Measuring literacy outcomes for the blind and for the deaf: Nationally representative results from Kenya

International Journal of Educational Development 69 (2019) 1–8

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Measuring literacy outcomes for the blind and for the deaf: Nationally representative results from Kenya

T

Benjamin Pipera, , Jennae Bulatb, Dunston Kwayumbaa, John Oketcha, Lilian Ganglac ⁎

a

RTI International, 5th Floor, The Westwood, Vale Close, off Ring Road Parklands, P.O. Box 1181-00621, Village Market, Nairobi, Kenya RTI International, 3040 East Cornwallis Road, P.O. Box 12194, Research Triangle Park, NC, 27709-2194, USA c USAID/Kenya and East Africa, U.S. Embassy, United Nations Avenue, Gigiri, PO Box 629-00621, Village Market, Nairobi, Kenya b

ABSTRACT

Evidence is scarce as to the literacy skills of children with special needs in low- and middle-income countries. Utilizing nationally representative data from Kenya, we present literacy outcomes for the blind and for the deaf in English, and for the blind in Kiswahili in Grade 1 and 2. Although comparisons with children in “regular” classrooms would be inappropriate due to language differences, we used large-scale data available from non-special-needs Kenyan classrooms to investigate the distribution of literacy skills. We found that children served by special schools for the blind outperformed those who were attending special units within “regular” schools, across nearly all estimates of literacy. For the deaf, no meaningful differences emerged in performance scores between children attending special schools and special units. Further, language preferences for the deaf population varied broadly; future research should consider assessing skills in Kenya Sign Language as well as Signed Exact English. Given the low literacy skills of both the blind and the deaf populations, we recommend substantial investment in programs designed to improve literacy outcomes for these populations. We also recommend examining literacy skills among all special-needs learners at scale, because of the complexity countries find in supporting these diverse learners, and because only looking at a handful of schools can mask trends in low performance that would become more obvious at scale.

1. Introduction More than a billion people, or approximately 15% of the world’s population, have some type of disability (World Health Organization, 2011). Eighty percent of these individuals with disabilities live in lowand middle-income countries (LMICs) (World Health Organization, 2015), and an estimated 150 million are children (i.e., 14 years old or younger), 93 million of whom have a moderate or severe disability (World Health Organization, 2011). Most children with disabilities live in Africa (United Nations Educational, Scientific, and Cultural Organization (UNESCO), 2005). Although the disabilities terminology encompasses a wide range of actual disabilities, many of which are worthy of research, the focus of this analysis is on the deaf and on the blind. An estimated 253 million people—including 19 million children—live with visual impairment globally (World Health Organization, 2017). Approximately 1.4 million children worldwide have irreversible blindness (World Health Organization, 2017). In the United States, the National Center for Children’s Vision and Eye Health (2016) estimated that 3% of children younger than 18 are blind or

visually impaired1 ; the 2010 U.S. census estimated this figure to be less than 1% (Brault, 2011). These estimates from the United States are comparable to the prevalence estimates for children who are blind or partially sighted in the United Kingdom (less than 1%) (Royal National Institute of Blind People, 2018) and across countries of the Organisation for Economic Co-operation and Development (Centre for Educational Research and Innovation, OECD, 2000). Regarding hearing, over 5% of the world’s population—or 466 million people—have disabling hearing loss (432 million adults and 34 million children) (World Health Organization, 2018).2 In high-income countries, the World Health Organization (2012) estimated approximately 0.5% of children (0.8 million) with disabling hearing loss. Among OECD countries almost two decades ago, estimates of children who were deaf or only partially hearing ranged from 0.07% (in Greece) to 0.21% (in Portugal) (Centre for Educational Research and Innovation, OECD, 2000). A 2013 study found that 1.23% of children ages 0–14 in sub-Saharan African countries, and 1.53% of similarly aged children in the South Asia region, were moderately to profoundly deaf, compared to 0.31% of children ages 0–14 in high-income countries (Stevens et al., 2013).

Corresponding author. E-mail address: [email protected] (B. Piper). 1 Defined as having trouble seeing even when wearing glasses or contact lenses. 2 While global prevalence estimates for children who are deaf-blind are unavailable, in the United States an estimated 10,000 children and young adults were deafblind in 2008 (National Center on Deaf-Blindness, 2008). Data on learning outcomes in LMICs for children who are deaf-blind are also unavailable. ⁎

https://doi.org/10.1016/j.ijedudev.2019.05.002 Received 27 November 2018; Received in revised form 9 April 2019; Accepted 10 May 2019 Available online 04 June 2019 0738-0593/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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The research evidence on the prevalence of vision and hearing loss is much more likely to be available from high-income countries than from LMICs, because many countries find it challenging to undertake wide-scale screening. The available estimates, however, suggest that the prevalence of disabilities is higher in LMICs. For example, Parikshit and Clare (2007) estimated that for a hypothetical population of 5 million very poor children (out of a total of 10 million children overall), 1.2 out of every 1000 children (0.12%) would be blind. More recently, the World Health Organization estimated that among African countries, 15% of the overall population was blind and 8.3% had low vision (World Health Organization, 2012a).

Similar to the challenges for children who are blind, children who are deaf often have poorer reading comprehension and other literacy skills than hearing peers: According to a 2016 study, approximately half of students who were deaf in the United States graduated from high school reading below the fourth-grade level, and only 7%–10% of high school graduates who were deaf could read at or above the seventh-grade level (Hrastinski & Wilbur, 2016). Estimates in 2001 were that half of youth in the United States who were deaf did not graduate from high school (Blanchfield et al., 2001). This low academic performance, however, is not necessarily correlated with hearing loss alone. In one 5-year study, for example, students who were deaf and enrolled in mainstream classrooms achieved average or above-average academic levels (Hrastinski & Wilbur, 2016). Rather, the ability to achieve grade-level academic performance in English literacy appears to be positively correlated with sign-language skills. Children who are exposed to vocabulary and the linguistic components of sign language can develop levels of literacy at the same rate as their hearing peers (Dostal et al., 2017). In the United States, the United Kingdom, and other high-income countries, resources exist to support children who are blind or deaf. While segregated schools do continue to serve some students who are deaf or blind, during the 21st century, students with disabilities have increasingly been educated in mainstream classes, although percentages can vary dramatically among high-income countries (Centre for Educational Research and Innovation, OECD, 2000). Even so, many students with vision or hearing disabilities struggle to attain a high-quality education. Students may lack access to specialist teachers or other support structures (National Deaf Children’s Society, n.d.), and without full access to the curriculum or the language being spoken by peers and teachers, they are deprived of full academic success and social interactions. These challenges of resources and access are even more dramatic in LMICs, in which funding for education is limited and children with disabilities generally are segregated to a few special schools or special units not known for their high-quality education services. In many LMICs, support for children who are deaf or blind may come not from the government but from philanthropic or religious groups. In Liberia, for example, of the 12 schools for children with disabilities, 11 are privately operated; one school, for the blind, is sponsored by the government. In Cambodia, a 2016 review found that the only schools for children who were deaf or blind were private, although the government planned to assume control of these schools. By contrast, Kenya has been particularly progressive, in terms of not only its deep and comprehensive special education policies but also the extent to which the government has retained control over segregated schools for students with disabilities (VSO Jitolee, 2016). Historically, in most LMICs, it has been difficult for the government to implement large-scale literacy interventions for children with disabilities, given the children’s diverse schooling locations. Even enrollment in a designated school for children with disabilities does not ensure that children with disabilities meet their learning targets; in Tanzania, for example, a study of one segregated school for the deaf and two integrated schools found evidence from both teachers and students of poor academic results (Ministry of Education, Science and Technology, 2018). Small-scale pilots of interventions have shown promise in building literacy among children who are blind or deaf (see, for example, in India, Gillen et al., 2016; in South Africa, van Staden, 2013; in Tanzania, Simon, 2017; in Kenya, Sense International, 2018). A rapid evidence assessment of intervention outcomes in LMICs, for example, found 15 small-scale studies that assessed the impact of interventions intended to promote learning outcomes for children with disabilities at the primary level (Kuper, n.d.). International donors also have funded efforts in LMICs to train teachers in differentiated instruction, the use of Individualized Education Plans, development of braille and large-print materials, and piloting of vision and hearing screening tools (see, for example, Betts & Strigel, 2018; RTI International, 2017). It is, to our knowledge, only in Kenya that a national-scale implementation of adapted teaching and learning materials, teacher training, and student assessment has occurred for both the blind and the deaf. Although the handful of studies described above examined the prevalence of blindness and deafness (as well as other disabilities) in

2. Literature review A handful of studies from LMICs give specific prevalence data for individual countries. A national survey of blindness in Nigeria found a prevalence of 4.2% for children and adults (Rabiu et al., 2012). In Ethiopia, a 2017 study found that 3.9% of school-aged children had low vision and 3.3% had severe visual impairment (Bezabih et al., 2017). In Kenya, research by VSO Jitolee (2016) suggested that 10.4% of children under the age of 21 had a hearing impairment, while 19.5% had a visual impairment. As of about 2010, an estimated 1.9% (6.8 million children) with hearing loss were living in sub-Saharan Africa and 2.4% (12.3 million) of such children were living in South Asia (World Health Organization, 2012b). Causes of vision and hearing loss vary, particularly in LMICs. Of the estimated 19 million of children who have vision loss, 12 million children have a vision impairment due to refractive error (World Health Organization, 2017). Infectious eye diseases, such as trachoma and onchocerciasis, can also cause vision loss (World Health Organization, 2017), particularly in LMICs, where such diseases are less likely to be promptly diagnosed and treated. Among children in LMICs under 15 years of age, 75% of hearing loss is attributable to preventable causes, such as infections (e.g., mumps, measles, rubella, meningitis, cytomegalovirus infections, and chronic otitis media), complications at birth (e.g., birth asphyxia, low birthweight, prematurity, and jaundice), and the use of ototoxic medicines in expectant mothers and in babies (World Health Organization, 2018). Children who are profoundly or completely deaf or blind struggle to attain the levels of literacy reached by their peers without such disabilities. According to the American Printing House for the Blind, in 2016 only 8.5% of children (ages 4–21) enrolled in public schools in the United States were identified as fully braille readers; conversely, over half (53%) of children who were blind were identified as either nonreaders or pre-readers (BrailleWorks, 2016). Recent data indicated that nearly a quarter (23.2%) of US individuals who were blind did not finish high school, and only 14.9% achieved a bachelor’s degree or higher (National Federation of the Blind, 2018). This compares with only 12% of the general population in the United States without a high school diploma or general education diploma (GED), and nearly 33% of individuals with a bachelor’s degree or higher (Ryan & Bauman, 2016). Given that a very similar proportion of 25- to 34-year-olds in OECD countries is known to have completed a bachelor’s degree or higher (OECD, 2016), the gap between learning outcomes of the blind and the rest of the population in OECD contexts might be similarly wide. The lack of literacy in braille in the United States stems in part from a lack of braille instruction, with only an estimated 10% of blind children learning it in formal educational settings (National Federation of the Blind, 2009). Learning to read braille is a more complex process than learning to read printed text. In addition to learning the foundational skills of reading—such as letters, vocabulary, and grammar—a learner of braille must learn a large number (189) of braille contractions and rules about their use. Although learning alphabetic braille (which does not use contractions) for beginning reading instruction facilitates the development of reading fluency, vocabulary, comprehension, and spelling, it can also slow down reading braille. Conversely, learning braille contractions speeds reading, by allowing a reader to take in more information at a time, but it is more challenging for emergent readers to learn (Emerson et al., 2009). 2

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LMICs, few large-scale studies have estimated learning outcomes for these populations, and even fewer have compared learning outcomes for the populations with disabilities and the populations without disabilities at school. Those that did attempt to compare learning outcomes for students with and without disabilities struggled with how to effectively identify children with disabilities to be included in the sample (Rose et al., 2018).

Tusome’s second area of SNE assistance has been teacher professional development. By the end of August 2018, Tusome had trained 288 teachers to implement classroom instruction using the teachers’ guides—187 teachers of hearing-impaired learners and 101 of visually impaired learners. In addition, Tusome guided the government’s coaches, called Curriculum Support Officers for special-needs education, to give instructional support to teachers of these pupils in public schools. To evaluate the impact of the Tusome SNE program, the Tusome project staff and technical advisors from the Ministry of Education collected baseline data in May 2017 and are planning an endline study in late 2019. Note that data from the Tusome “baseline” assessment was collected two to three months after the intervention began in grade 1, and teachers had invested heavily in instructing students in letter sounds by that point in the program. The data presented here were captured during two national baseline surveys of literacy skills—one for the deaf population in special units and special schools, and the other for the blind population in special units and special schools. These data, as we suggest below, may embody the first nationally representative study of SNE learning outcomes in an LMIC. For this paper, we used the data to seek answers to the following research questions: RQ1: What are the literacy outcomes for the blind and the deaf in Kenya? RQ2: Do the literacy outcomes differ by special units or special schools? RQ3: What are the factors that relate to literacy outcomes of blind and of deaf children?

3. Kenyan context In Kenya, the prevalence levels for hearing impairment and visual impairment are 12% and 10%, respectively, of the overall total number of people with disabilities. This country is one of those in which children with disabilities are often taught in special schools and units. This arrangement, however, is contrary to government policy and other declarations to which Kenya is a signatory: The Sector Policy for Learners and Trainees with Disabilities (Ministry of Education, 2018a,b) and Goal 4 of the Sustainable Development Goals (United Nations Development Programme, 2015). Inclusive education, rather than segregated education for children with disabilities, is also an expectation of the United Nations Convention on the Rights of Persons with Disabilities (United Nations, 2018). Although Kenya has not fully implemented inclusive education, meaningful efforts have been made to improve the quality of education that learners with disabilities receive. For example, most teachers in special schools for early grade learners with disabilities have been trained in special-needs education (SNE) (VSO Jitolee, 2016). Since the 1990s, the Kenya Institute of Special Education (KISE) has been offering a 2-year residential in-service course for teachers. The course equips teachers with requisite knowledge, skills, and attitudes for supporting children with various categories of disabilities in the classroom. In previous eras, KISE had the ability to enroll only two student teachers from every district in this two-year course. To better support SNE in Kenya, this program expanded enrollment in 2001 by allowing distance-learning methods, and as of late 2017, KISE could train approximately 4000 teachers annually. Despite this progress, Kenya still experiences substantial challenges in bringing high-quality education to learners with visual and hearing impairments. Learners with visual or hearing loss have unique educational needs that are most effectively met using a multidisciplinary team approach, including professionals from different sectors. These learners need qualified teams of teachers as well as specialized materials and equipment such as braille books, braillers, slates, and styluses for learners with visual loss; and hearing aids and access to sign language for learners with hearing loss. Kenya has not yet been able to offer this approach in most schools, particularly in rural areas.

5. Research design In this section we present the research design of the two national surveys, including the sampling methodology, the achieved sample size, and a summary of the measurement tools. 5.1. Sampling methodology and achieved samples The Tusome SNE study was nationally representative for both the blind and the deaf, although the populations and therefore the sampling were separated. The Kenyan Ministry of Education supplied the Tusome technical team with a list of all 14 special schools and special units for the deaf and for the blind. This national list served as the sampling frame, and the study included each of these schools. The Tusome study sampled students in grades 1 and 2 in those schools who were attending school on the day of data collection. For cases where the school had more than 10 pupils, five grade 1 and five grade 2 pupils were randomly selected using systematic random sampling, resulting in 131 pupils being assessed. Table 1 presents the achieved sample by gender. Given differences in enrollment by gender, more boys were sampled than girls. For the deaf population, the Ministry of Education provided a list of 112 schools and special units for the deaf. Proportional-to-population sampling was used to select schools for the study, resulting in 39 schools being selected. In these schools, four grade 1 and four grade 2 pupils were assessed. For sampled schools which had more than four deaf pupils in each of the target grades, a process of systematic random sampling was used to select the eight pupils assessed. This systematic random sampling procedure resulted in a sample of 298 deaf students nationally. Given differences in

4. Tusome support to SNE in Kenya Kenya began implementing the Tusome Early Grade Reading Activity in 2014 and introducing interventions at grade 1 beginning in 2015. Tusome is supported by the United States Agency for International Development (USAID)/Kenya. Although the Tusome program focused only on children without disabilities during its first years of implementation (2015 and 2016), the demand from the government and the community to support children with special needs was overwhelming. The appeals resulted in requests to USAID to extend Tusome to cover the blind and the deaf populations. Tusome’s SNE activities focused on two areas. The first was the adaptation, development, production, printing, and distribution of learning materials to support the blind and the deaf in Kenya. Working with KISE and the Kenya Institute of Curriculum Development, Tusome developed, printed, and eventually distributed learning materials for these populations at a national level. A total of 3096 adapted learner books and teachers’ guides for the deaf and 667 braille materials for the blind were printed and distributed nationally.

Table 1 Achieved sample for the blind and for the deaf. Source: Kwayumba et al. (2018), p. 16. Gender

Boys Girls Total

3

Blind pupils

Deaf pupils

Grade 1

Grade 2

Total

Grade 1

Grade 2

Total

37 31 68

35 28 63

72 59 131

77 72 149

80 69 149

157 141 298

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Table 2 Mean English and Kiswahili braille EGRA subtask scores (standard errors in parentheses). Source: Adapted from Kwayumba et al. (2018), pp. 22–23. Subtask and unit of measure

Letter sound fluency (correct letter sounds per minute [clspm]) Phoneme segmentation (% correct) Syllable fluency (correct syllable sounds per minute [csspm]) Familiar word fluency (correct words per minute [cwpm]) Oral reading fluency (cwpm) Reading comprehension (% correct)

English

Kiswahili

Grade 1

Grade 2

Grade 1

Grade 2

7.2 (2.2) 40.6 (9.2) N A−1 5.6 (2.3) 4.5 (2.3) 7.8 (4.6)

18.8 (4.1) 66.4 (6.9) N A−1 10.7 (2.7) 16.1 (4.4) 36.1 (9.0)

7.2 (2.4) N A−1 5.2 (2.3) 3.1 (1.6) 3.0 (1.6) 10.5 (5.3)

20.3 (4.1) N A−1 16.1 (3.6) 10.1 (2.6) 9.6 (2.5) 37.0 (9.4)

Note. N A−1 = not applicable; some subtasks were unique to one instrument.

5.3. Data collectors’ training and reliability

enrollment by gender, there were more boys sampled than girls.

The Tusome SNE study employed 45 assessors, of whom 24 administered the braille EGRA thanks to their braille skills, as measured by experts in braille external to the program. Another 21 assessors administered the EGRA for the deaf in SEE given their fluency with that language. This team of assessors was trained on EGRA procedures for one week by experts from KISE and the Ministry of Education, as well as by Tusome technical officers. Two aspects of the training were learning how to fully administer the instruments via data collection software (Tangerine™) on digital tablets, and how to upload the results daily to a central server for compilation and review. During the training, the assessors were examined using interrater reliability (IRR) assessments. The assessors who undertook the braille EGRA assessment had an average IRR score of 97%, while those for the EGRA for the deaf in SEE had an IRR score of 94%. Both scores were above the threshold for acceptability. The assessors worked in pairs to collect data between July 3 and 7, 2017.

5.2. Assessment tools Blind pupils were assessed using an Early Grade Reading Assessment (EGRA) instrument that originally had been tailored for Kenya, for use with students without disabilities, and that was further adapted into braille for both English and Kiswahili. The adaptation process involved experts from the Kenyan Ministry of Education, Kenya Institute of Curriculum Development, and KISE who had experience in designing materials and assessment tools for this population. The English and Kiswahili braille tools both contained six subtasks, among them being letter sound identification, familiar word reading, and oral passage reading with five associated reading comprehension questions. They differed in that the Kiswahili instrument had a syllable-reading subtask, and the English assessment incorporated a pupil context interview that examined the socioeconomic status of the pupils. The tools are presented in Appendices A and B. Similar to the braille EGRA, deaf pupils were assessed using the EGRA tool tailored for Kenya and subsequently adapted for the deaf, presented in Appendix C. For the adaptation process, the Tusome team engaged a set of Kenyan experts in curriculum development, materials development, and assessment tool development from the Ministry of Education, the Kenya Institute of Curriculum Development, and KISE. The tool focused specifically on skills in Signed Exact English (SEE), as this was the preference of the Ministry of Education and the Kenyan technical experts. The assessment subtasks were letter name identification, reading of familiar words, and passage reading and comprehension (Kwayumba et al., 2018). In addition, the study team administered interviews to head teachers and teachers in grades 1 and 2, to help determine whether any school- or classroom-level characteristics correlated with learners’ performance on the EGRA subtasks. Interview items examined pupil and teacher attendance, availability of instructional materials, the state of the school’s infrastructure, and opportunities for instructional support. The study team used Cronbach’s alpha to test item consistency. The English braille tool had an acceptable reliability coefficient of 0.88, and the Kiswahili braille tool had a very high coefficient of 0.97, but the SEE tool coefficient was 0.54 (Kwayumba et al., 2018). The study team posited that the SEE tool’s poor performance stemmed in part from pupils’ and teachers’ uneven levels of facility with SEE as opposed to Kenyan Sign Language (KSL), and confusion between the signs in the two languages. Given the poor Cronbach’s alpha result, the EGRA tool for the deaf did not meet the basic threshold to be able to claim that it could reliably analyze reading outcomes. Typically, data collected using such a tool would be ignored. Given the lack of data in the disabilities sector, however, we present the EGRA SEE findings to examine the general pattern of results, rather than expecting high levels of specificity in the outcomes. Adjustments that are being made to the EGRA for the deaf for the 2019 endline assessment should result in greater reliability.

5.4. Analytic methods The reading assessment data for the blind and for the deaf were weighted according to the sampling frameworks described above. We used Stata’s svy suite of commands to analyze the data, which were nationally representative of the deaf and the blind populations in grades 1 and 2 in Kenya. The analysis we present is basic, examining simple means presented in the findings section. For research question 2, we ran a simple ordinary least squares (OLS) regression to determine whether there were statistically significant differences in reading outcomes by school type, comparing children in special schools with children in special units within a school. For research question 3, we used student, teacher, and school background variables as predictors to determine whether there were statistically significant relationships with the outcomes. 6. Findings 6.1. Reading outcomes of the blind In this section we present the findings on nationally representative learning outcomes of children in grades 1 and 2 who are blind. Table 2 shows the mean scores across the six subtasks for English and the six subtasks for Kiswahili, by grade, along with the relevant units of measure. All the results showed low literacy skills. The average oral reading fluency rate of 16.1 correct words per minute (cwpm) for grade 2 English was somewhat higher than the rate of 9.6 cwpm for grade 2 Kiswahili, although the comparison is somewhat strained given the quite different syntactic and orthographic structures of the two languages. No specific Kenyan guidelines or benchmarks have been designated for reading proficiency for the blind, but even so, the results were far lower than desirable. Mean reading comprehension rates were 36.1% correct for grade 2 English, and 37.0% correct for grade 2 Kiswahili. The grade 2 percentages were significantly 4

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Table 3 Mean Signed Exact English EGRA scores (standard errors in parentheses). Source: Adapted from Kwayumba et al. (2018), p. 23. Subtask and unit of measure

Grade 1

Grade 2

Letter sign fluency (clpm) Familiar word fluency (correct signs per minute [cspm]) Sign reading fluency (cspm) Reading comprehension (% correct)

29.7 (1.7) 1.9 (0.5) 4.9 (0.8) 2.2 (1.0)

41.2 (1.7) 5.0 (1.1) 10.8 (1.2) 4.0 (1.2)

higher than found in grade 1, yet also lower than hoped for. 6.2. Reading outcomes for the deaf Fig. 2. Letter sound and letter signing fluency, by grade. Note. cwpm = correct words per minute; cspm = correct signs per minute Data sources: Tusome national literacy baseline (2015, from Freudenberger & Davis, 2017); national EGRA braille study (2017); and Signed Exact English fluency assessment (2017)

We present the reading skills for the deaf pupils in Table 3. Recall that the reliability scores of the EGRA for the deaf were less than desirable, so we do not have confidence enough in the specific mean scores to claim certainty in the results. However, Table 3 indicates with little doubt that outcomes for this group of learners were low. While the children’s ability to identify letter signs was relatively high, with a mean of 41.2 letters correctly identified in grade 2, word-identification scores were very low, with 5.0 familiar words identified correctly in grade 2 and 10.8 signs identified correctly from the EGRA story in grade 2. Comprehension also was very low, at 4.0% correct responses in grade 2. As we describe below, the low scores could be explained in part by confusion about the language used in the assessment.

fluency rate was 10.8 correct signs per minute (cspm). To read and sign words, children need to know the basic letter sounds and signs. Fig. 2 presents the results of a comparison between letter and sign fluency rates for the three populations described above. The relationships in this comparison are very different from what they were in the fluency comparison shown above. For example, when we compare grade 2 letter fluency, it appears that children without disabilities had the lowest results (10.2 clpm), with blind learners identifying 18.8 letters in a minute, although both of these rates were much lower than the signed letter fluency of 41.2 correct letter signs per minute (clspm). Note that the differences between grade 1 and 2 letter sound fluency for English and Kiswahili may have stemmed from the fact noted earlier that the Tusome “baseline” assessment actually came two months after the intervention began in grade 1, and teachers had invested heavily in instructing students in letter sounds by that point in the program.

6.3. Reading outcomes comparisons The purpose of the Tusome SNE study was not to compare learners without disabilities with those in these special units and special schools for the deaf and for the blind. The lack of data collected at similar times and the fundamental differences among braille, signed exact English, English, and Kiswahili made such comparisons impossible. In any case, we present a simple set of data in Fig. 1. For both English and Kiswahili, we present the mean fluency rates for children in the Tusome baseline assessment in 2015. This assessment was nationally representative and was used to compare gains in learning outcomes between 2015 and 2016, as presented by Freudenberger and Davis (2017). The baseline scores were very low, and the gains identified after one year were substantial. In any case, this modest comparison seems to indicate that, where comparisons are possible, reading fluency outcomes were somewhat higher in the classrooms without children with disabilities than outcomes for reading or signing in special schools and units for the deaf and blind. For example, for English in grade 2, the fluency rate for children without disabilities was 23.8 cwpm, while for braille the fluency rate was 16.1 cwpm, and for signs in SEE, the

6.4. Comparing outcomes for the blind and deaf for children in special units and special schools Our second research question asks about differences in learning outcomes for children who are attending a special school and children who attend a special unit within a special school. For the blind population assessed in this study, we found that 83.2% of the children attended special schools. The results showed that, for all of the measures

Fig. 1. Mean reading and signing fluency, by grade. Note. cwpm = correct words per minute. Data sources: Tusome national literacy baseline (2015, from Freudenberger & Davis, 2017); national EGRA braille baseline (2017); and Signed Exact English fluency assessment (2017).

Fig. 3. Comparison of mean scores between blind students in special schools and special units, by subtask. 5

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Fig. 4. Comparison of mean scores between deaf students in special schools and special units, by subtask.

except for English familiar word fluency, the mean scores of children in the special schools were statistically significantly higher than those of children attending special units (Fig. 3). For example, English oral reading fluency was 12.2 cwpm for those in special schools and only 2.1 cwpm for those in special units (p-value < 0.05). Similarly, mean reading comprehension for English was 24.0% in special schools, more than triple the 7.3% in special units (p-value < 0.05). These patterns are similar for Kiswahili, with mean reading comprehension in special units at 27.3% and only 6.6% in special units (p-value < 0.05). To answer our second research question for the deaf, we fit similar OLS regression models that examined whether school type was statistically significantly related to learning outcomes for this population. For the deaf students assessed, 89% were attending special schools, which is a slightly higher percentage than for the blind. We found no statistically significant differences in any of the four outcomes between children who attended special schools and those in special units (Fig. 4). Signing fluency in SEE was 6.7 cspm for special schools and 6.9 cspm for special units, which was not statistically significantly different (p-value 0.93).

Fig. 6. Predictors of SEE sign reading fluency and associated differences in correct signs per minute. Source: Kwayumba et al. (2018), p. 28.

Speaking English at school was associated with 6.4 cwpm higher (0.035), while in the other direction, repeating a grade was associated with 8.3 cwpm lower (p-value 0.013). Somewhat surprisingly, speaking mother tongue at school was associated with 8.9 lower oral reading fluency in braille (p-value 0.007), as was having other books or reading materials available in mother tongue at home (p-value 0.007). Many of the schoolspecific characteristics and teacher background information had no relationship with these outcomes, which also was unexpected. Given the poor performance of the SEE tool for the deaf, as explained above, we had not anticipated seeing any statistically significant relationships with background variables, let alone ones that were related to language. This is why these results, in response to our third research question, were particularly of interest, although we present these results with caution. While Kenya seems to have real complexities regarding the language of choice for Kenyans learning to sign, signing in both SEE and KSL was positively correlated with SEE signing fluency. Signing in SEE at school was associated with 4.8 higher cspm (p-value < 0.001) and signing in SEE at home was associated with 4.1 more cspm (p-value < .05), both of which make sense. Somewhat surprising is that signing at KSL at home and signing in KSL at school were associated with 4.0 cspm (p-value < .01) and 3.9 cspm (p-value < 0.001) higher outcomes, even though the language of the assessment was SEE, not KSL. Otherwise, the relationships identified in Fig. 6 were mostly as expected. From a student background perspective, having a mother or father who could read and write was associated with 4.3 greater cspm (p-value < 0.001) or 3.4 greater cspm (pvalue < 0.001) respectively, and having a mother or father who could sign was associated with 2.9 higher cspm (p-value < .01) or 3.5 higher cspm (p-value < 0.001), respectively. Preschool attendance was associated with 2.5 cspm higher (p-value < .001), as was attending the same school in January 2017 as in June 2017 by 3.1 cspm (p-value < 0.05), even though that time point was only 6 months before data collection commenced.

6.5. Predictors of learning outcomes for the blind and the deaf Our third research question queries what factors predicted differences in learning outcomes for the blind and outcomes for the deaf. These analyses emanate from OLS regressions with oral reading fluency in braille and background characteristics of the child, of the teacher, and of the school. We present in Fig. 5 statistically significant relationships. Having an English book available in braille was associated with a rate that was 14.2 cwpm higher (p-value = 0.023). A child whose mother could read and write was associated with 7.9 higher cwpm (p-value = 0.009).

7. Discussion We believe that the Tusome SNE study was the first nationally representative and national-scale assessment of literacy outcomes for the blind and for the deaf in an LMIC, and one of very few national-scale assessments of its type in the world. The study was unique not only for

Fig. 5. Predictors of braille oral reading fluency and associated differences in oral reading fluency (cwpm). Source: Kwayumba et al. (2018), p. 34. 6

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its scope, but also for its potential to increase attention to the needs of these learners in Kenya and in other countries. By examining the SNE data, we found that reading fluency rates for the blind and signing fluency rates for the deaf were lower than desirable, and that much more should be done to improve the quality of these students’ learning outcomes. At the same time, however, we found evidence seemingly indicating that children in both the blind and the deaf populations were more competent in their letters and letter signs than were children without disabilities in the equivalent grades. Our comparisons indicated that the learning outcomes for children in special schools were much higher than for those in special units. This result occurred despite the two groups being taught using the same curriculum. One possible explanation could be fewer learning materials—including braille machines, slates, and styluses—in the special units. In addition, it might be that the special units, which also support learners with low vision and learners who are not completely deaf, might not be paying sufficient direct attention to the fully blind learners. Teacher assignments to the special units might not require special-needs training as rigorously as assignments to the special schools; and it could be that most teachers who handle special units for the blind do not have specialized training or prior experience in that category of disability. In addition, based on the teacher interview data, special schools might be more likely to operate a preschool serving the same populations. If so, then children in a special school might have more opportunities to acquire literacy skills in preschool, and blind learners might have had early chances to interact with braille equipment. Finally, special schools typically are boarding schools, whereas most special units are day schools. This could imply that based solely on proximity, pupils in special schools have more supervised learning time than those in special units, and attendance rates might be higher as well. The fact that we could identify differences between special schools and special units only among the blind population inclined us to attribute the difference to the lack of braille machines and other materials in special units, a deficiency that would not affect the special units for the deaf. Data limitations did not allow us to test this hypothesis, which future research should examine in detail. Our findings pinpointed certain characteristics that were associated with higher learning outcomes. For the blind, the availability of materials—particularly materials in braille—was dominant in predicting learning outcomes. The implications of this argument are somewhat obvious, and Tusome’s support to the government to develop and distribute learning materials in braille nationally might effect improvements in this area. The findings regarding what factors might predict outcomes for the deaf were somewhat more complicated. First, the SEE tool’s lack of precision introduced uncertainty that the results identified were truly present in the population. Second, given that teachers receiving instructional support in either KSL or SEE was associated with learners’ higher SEE fluency, giving teachers specific support in improving their sign language skills could be important, particularly since parental sign language fluency also correlated with learner outcomes. Third, the debate over which of these two sign languages should be used in classrooms (and beyond) is fraught with controversy in Kenya, and in retrospect, it might have been better for the Tusome program to work in both KSL and SEE, rather than choosing one language.

July 2017. Given the delayed assessment, the differences we perceived in the outcomes between students in special units and special schools may have been due to differential implementation of the first few weeks of the Tusome intervention rather than true initial outcomes. Although the SNE study data are important for understanding the learning outcomes of children in Kenya, the study did not systematically parallel an assessment of children without disabilities, and therefore no comparison is possible. Other data that Tusome collected independently but at about the same time in the academic year (July 2016) showed relatively higher learning outcomes for children without disabilities than for those in either the blind or the deaf population. On the other hand, even if those data were collected at the same time as the assessment data for the blind and for the deaf, no formal equating processes were done between the deaf and blind literacy assessments. And, from a conceptual point of view, we would argue that these are fundamentally different languages and codes and should not be compared directly. We appeal to other researchers who may have the options and resources to undertake largescale assessments to invest in analyzing the learning outcomes of these and other special-needs populations. The data set that we analyzed did not distinguish whether the children assessed had multiple disabilities. We were therefore unable to scrutinize or differentiate the complex relationships among disabilities when examining literacy performance. We hope that future research and analyses will be able to address this shortcoming. A final limitation relates to the complicated language situation for the deaf population of Kenya. As mentioned earlier, the deaf language community has significant internal disagreement as to whether KSL or SEE is the preferable language for instruction and for assessment. Teacher interview data collected for this SNE study showed that 56.5% of teachers used SEE, while 46.4% said that they used KSL, which means that some teachers used both. The student assessment tool used SEE, such that learners who were more comfortable using KSL would have underperformed. If the Ministry of Education had directed the Tusome team to develop the tool in the other direction, using KSL, then we would expect those more capable in SEE to have underperformed. It appears that more work is required within Kenya to determine what language is preferable for the deaf learner population, and future assessment work should follow those determinations. Based on the data, the SEE tool’s unreliability may have hinged on this language complexity; in future rounds of data collection, using KSL instead of SEE, or allowing a combination of both, might be a preferable option. 9. Conclusion The national SNE study in Kenya suggests that it is possible, in LMICs, to design and implement large-scale assessments of learning outcomes for the blind and for the deaf. This innovation should encourage future research in two directions. The first is expanding assessment opportunities for these populations, and the second is expanding the types of specialneeds learners from whom we collect data on their learning outcomes. In the case of Kenya, we know that the education system supports several other categories of special-needs learners whose outcomes have not been systematically measured. Future research should design tools to estimate their skills, as Kenya has shown that it has the human capital within the government and in the education community to undertake this research. The Tusome Early Grade Reading Activity developed learning materials, training plans and documents, and instructional support activities for the blind and for the deaf in 2017, and implementation continues in 2019. Despite the scale and geographic reach of these activities, however, more thoughtful and thorough design of the interventions for these populations would be desirable in the future. That is, Tusome’s work was an initial foray into this subsector, rather than being resourced enough to design and execute the full range of services that the children learning in these schools in Kenya actually need.

8. Limitations The Tusome SNE study, while deserving credit as one of the first largescale and potentially nationally representative assessments of learning for the blind and for the deaf populations, did have several key limitations. The first was that the assessment, for both populations, occurred in July 2017, after the Tusome program had delivered materials and training for teachers at special units and special schools (i.e., from March to May 2017). Further, due to the national nature of the interventions and assessments, there were no comparison groups. Thus, our analysis necessarily focused on a descriptive understanding of the status of learning as of 7

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Declaration of interest

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None. The first author served as Chief of Party of the Tusome Early Grade Reading Activity. The second, third, and fourth authors served in various roles on the Tusome Activity. The fifth author served as the Contracting Officer’s Representative for USAID on the Tusome Early Grade Reading Activity. Acknowledgments The authors acknowledge the leadership of the Ministry of Education in Kenya, including the capable staff with expertise in special-needs education who led the Tusome Early Grade Reading Activity’s support for special needs, and the Kenyan teachers who implement in classrooms. We appreciate the generous funding of the United States Agency for International Development (USAID) for Tusome implementation. Finally, we are thankful for Erin Newton’s careful editorial contributions. All mistakes are our own. References Betts, K., Strigel, C., 2018. Using mobile technology for sensory disability screening: Field experiences from the Philippines [Presentation slide deck]. Prepared for USAID under All Children Reading–Asia, Vision and Hearing Screening Pilot Activity in the Philippines, Task Order No. AID-OAA-TO-16-00017. RTI International, Research Triangle Park, NC. http://shared.rti.org/content/using-mobile-technology-sensorydisability-screening-field-experiences-philippines. Bezabih, L., Abebe, T.W., Fite, R.O., 2017. Prevalence and factors associated with childhood visual impairment in Ethiopia. Clin. Ophthalmol. (Auckland, N.Z.) 11, 1941–1948 https://doi.org/10.2147/OPTH.S135011. Blanchfield, B.B., Feldman, J.J., Dunbar, J.L., Gardner, E.N., 2001. The severely to profoundly hearing-impaired population in the United States: prevalence estimates and demographics. J. Am. Acad. Audiol. 12 (4), 183–189. BrailleWorks, 2016. Braille literacy statistics and how they relate to equality [Web log post]. https://brailleworks.com/braille-literacy-statistics/. Brault, M.W., 2011. School-aged children with disabilities in the U.S. metropolitan statistical areas: 2010. United States Census Bureau, Washington, DC Report Number ACSBR/10-12. https://www.census.gov/library/publications/2011/acs/acsbr10-12. html. Centre for Educational Research and Innovation, OECD, 2000. Special needs education statistics and indicators: statistics and indicators. Education and skills. OECD Publishing, Geneva. Dostal, H., Gabriel, R., Weir, J., 2017. Supporting the literacy development of students who are deaf/hard of hearing in inclusive classrooms. Reading Teacher 71 (3), 327–334. https://doi.org/10.1002/trtr.1619. Emerson, R.W., Holbrook, M.C., D’Andrea, F.M., 2009. Acquisition of literacy skills by young children who are blind: Results from the ABC Braille Study. J. Visual Impairment Blindness 103 (10), 610–624. Freudenberger, E., Davis, J., 2017. Tusome external evaluation—Midline report. Prepared for the Ministry of Education of Kenya, USAID/Kenya, and the UK Department for International Development under Contract No. AID-615-TO-16-00012. Management Sciences International, a Tetra Tech company, Washington, DC. http://pdf.usaid. gov/pdf_docs/PA00MS6J.pdf. Gillen, J., Panda, S., Papen, U., Zeshan, U., 2016. Peer to peer deaf literacy: working with young deaf people and peer tutors in India. Language Language Teach. 5 (2), 1–7. (10),. http://apfstatic.s3.ap-south-1.amazonaws.com/s3fs-public/Peer%20to %20Peer%20Deaf%20Literacy_Working%20with%20Young%20Deaf%20People %20and%20Peer%20Tutors%20in%20India.pdf. Hrastinski, I., Wilbur, R.B., 2016. Academic achievement of deaf and hard-of-hearing students in an ASL/English bilingual program. J. Deaf Stud. Deaf Educ. 21 (2), 156–170. https://doi.org/10.1093/deafed/env072. Kwayumba, D., Piper, B., Oyanga, A., Oketch, J., 2018. USAID Kenya Tusome Early Grade Reading Activity: Special Needs Education (SNE) Baseline Report. RTI International, Research Triangle Park, NC Prepared for USAID under Contract No. AID-615-C-1400007. Kuper, H., Saran, A., White, H., (n.d.). Rapid evidence assessment (REA) of what works to improve educational outcomes for people with disabilities in low- and middle-income countries. London: International Centre for Evidence in Disability, London School of Hygiene; and Campbell Collaboration. https://assets.publishing.service.gov.uk/ government/uploads/system/uploads/attachment_data/file/738206/Education_ Rapid_Review_full_report.pdf. Ministry of Education [Kenya], 2018a. Sector policy for learners and trainees with disabilities. Ministry of Education,. Republic of Kenya, Nairobi. http://planipolis.iiep. unesco.org/sites/planipolis/files/ressources/kenya_sector_policy_learners_trainees_ disabilities.pdf. Ministry of Education, Science and Technology, (MoEST), 2018b. Republic of Tanzania]. Deaf students’ Performance in Secondary School Examination: Rapid Assessment for the Ministry of Education, Science, and Technology. MoEST, Dodoma. National Center for Children’s Vision and Eye Health, 2016. Children’s Vision and Eye

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