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      Erkennen von Rechenschwäche durch LehrerInnen und Testungen im Klassenverband

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          Abstract

          Zusammenfassung. In den letzten Jahren wächst in den Schulen stetig das Bewusstsein für Rechenschwäche. Um einem Kind schulische Maßnahmen wie Förderunterricht zukommen zu lassen, ist dabei nach wie vor das LeherInnenurteil von zentraler Bedeutung. Testbasierte Diagnostik kann jedoch helfen, die Förderung in der Schule zu optimieren, indem konkrete Problembereiche der jeweiligen Kinder aufgedeckt werden. Zu diesem Zweck kooperiert unsere Arbeitsgruppe seit mehreren Jahren mit einer Grundschule, in der die Kinder nicht nur von den Lehrkräften für die Förderung ausgewählt werden, sondern auch eine Klassentestung numerischer Fähigkeiten durch PsychologInnen unserer Arbeitsgruppe stattfindet. Kinder, die in dieser Testung die niedrigsten Werte erreichen, werden dementsprechend von uns für die Förderung vorgeschlagen. Die Qualität dieser gemeinsamen Einschätzung haben wir nun auf die Probe gestellt, indem wir die Kinder, die von den LehrerInnen oder unserer Testung als förderbedürftig eingeschätzt wurden, einer umfangreichen Rechenschwächediagnostik unterzogen haben. Wir wollten wissen, ob es uns gemeinsam mit den Lehrkräften gelingt, Kinder mit Rechenschwäche zuverlässig zu erkennen. Generell zeigte sich eine gute Übereinstimmung zwischen der LehrerInneneinschätzung und unseren Testergebnissen. Der vorliegende Beitrag soll als ein erster Erfahrungsbericht beschreiben, wie eine solche gemeinsame Einschätzung durch LehrerInnen und PsychologInnen zur besseren Erkennung rechenschwacher Grundschüler beitragen kann.

          Detecting Mathematical Learning Difficulties through Teacher and Classroom Test Assessment

          Abstract. Background: In recent years, an increasing number of schools in German-speaking countries have started to accept dyscalculia as a learning disability. Fortunately, in order for schools to be allowed to assign children to remedial education, they only have to rely on teachers' judgment. Therefore, it is not necessary for parents to acquire an extensive and often expensive assessment from a child psychiatrist to make sure their child receives remediation.

          However, when done right, a psychological diagnostic assessment can help optimize scholastic remediation by revealing each child's specific problem areas. When teachers judge children's scholastic skills, they often do not consider that dyscalculia affects mostly basic numerical competencies. They might not be aware of where children's problems with mathematics start and might therefore benefit from assistance when determining what kind of support a certain child needs. Remediation can only be really effective if it is individually tailored to each child's problem areas. A diagnostic assessment can help identify these problem areas. Also, an early diagnosis is important so that remediation can also start at an early age and children can more quickly catch up to their peers.

          Aims: For this reason, our research team has been cooperating with a primary school for a couple of years now. Our part in this cooperation is to help select children for remedial classes by conducting screenings of children’s numerical competencies. Those children with the lowest scores in the screening are then suggested to participate in remedial classes. In the current study, the quality of this cooperation was put to the test. Children who were identified by either teachers or the screening as having difficulties in mathematics were individually tested with comprehensive tests for identifying dyscalculia. We were interested in whether together we managed to reliably detect children with mathematical learning difficulties. Also, the results of the individual tests were used to provide teachers with a more comprehensive profile of each child's mathematical and numerical abilities. This article relays our experience in working with schools and teachers to identify children with mathematical learning difficulties and refer them to individualized remedial education.

          Methods: 222 children from grades 1 to 4 participated in the study. Teachers classified children into three categories: i) No mathematical difficulties, ii) might need remediation, and iii) definitely needs remediation. The screening results were classified in a similar way to allow for integration of the results. Children with total test scores below the 10 th percentile or subtest scores below the 5 th percentile were classified as definitely in need of remediation, and children with total test scores below the 15 th percentile were classified as maybe in need of remediation. Children with test scores above the 15 th percentile were classified as having no mathematical difficulties.

          In the screening, we used both basic numerical tasks (such as number line estimation tasks or the ERT 0+, Lenart et al., 2014) as well as mathematical tasks such as addition, subtraction, multiplication, and complex number comparison (partially taken from the HRT 1 – 4, Haffner et al., 2005). From these tasks, we calculated a total score which we used for the classification. To control for general cognitive abilities, fluid intelligence (CFT 1-R, Weiss & Osterland, 2013) and short-term memory (self-developed tasks for visual and verbal STM) were measured.

          To test children for dyscalculia, we used the TEDI-MATH ( Kaufmann et al., 2009) in grades 1 and 2; and the RZD 2 – 6 ( Jacobs & Petermann, 2006) in grades 3 and 4. These tests were not only used to diagnose mathematical difficulties, but also to find out about children's problem areas so that remediation could be individually tailored to each child's needs.

          Results: In general, we found good levels of agreement between teachers' judgments and our screening results. This was tested by calculating weighted kappa coefficients ( Cohen's Kappa; Cohen, 1960). For all 222 children, we found a kappa of κ w = 0,43, reflecting a moderate agreement ( Landis & Koch, 1977). Teachers and psychologists classified 170 children (77 %) similarly. Separately by grade, we found a fair agreement (κ w = 0,18) in grade 1, and moderate agreements in grades 2 – 4 (grade 2: κ w = 0,53; grade 3: κ w = 0,49; grade 4: κ w = 0,54).

          Children who were identified as having difficulties in mathematics were tested for dyscalculia individually (with TEDI-MATH or RZD 2 – 6 according to their grade). As suggested by both tests, we interpreted test scores below the 10 th percentile as indicative of dyscalculia. Additionally, we classified children with test scores below the 25 th percentile as being at risk of developing dyscalculia. Out of 55 children we tested individually, 35 (63.6 %) were identified as dyscalculic; and another 10 (18.2 %) were identified as being at risk. This means that out of 55 children, 45 were in need of remediation.

          For the small subset of children who were individually tested, we calculated correlation coefficients between our screening measures and the dyscalculia test scores. In most grades, the calculation portion of the screening was significantly correlated with the dyscalculia tests. Additionally, fluid intelligence was correlated with the dyscalculia test scores in grades 1 and 4. In grade 4, we also observed a correlation of the dyscalculia test scores with number line estimation. We interpreted this finding as indicative of children using strategies such as proportion judgment in grade 4 to perform number line estimation, which rely heavily on their calculation skills.

          Discussion: Over the course of our cooperation, we learned a lot about the possibilities of working together with teachers and schools. Our screening helped identify children who had difficulties in mathematics, but we also learned that some of our measures should be adapted for future screenings. Both our results as well as teachers' assessment of children helped identify children and could be used to provide children with remedial education.

          While teachers know children very well and can therefore intuitively judge their skills very well, these two sources of information could be well integrated and were oftentimes in agreement. Out of all children who were tested individually, 81.8 % actually needed remediation, which hints at a high accuracy of the combined judgment of teachers and screening results. In the future, such screenings and forms of cooperation should be integrated more often into regular education, seeing as they only take one hour per classroom.

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          Most cited references27

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          The Measurement of Observer Agreement for Categorical Data

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            Prävalenz von Lernschwächen und Lernstörungen: Zur Bedeutung der Diagnosekriterien

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                Author and article information

                Journal
                lls
                Lernen und Lernstörungen
                Hogrefe AG, Bern
                2235-0977
                2235-0985
                2015
                : 4
                : 4
                : 269-285
                Affiliations
                [ 1 ]Leibniz-Institut für Wissensmedien, Tübingen
                [ 2 ]Fachbereich Psychologie, Eberhard Karls Universität, Tübingen
                Author notes
                Dr. Ursula Fischer, Leibniz-Institut für Wissensmedien, Schleichstrasse 6, 72076 Tübingen, Deutschland, E-Mail u.fischer@ 123456iwm-tuebingen.de
                Article
                lls_4_4_269
                10.1024/2235-0977/a000116
                fea23072-b507-4b93-96d5-d00ef583c79e
                Product
                Self URI (journal-page): https://econtent.hogrefe.com/loi/lls
                Categories
                Empirische Arbeit

                Pediatrics,Psychology,Neurosciences,Family & Child studies,Development studies,Clinical Psychology & Psychiatry
                Diagnostik,Förderung,Rechenschwäche,Forschung und Praxis,Mathematical learning difficulties,diagnostics,remediation,collaboration between research and practice

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