign language. Content learning was assessed by having the deaf students recount passage content in writing or in sign. Hearing students received the passages in print and via audition, and comprehension was assessed by having them recount the information in writing or through speech. Experiment 2 thus involved a 2 (hearing status: deaf or hearing) by 2 (input mode: print or signed/ spoken) by 2 (output mode: written or signing/speaking) design. This methodology allowed students to generate more complete output for each passage than was the case in the study by R. R. Kelly and colleagues and in Experiment 1, and thus offered a more comprehensive assessment of how much students gained from passages presented in different modes.

Method Participants Experiment 2 involved 23 deaf students and 24 hearing students enrolled at RIT, none of whom had participated in Experiment 1. Demographic information for the deaf students was available both from institutional databases (19 students) and a communication questionnaire administered between students’ receipt of the two passages (23 students). Data were available from self-reports on

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DEAF STUDENTS’ READING CHALLENGES • preferred mode of communication (ASL only, SimCom, signing English, speech only) • sign skill • speech skill • SimCom skill • speechreading skill (with sound) • age at which the student began learning to sign • hearing aid and cochlear implant use and from the databases on • ACT4 English, mathematics, and composite scores • California Reading Comprehension Test scores • Michigan Reading Test scores • NTID reading and writing placement test results • number of deaf parents • pure tone threshold (PTA) in each ear The deaf students’ average pure tone hearing thresholds were 100 dB in the left ear and 98 in the right ear. On 5-point Likert scales ranging from “no skill” to “excellent,” these students rated their ASL skills at 3.8 and their speech skills at 3.6, reflecting the considerable communication flexibility found in the population of deaf students at RIT (Convertino et al., 2009). Overall, they had a slight preference for sign over speech (2.9 on a 5-point scale from speech to sign). These and the other variables were considered in the analyses described below.

Materials and Procedure The materials were the same as those used in Experiment 1. Students again read one passage and saw the other spoken/signed (with audio). Within each input modality, half of the students immediately wrote down as much of the passage as they could remember. The other half did the

same task, but produced their output either orally (hearing students) or in sign language (deaf students).5 This method yielded four input-output conditions: read-written, read-signed/ spoken, signed/spoken-written, signed/ spoken-signed/spoken. Students had as long as they wanted to complete their retellings. Participants were tested individually, and passages and presentation order were counterbalanced. Each student was videotaped during signed/ spoken output for ease of scoring. Hearing students’ transcripts were prepared verbatim from their spoken output. Deaf students’ transcripts were prepared by a pair of nationally certified sign language interpreters—one a child of deaf parents—working together but blind to the purposes of the experiment. The two watched each signed production several times, and they alternated preparing initial interpretations using a tape recorder. After one interpreter prepared a transcript, the second interpreter would edit it; then the two would watch the videotape and continue editing until they were in full agreement. Because Experiment 1 called into question the validity of the method of scoring main ideas used by R. R. Kelly and colleagues (2001), we adopted an alternative rubric drawn from the reading literature. Scoring of student output involved three different indexes, utilizing a methodology derived from the comprehension-retelling procedure described by Leslie and Caldwell (2006, pp. 83–88). Up to 3 points were awarded for the generation of the implicit theme of each passage (e.g., the deepest parts of the Earth’s oceans remain a mystery); up to 2 points were awarded for each of the three central ideas presented in each passage (e.g., these regions make up a considerable portion of the Earth’s oceans); and 1 point was awarded for each of the rele-

vant details in each passage (e.g., shallow areas include continental shelves). There were 35 relevant details in the Sea passage and 30 in the Snakes passage. In addition, a total, summed score was obtained.

Results and Discussion Because of the different number of points possible in each scoring category, all raw scores were converted to proportions. A 2 (hearing status) by 4 (condition) ANOVA with the theme scores as the dependent variable yielded only a marginal hearing status by condition interaction, F(3, 138) = 2.55, MSE = .10, p < .06. Deaf students scored higher when they wrote their output than when they signed it, a finding that argues against any bias inherent in assessments of their learning via writing (see Table 2). There was no discernible pattern in the hearing students’ data. A similar analysis with idea scores as the dependent variable yielded only a significant main effect of hearing status, F(1, 46) = 6.57, MSE = .14, as hearing students produced more of the three central points of each passage in three of the four conditions (see Table 2). Analysis of the detail scores also yielded a significant effect of hearing status, F(1, 46)=4.24, MSE = .09, as hearing students produced more passage details than their deaf peers, regardless of condition (see Table 2). There was also a significant main effect of condition, F(3, 138) = 4.79, MSE = .02, as the signed/spoken-written condition yielded a higher proportion of generated details (.40) than the signed/spoken-signed/spoken condition (.29). None of the other simple comparisons were significant, with the read-written (.35) and readsigned/spoken (.33) conditions falling between the other two, a pattern that held for both deaf and hearing students.

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Mean Proportions (and Standard Deviations) for Deaf and Hearing Students Reading Science Passages or Seeing Them Signed or Spoken (Respectively) and Reproducing Them via Writing or Sign/Speech in Experiment 2 Condition

Deaf

Hearing

Read-written Theme score Ideas score Details score Total score

.50 (.43) .26 (.30) .30 (.15) .36 (.18)

.46 (.33) .48 (.32) .39 (.18) .45 (.19)

Read-signed/spoken Theme score Ideas score Details score Total score

.37 (.36) .36 (.28) .31 (.10) .38 (.18)

.62 (.32) .36 (.28) .34 (.20) .45 (.17)

Signed/spoken-written Theme score Ideas score Details score Total score

.45 (.43) .15 (.24) .31 (.24) .24 (.19)

.48 (.42) .32 (.31) .48 (.30) .36 (.20)

Signed/spoken-signed/spoken Theme score Ideas score Details score Total score

.32 (.33) .21 (.28) .24 (.18) .28 (.21)

.42 (.39) .36 (.36) .32 (.21) .30 (.20)

Finally, an analysis with total scores as the dependent variable yielded only a significant effect of condition, F(1, 132) = 9.57, MSE = .021, as the two conditions in which the passages were read yielded significantly higher scores than the two conditions in which the passages were signed or spoken (read-written = .40, readsigned/spoken = .42, signed/spokenwritten = .30, signed/spoken-signed/ spoken = .29). There was a trend toward hearing students outperforming deaf students, F(1, 44)= 3.23, MSE = .08, p < .08, but there was no hint of an interaction, as input mode rather than output mode appeared to be the important factor in overall comprehension of the passages for both groups (see Table 2). (Total scores were composed of up to 3 points from the theme scores, up to 9

from the idea scores, and up to 30 or 35 points—depending on the text— from the detail scores, which had the effect of weighting the total toward the generation of details.) A correlational analysis indicated that among deaf students, theme scores and detail scores were significantly related, r(21) = .50, and all three subscores were significantly correlated with total scores, rs(21) = .46–.72. Among hearing students, the only significant correlation was that between the proportion of details recalled and the total score, r(21) = .72. Together with results from the ANOVAs, these results suggest that deaf students are more likely than hearing students to see the meaning of a passage as a collection of individual ideas, rather than potentially as more than the sum of its parts. There

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is some evidence to support this view from studies of deaf and hearing students’ reading (Banks, Gray, & Fyfe, 1990; Marschark, DeBeni, Polazzo, & Cornoldi, 1993), but the issue has not been explored with through-the-air communication (sign or speech). At the same time, the relatively low level of performance exhibited by hearing as well as deaf students with the academic passages used in the present study suggests the need to examine ways in which comprehension strategies might vary with the difficulty of the materials. Correlations also were computed between the available demographic data and scores in the various conditions. There are two notable patterns evident in Table 3, where all significant coefficients are shown. First, when passages were read, only achievement variables (the ACT composite scores and NTID reading placement test results) were related to performance. Second, when input was signed, only achievement variables and speech-related variables were related to performance. The latter were all positive, as better self-rated spoken-language and SimCom skills and a preference for spoken language in the classroom were associated with higher scores (Convertino et al., 2009), even though the students generally had rated themselves as excellent signers. Selfrated sign language skills and variables associated with sign language (e.g., deaf parents, age when one learned to sign) were never significantly related to performance, regardless of whether input, output, or both were signed, a finding also obtained by Convertino and colleagues (2009). Although null findings, these results are consistent with all of the other results in suggesting that deaf college students do not gain more from sign language than they do from reading, at least when academic content is in-

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DEAF STUDENTS’ READING CHALLENGES Table 3

Correlations (With N for Each Cell) Between Demographic/Communication Variables and Performance for Deaf Students In Experiment 2 (Only Significant Correlations Are Shown) Condition

Theme score

Variable

Ideas Details Total score score score

Read-write ACT composite NTID reading

.69 (12) .70 (12) .67 (12) .65 (12)

.68 (12) .61 (12)

Read-signed/spoken ACT composite

.63 (12)

Signed/spoken-written SimCom skill Speechreading skill ACT composite ACT English NTID writing

58 (11) .68 (14) .73 (14)

.58 (14) .79 (14) .60 (12)

.59 (12) .69 (11)

Signed/spoken-signed/spoken Preferred communication mode Speech skill

.56 (14) .69 (14)

Notes. ACT, American College Test. NTID, National Technical Institute for the Deaf. SimCom, Simultaneous Communication.

volved. Similar findings have been obtained in other studies (Marschark et al., 2006; Marschark, Sapere, et al., 2004; Stinson, Meath-Lang, & MacLeod, 1981; Stinson & Ng, 1983), and we are not aware of any findings to the contrary. General Discussion The present study was undertaken to determine whether findings reported by R. R. Kelly and colleagues (2001) and interpreted as indicating that deaf students have particular difficulties in applying metacognitive strategies to reading were really about reading. The motivation for the study came in part from findings reported by Marschark, Sapere, and colleagues (2004) indicating that hearing students’ predictions concerning classroom learning were significantly related to their test performance, while deaf students’ predictions were not. Related findings emerged from a

study by Marschark and colleagues (2007). They conducted a study in which deaf college students who used spoken language (and had little or no knowledge of sign language) and deaf students who used ASL (most since childhood) played a version of the board game Trivial Pursuit. Most important for the present purposes, while taking turns asking and answering questions, students had to indicate their understanding of the questions by repeating them back to the questioner. They were encouraged to ask for a repetition before doing so if they were at all unsure. Overall, the “oral” students understood questions spoken to them by other oral students only 44% of the time, and signing students understood the questions when they were signed to them by other strong signers only 63% of the time. These relatively low levels of performance, together with the finding that oral and

signing students asked for repetition of questions only 20% and 13% of the time, respectively, were taken as an example of the “unskilled and unaware effect” (Kruger & Dunning, 1999). According to Dunning and his associates, lack of knowledge or expertise in a particular area puts an individual at a double disadvantage, in that not only will they not perform as well as others with greater knowledge or expertise, but they will be less cognizant of their lesser performance (Kruger & Dunning, 1999). Marschark and Wauters (2008) suggested that the lack of early, full, and effective exposure to language may put many deaf students in such a “double burden” situation. Not only does lack of full access to communication impede formal and informal teaching and learning, but a related lack of language fluency can leave deaf students relatively unaware of how much they are missing. In essence, Marschark and Wauters’s suggestion was that the apparent lack of comprehension monitoring frequently observed in deaf students’ reading (e.g., R. R. Kelly et al., 2001; Strassman, 1997) also occurs during through-the-air communication (sign or speech). That suggestion is consistent with the findings of the previous studies, including Marschark, Sapere, and colleagues (2004, 2005) and Marschark and colleagues (2007), and the results obtained in the present experiments. Napier and Barker (2004) obtained findings that further highlight the issue: Deaf college students reported recognizing that they never fully understood interpreted classroom lectures, missing up to 50%, but also indicated that they did not expect to do so. Recognizing that they touch on sensitive issues, we believe that such findings might help to explain why so little progress has been made in improving deaf students’ reading over the past

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students significantly outperformed the deaf students. Further, theme scores also were relatively low in Experiment 2, which utilized a theoretically based scoring rubric, and the difference between deaf and hearing students was not significant. Hearing students had significantly higher main-idea and detail scores than deaf students, however, which suggests that asking students to identify a single, comprehensive theme in academic science materials is not as straightforward a request as it seems. Either these materials do not lend themselves to such global summarization, or college students are more oriented toward remembering facts than reducing academic texts to a single sentence. The finding in Experiment 2 that deaf students learned more from reading than they did from seeing the passages signed by a highly skilled interpreter might appear surprising given deaf students’ acknowledged difficulty in reading and the implicit assumption that young deaf adults who grow up with sign language can be considered relatively fluent. Yet in over a dozen previous experiments, we have never found having deaf parents or the number of years using sign language to be significant predictors of classroom learning via sign language (Convertino et al., 2009). This is not to say that those variables are unimportant for language acquisition, language comprehension, or academic achievement, but they evidently are insufficient to result in deaf students’ learning as much in the classroom as their hearing peers, regardless of whether communication is via interpreters or from deaf or hearing instructors signing for themselves (Marschark, Sapere, Convertino, & Pelz, 2008). If it were not for the frequent suggestion that deaf students are disad-

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100 years: Perhaps investigators have been looking in the wrong place. Focusing on print literacy and assuming that, at least by college age, deaf students’ through-the-air communication skills are sufficient for educational purposes, researchers and educators may have overlooked more basic needs with regard to the automatization of lower-level language processing skills (Bebko & Metcalfe-Haggert, 1997). It is well recognized that building print literacy depends on a firm foundation in a first language (Mayer, 2007; Mayer & Akamatsu, 1999, 2003; Perfetti & Sandak, 2000), and in the absence of such skills, higher-level processes such as inferencing and comprehension monitoring are at just as much risk as lower-level processes involving vocabulary and grammar (L. Kelly, 1996). There is no reason to believe—at least on the basis of the present results—that the failure to utilize such processes is limited to language comprehension that involves print. Although the present two experiments used different measures of learning, scores in both indicated that deaf students learned no more from signed instruction than they did from reading the corresponding texts. It could be argued that this finding was the result of the materials being mediated by an interpreter, but Marschark and colleagues (2006, Experiment 3) obtained the same result when geography lessons were signed by a deaf teacher or read by 12-to-16-year-olds. Experiment 1 also suggested that the “main idea” scoring of R. R. Kelly and colleagues might not be a valid indicator of comprehension, as both hearing and deaf college students scored well below 50%, despite the fact that the texts were written at a 8th-to10th-grade level. Scores on posttest multiple-choice questions, in contrast, were relatively high, and, consistent with previous studies, hearing

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vantaged by the educational necessity of reading, the finding that text was a relatively more effective medium for learning than sign language might not be surprising. Chall (1996), for example, suggested that by early adolescence, reading becomes more efficient than listening (for hearing students). In her Learning to Read–Reading to Learn framework, there are five stages: Stage One, Initial Reading and Decoding typically is achieved in first and second grades, when children “learn the code” and recognize an increasing number of words. Stage Two, Confirmation is attained in second and third grades, as children consolidate their word-decoding skills and reading becomes relatively accurate, effortless, and fluent. At Stage Three, Reading for Learning New Information, which occurs in grades 4–8, children start reading to learn (versus learning to read). This is the stage in which they use print to develop new ideas and read texts from particular viewpoints, and in which Chall argues that reading becomes more efficient than listening (see also Verhoeven & van Leeuwen, 2008). To the extent that deaf students’ learning via sign language is functionally or even just pragmatically comparable to hearing students’ learning via spoken language, one might argue that the present results simply indicate that the deaf college students in these experiments have reached a point at which reading may be more efficient than through-the-air communication. Wauters, Tellings, van Bon, and van Hafften (2003) found that at the point where children make the transition from learning to read to reading to learn (grade 3 in the Netherlands), the vocabulary and complexity of school texts also change. Deaf children may well lag behind hearing peers at that point and could benefit from the use of alternative reading

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DEAF STUDENTS’ READING CHALLENGES materials or additional years of reading instruction—or more effective and targeted instruction—until various reading subskills have become automatic (Bebko & Metcalfe-Haggert, 1997; Verhoeven & van Leeuwen, 2008). Deaf students who have succeeded in gaining admission to college generally have acquired sufficient print literacy skills and relevant knowledge to allow them to function in that setting. As children, however, most were never taught sign language, but had to pick it up from deaf adults, peers, and signing hearing individuals—the latter frequently less-thanideal models. As a result, their fluency in sign language would not necessarily exceed their fluency in reading the vernacular, whether or not they are aware of that fact. A related explanation for the advantage of reading over learning from sign or speech is the evanescence of through-the-air communication. We suggested earlier in the present article that deaf students’ seeing sign can be considered functionally equivalent to hearing students’ hearing speech for the purposes of interpersonal communication, and the two modalities share the linguistic quality of rapid fading. Michael, Keller, Carpenter, and Just (2001) observed very different patterns of brain activity when hearing individuals heard or read identical sentences, an indication of qualitative differences between the two ways of processing information. Michael and colleagues suggested that listening requires much more processing—as well as greater utilization of memory resources—than reading, in large measure because of rapid fading. With printed text, in contrast, the reader can control how fast words are processed, and portions of the text can be re-read to provide context or disambiguation of more difficult or misread segments (an option not available with real-time text).

To the extent that deaf students are less than fluent in sign language, at least relative to hearing students’ spoken-language abilities, the lack of automatization in lexical lookup and grammatical parsing will further tax linguistic short-term or working memory, a resource in which they also lag behind hearing peers regardless of whether they use signed or spoken language (Bavelier et al., in press; Pisoni et al., 2008). For example, deaf students’ weaker associations among concepts relative to hearing peers (Marschark, Convertino, et al., 2004; McEvoy, Marschark, & Nelson, 1999) would result in their being less likely to automatically activate related knowledge during reading (i.e., there would be less “spreading activation”), and thus comprehension would tend toward being more superficial. Differences in such processes during sign language comprehension versus print comprehension remain to be empirically examined. The finding that learning via the reading of printed texts surpassed learning via sign language for deaf students in the present study, but not in the experiments by Marschark and colleagues (2006) involving (the more evanescent) realtime text, suggests that such investigations would be worthwhile. In summary, the present experiments were undertaken to explore possible similarities in deaf students’ comprehension of sign language and print, with the ultimate goal of improving learning though both media. In the context of findings from previous studies, the present results suggest that although deaf college students frequently might believe that they understand sign better than print, the reverse may be true. At least in the context of STEM content (science, technology, engineering, and mathematics), results from this and previous studies indicate that deaf students un-

derstand less of a signed lecture than they think they do, and may understand more—or at least learn more— from printed text than they think they do. Such results are informative with respect to both language comprehension and classroom learning, pointing to the need for research and instruction concerning metacognitive strategies during language comprehension at large, and not only in reading. More generally, these findings suggest that while deaf students frequently encounter challenges in reading comprehension, the roots of such difficulties may lie in more general language-comprehension processes and thus may be amenable to improvement through multiple pathways. Hopefully, such alternatives can be exploited more successfully than the purely print-focused approaches that have yielded relatively little progress in the past. General Note Preparation of the present report was supported by Grant No. REC–0633928 from the National Science Foundation. Any opinions, findings, conclusions, or recommendations expressed in this article are those of the authors and do not necessarily reflect the views of the National Science Foundation. Notes 1. “English” and “American Sign Language” (ASL) are used generically here to refer to any written language and any natural sign language. 2. Only one student reported having deaf parents, and this variable was not included in the analyses. 3. The authors wish to thank Ronald Kelly, John Albertini, and Nora Shannon for sharing their materials, training us in their scoring method, and providing feedback during the conduct of this study. 4. American College Test, normally required for students entering RIT.

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