04 SES 14 A, Impacts of Labelling Students with Special Educational Needs – Theory and Empirical Evidence
The allocation of special educational needs is internationally a controversial issue (e.g. Algraigray & Boyle, 2017; Norwich, 2014). Many authors argue that a diagnosis is important to provide students with additional resources and individualized support (e.g. Norwich, 2014). Critics of labels emphasize the risks associated with the deficit-oriented, classifying and sometimes stigmatizing label (Algraigray & Boyle, 2017; Boyle, 2014). Many international studies that investigate the competence development of students with special educational needs do not find any positive effect (e.g. Demspey & Valentine, 2017; Dempsey, Valentine & Colyvas, 2016; Morgan, Frisco, Farkas & Hibel, 2010). In the case of diagnosing special educational needs in the area of learning (SEN-L), it has to be considered that this category is not scientifically proven (e.g. Algraigray & Boyle, 2017), which results in disparities of identifying students with SEN-L (e.g. Goldan & Kemper, 2019). Thus, it is not surprising that a considerable overlap of competence distributions of students with and without SEN-L can be found. By using the variation of the allocation of SEN-L categories, this study investigates the effect of the label 'special educational needs in the area of learning' on mathematical competence development. Based on previous results, it is assumed that the label has no or a negative effect on competence development. Using data from starting cohort 2 (Kindergarten, n = 9337) of the National Educational Panel (NEPS, Blossfeld, Roßbach & von Maurice, 2011), the competence development in the domain mathematics (Neumann et al., 2013) of students who have been diagnosed with SEN-L will be examined in comparison to a matched sample with regard to relevant background variables (basic cognitive skills, vocabulary, HISEI, federal state etc.). A total of 164 statistical twins in regular primary schools were identified. By means of a latent growth curve model, the development of competence in mathematics during primary school was modelled using three measuring points. Preliminary results show that the presence of a diagnosis had no effect on the competence growth of the students who had received an SEN-L label. It is discussed to what extent the label – along with e.g. individual support – may compensate for potential negative effects on competence development. The study is limited by the fact that potentially unobserved variance between the two groups could have an effect on competence development.
Algraigray, H. & Boyle, C. (2017). The SEN label and its effect on special education. Educational and Child Psychology. 34(4), 70-79. Boyle, C. (2014). Labelling in special education: Where do the benefits lie? In A. Holliman (Ed.), The Routledge International Companion to Educational Psychology (pp.213–221). London: Routledge. Demspey, I. & Valentine, M. (2017). Special Education Outcomes and Young Australian School Students: A Propensity Score Analysis Replication. Australasian Journal of Special Education, 41(1), 68-86. Dempsey, I., Valentine, M., & Colyvas, K. (2016). The effects of special education support on young Australian school students. International Journal of Disability, Development and Education, 63, 271–292. doi:10.1080/1034912X.2015.1091066 Goldan, J. & Kemper, T. (2019). Prävalenz von Schüler/innen mit Förderschwerpunkt Lernen – Regionale und jahrgangsstufenspezifische Disparitäten. Eine exemplarische Analyse für das Land Nordrhein-Westfalen anhand von Daten der amtlichen Schulstatistik. Sonderpädagogische Förderung heute, 64(3), 302-317. Morgan, P. L., Frisco, M. L., Farkas, G., & Hibel, J. (2010). A propensity score matching analysis of the effects of special education services. The Journal of Special Education, 43, 236–254. doi:10.1177/0022466908323007 Norwich, B. (2014). Categories of Special educational needs. In L. Florian (Ed.), The SAGE handbook of special education (pp. 55-71). Los Angeles: SAGE.
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