Factors Predicting Recall of Mathematics Terms by Deaf Students: Implications for Teaching

Lang, Harry G.; Pagliaro, Claudia M.

doi:10.1093/deafed/enm021

Cited by 24 publications

(15 citation statements)

References 27 publications

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“…Deaf children's poor performance in WM measures is a cause for concern because the WM-CE component plays an important role in their learning. It is a predictor of their spoken language processing [6], reading achievement [8,11] as well as reading comprehension [12], mathematical achievement [13] and geometry learning [14]. Thus it would be very important to attempt to improve deaf children's WM.…”

Section: Deaf Children's Performance In Wm Tasksmentioning

confidence: 99%

Improving Deaf Children’s Working Memory through Training

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Evans

et al. 2014

Int. J. Speech Lang. Pathol. Audiol.

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The aim of this study was to design and to assess the effectiveness of a working memory (WM) intervention for deaf children. A review of factors that explain deaf students' poor results in WM tasks was used to identify deaf participants' strengths and difficulties in WM tasks. This review formed the basis for the design of the WM training program assessed in the study. Participants were 77 children in a comparison group and 73 in an intervention group, with the mean age of 8y5m at the start of the program. The participants' severity of hearing loss was at least moderate; approximately 68% of the children had one cochlear implant and the rest had hearing aids only. The design was quasi-experimental: teachers rather than children were assigned either to the comparison or the intervention group. The children were pre-and post-tested in three WM tasks; the regression factor score was used as the measure of their WM. The intervention was implemented by the teachers and comprised two types of games: teacher led games, during which the teachers taught the children rehearsal strategies that combined linguistic and visual-spatial encoding, and web-based games, which the children played without a tutor and involved biased competition with the aim of developing attention control. An analysis of covariance (controlling for pre-test WM scores, age, cognitive ability and pre-to post-test interval) showed that the intervention group differed significantly from the comparison group at post-test; Cohen's d effect size was 0.78. We conclude that it is possible to improve deaf children's performance in WM measures by training that targets their attention problems and teaches them rehearsal strategies.

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Section: Deaf Children's Performance In Wm Tasksmentioning

confidence: 99%

Improving Deaf Children’s Working Memory through Training

Nuñes

Barros

Evans

et al. 2014

Int. J. Speech Lang. Pathol. Audiol.

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“…For background about deaf and hard-of-hearing children and the learning of mathematics, see [Kritzer 2009;Lang and Pagliaro 2007;Nunes and Moreno 2002]. A reference on the education of deaf and hard-of-hearing children in general is [Marschark et al 2001].…”

Section: Further Readingmentioning

confidence: 99%

Undergraduate research in mathematics with deaf and hard-of-hearing students: four perspectives

Adler

Jacob

Kurz

et al. 2014

Involve

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(Communicated by Darren A. Narayan)Involving more deaf and hard-of-hearing students in undergraduate research is a step toward getting more such students into STEM (science, technology, engineering and mathematics) careers. Since evidence exists that undergraduate research improves retention, especially for some underrepresented groups that have low retention rates -as, for example, deaf and hard-of-hearing STEM majors -it is a particularly pertinent step to keep interested students in these career paths. Nunes and Moreno have suggested that deaf and hard-of-hearing students have the potential to pursue mathematics, but lack the resources. By involving more such individuals in undergraduate mathematics research, we can improve their success rates and promote mathematics research within the Deaf community.Here we describe our experiences working both with and as deaf or hard-ofhearing students in research, as well as advice that stems from these experiences. Each of the authors is a faculty member at the National Technical Institute for the Deaf, a college of Rochester Institute of Technology, and holds a PhD in a scientific field. Three of the authors are deaf, and one (Jacob) is hearing. While this paper describes the experiences and opinions of individuals, and is not meant to be an all-inclusive handbook on how to do research with any deaf or hard-of-hearing student, we hope that it will be a helpful resource.

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“…Several authors argue that the lack of a common sign language lexicon for scientific or technical terms may impair the engagement of deaf students in the learning process .…”

Section: Introductionmentioning

confidence: 99%

A computational approach to support the creation of terminological neologisms in sign languages

Souza¹,

Pádua²,

Lima³

et al. 2018

Comp Applic In Engineering

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This work presents a novel approach to support the creation of terminological neologisms (technical signs) for Science, Technology, Engineering, and Mathematics (STEM) dictionaries in sign languages. We propose the use of computational methods to assist the creation of terminological neologisms that will constitute new STEM dictionaries. More specifically, we propose the use of computer vision algorithms for shape extraction and description from the visual representation (illustration) of a signless technical concept, as well as natural language processing methods for determining the stem of its corresponding oral term. By comparing those pieces of information of the signless technical concept with the corresponding ones of general‐purpose signs in a repository, our approach creates a meaningful new sign from the existing ones with similar features. As far as we know, this is the first computer‐aided proposal to create new signs and deal with the problem of technical vocabulary absence in sign languages, making the process of sign creation more agile and scalable. To validate the created technical signs, an evaluation committee composed by fluent signers considers aspects such as, the phonetic, phonological, and morphological structures of the sign language. Experiments were performed with technical concepts of the architectural drawing area, for which new signs in the Brazilian Sign Language were created. The results are promising with 60% of the new signs being rated on a five‐point scale with a similar satisfaction level of signs of a reference dataset.

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Factors Predicting Recall of Mathematics Terms by Deaf Students: Implications for Teaching

Cited by 24 publications

References 27 publications

Improving Deaf Children’s Working Memory through Training

Improving Deaf Children’s Working Memory through Training

Undergraduate research in mathematics with deaf and hard-of-hearing students: four perspectives

A computational approach to support the creation of terminological neologisms in sign languages

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