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Small number enumeration processes of deaf or hard-of-hearing students: A study using eye tracking and artificial intelligence
Department of Special Education and Rehabilitation, Faculty of Human Sciences, University of Cologne, Cologne, Germany.
Department of Special Education and Rehabilitation, Faculty of Human Sciences, University of Cologne, Cologne, Germany.
Department of Special Education and Rehabilitation, Faculty of Human Sciences, University of Cologne, Cologne, Germany.
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-0804-8637
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2022 (English)In: Frontiers in Psychology, E-ISSN 1664-1078, Vol. 13, article id 909775Article in journal (Refereed) Published
Abstract [en]

Students who are deaf or hard-of-hearing (DHH) often show significant difficulties in learning mathematics. Previous studies have reported that students who are DHH lag several years behind in their mathematical development compared to hearing students. As possible reasons, limited learning opportunities due to a lesser incidental exposure to numerical ideas, delays in language and speech development, and further idiosyncratic difficulties of students who are DHH are discussed; however, early mathematical skills and their role in mathematical difficulties of students who are DHH are not explored sufficiently. In this study, we investigate whether students who are DHH differ from hearing students in their ability to enumerate small sets (1-9)-an ability that is associated with mathematical difficulties and their emergence. Based on a study with N = 63 who are DHH and N = 164 hearing students from third to fifth grade attempting 36 tasks, we used eye tracking, the recording of students' eye movements, to qualitatively investigate student enumeration processes. To reduce the effort of qualitative analysis of around 8,000 student enumeration processes (227 students x 36 tasks), we used Artificial Intelligence, in particular, a clustering algorithm, to identify student enumeration processes from the heatmaps of student gaze distributions. Based on the clustering, we found that gaze distributions of students who are DHH and students with normal hearing differed significantly on a group level, indicating differences in enumeration processes, with students who are DHH using advantageous processes (e.g., enumeration "at a glance") more often than hearing students. The results indicate that students who are DHH do not lag behind in small number enumeration as compared to hearing students but, rather, appear to perform better than their hearing peers in small number enumeration processes, as well as when conceptual knowledge about the part-whole relationship is involved. Our study suggests that the mathematical difficulties of students who are DHH are not related to difficulties in the small number enumeration, which offers interesting perspectives for further research.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2022. Vol. 13, article id 909775
Keywords [en]
Artificial Intelligence, deaf or hard-of-hearing students, eye tracking, mathematical difficulties, mathematics education, small number enumeration
National Category
Pedagogy Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-101153DOI: 10.3389/fpsyg.2022.909775ISI: 000849943200001PubMedID: 36072043Scopus ID: 2-s2.0-85137910913OAI: oai:DiVA.org:oru-101153DiVA, id: diva2:1694158
Available from: 2022-09-08 Created: 2022-09-08 Last updated: 2024-01-03Bibliographically approved

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Schaffernicht, ErikLilienthal, Achim J.

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