06 SES 14 A, Data(fication) in Schools: Perspectives, Practices, and Research Approaches
The usage of data and algorithms in schools varies from the use for school development as well as the tracking of activities and the learning progress of students through to the generation of comparative statistics (Jarke & Breiter 2019). However, where data and algorithms are used, inequality and discrimination is / can be systematically produced: Data are collected, weighted, hierarchized, and sorted in a context- and subject-dependent manner in order to gain insights (Allert 2020). For students, this means that in terms of an extended understanding of digital divide, not only do issues of access or degree of profit lead to multidimensional inequality (i.a. Hargittai & Hsieh 2013), but also data-based discrimination in terms of an algorithmic bias increases in relevance (i.a. Marr & Zillien 2018). Consequently, if e.g. learning behavior of students is affected by software and algorithms, the risk of inequalities in the classroom rises. This not only places demand on teachers, but on the entire educational staff in schools. Hence, it has to be investigated on what consequences data(based) practices of professionals have for students as well as for everyday (school)life. The well-established coherence between social background and inequalities in terms of educational success (i.a. Mostafa & Schwabe 2018) is joined by the use of data-based systems, which is a deepening, new component. But although data contributes in reproducing inequalities, data-based insights can in turn be used to focus individual learning and teaching: From a didactic perspective, internal differentiation is a response to different learning needs of students, by creating individual learning opportunities (Klafki & Stöker 1993). The increased use of e.g. learning-management-systems, generates new perspectives to meet the method of internal differentiation in the classroom (McCoy and Mathur 2017). Nevertheless, this requires accurate diagnoses of students‘ learning states, which makes the competence to correctly interpret, sort and use produced data inevitable (Letzel, Otto & Schneider 2019). Yet, the majority of educators feels unprepared to deal with data as well as algorithms. Therefore, the aim must be to develop data literacy education for all pedagogical professionals within schools and to address the described ambivalence in order to use data more within the meaning of "data for good". What are the connections between data practices of teachers as well as school social workers and data and algorithms in schools? To minimize inequalities, changing didactics, in which data practices should be considered, can be a first effective instrument.
Allert, H. (2020). Algorithmen und Ungleichheit. merz Zeitschrift für Medienpädagogik, Heft "Medien und Soziale Ungleichheit" 2020/03, 26-33. Hargiattai E.; Hsieh Y. P. (2013): Digital Inequality. In: William H. Dutton (Ed.), The Oxford Handbook of Internet Studies, Oxford: Oxford University Press. Jarke, J.; Breiter, A. (2019). Editorial: the datafication of education. Learning, Media and Technology, 44(1), 1–6. https://doi.org/10.1080/17439884.2019.1573833. Klafki, W., Stöcker H. (1993). Sechste Studie. Innere Differenzierung des Unterrichts. In: Klafki, W.: Neue Studien zur Bildungstheorie und Didaktik. Zeitgemäße Allgemeinbildung und kritisch konstruktive Didaktik. Basel: Beltz. Letzel, V., Otto, J. & Schneider, C. (2019). „Ich hoffe, dass ich treffsicher bin.“ Eine qualitative Studie zu Diagnosekriterien und Differenzierungsmaßnahmen der Lehrkräfte. In H. Knauder, C.-M. Reisinger (Hrsg.), Individuelle Förderung im Unterricht. Empirische Befunde und Hinweise für die Praxis. Waxmann, 69-84. Marr, M.; Zillien, N. (2018): Digitale Spaltung. In. Schweiger, W.; Beck, K. (Hrsg.): Handbuch Online-Kommunikation. Wiesbaden: Springer VS, S. 1-24. McCoy, M., Mathur S. R. (2017): Differentiation in the Digital-Based Classroom: A Universal Design Approach for Inclusive Settings in Middle Schools. In: Journal of Education and Development 1(1), 1-11. Mostafa, T.; Schwabe, M. (2018): Programme for International Student Assessment (PISA). PISA 2018 Ergebnisse. Ländernotiz. https://www.oecd.org/pisa/publications/PISA2018_CN_DEU_German.pdf
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