06 SES 13 A, Software, Play & Data
Although the process of equipping schools with information technologies has moved at a slow pace in Germany, the national digital pact and the COVID-19 pandemic has added momentum to the development. The developments are framed by the results of the International Computer and Information Literacy Study (ICILS) 2018 (Eickelmann et al. 2019), that show a medium level achievement for German students. And even though German schools are still lacking the necessary infrastructure, there is a growing use of digital instruments in the classrooms (Lorenz et al. 2017) during distance learning. It goes along with a hope for an increased individualization but also the expected increase in students’ motivation, owing to the fact that the students can work on subject matters by themselves instead of discussing them in public in the classroom (Rabenstein et al. 2018).
The contribution is focused on analyses from lessons with digital instruments. The lessons – recorded at four schools in four different German cities – provide a basis for the analyses. Within these lessons, students independently work on a subject using a digital learning software programme. The data provided by the software to lead and guide the learning process of the students reduce the interaction in the classroom and focus it to an exchange of reactions between the student and the digital machine. These protocols will be aligned with interviews taken with teachers during the COVID-19 pandemic.
International critical data studies already reveal convincingly for learning software producers a multiplicity of problematic consequences (Friesen 2011; Manolev et al. 2019; Selwyn 2011; Williamson 2017; 2019). Most of the research criticizes the distribution of data that lead to a control environment in schools. Under these conditions the school is no longer in control of the teachers but under the control of the companies. They do dramatically challenge schools by their need for data.
The critical data studies miss at a certain extent the pedagogically important changes that learning software and their distribution of data force. By focusing on the learning software itself as well as on the empirically recorded interaction in the classroom, we can show how educational interaction change our way of thinking of education and the didactical arranged process (Jornitz/Leser 2018; Jornitz/Klinge 2021; Rabenstein et al. 2018). In line with ongoing research that concentrate on the question of the “subjectisation” or of the students’ development as a socially responsible and mature person (Macgilchrist 2018) our research offers a deeper understanding of how the pedagogical relationship and its interaction is shaped and transformed by the use of learning software and the use of the digital produced data.
The students’ reflections and reactions to technically generated responses and data reveal that on the one hand the assistance given by the software fails to address the students’ problems while on the other hand, the students are tied to the software and learn how to get the best results by minimizing the understanding of the topic itself. The analyses can serve to demonstrate that a technical constraint of reflection with data has not only replaced or minimized social interaction in the classroom, but left the student with less confidence in its own knowledge of the subject matter. It’s the digital software and its data that define what and how the topic is understood, but less the student or the teacher. The analysis can therefore contribute to the discussion on distance learning during the COVID-19 pandemic.
Methodologically, the qualitative analysis has its roots in a twofold methodology, because it is focused on two sorts of materials: first, on the learning software itself and second, on the recorded classroom interaction. Both are interconnected in the empirically recorded practice. While we can observe the feedback from the digital learning system and the data it produces, they initiate a physically observable and a mentalist response on the students’ side. It is their comments in front of the computer that give us an idea of the cognitive procedures of the students. It is their handling of automated data feedback to a given response that enables deductions regarding the students’ learning process But to analyse both, we have to split these interconnected spheres in the research process. Therefore we use the qualitative approach of the research group of Decuypere to analyse in the first step the learning software itself (Decuypere 2019). By splitting up the software and scanning the screen in segments of clicks, we get the chance to understand the underlying structure of the tool itself. Theses analyses focus on the written part as much as on the visual one. Both are important aspects that shape the perception of the student user. Learning software has its own way of “language” by highlighting certain buttons or results given to the users. These aspects are not changeable by – for example – a teacher, therefore this inner structure has an impact on classroom interactions. In the second step, we focus on the recorded and transcribed classroom interaction protocols. These analyses can be assigned to a reconstruction logic procedure, developed in the structural theoretical approach by Ulrich Oevermann (1987). This structuralist approach is invented by Oevermann and well-known and in German-speaking countries well-established. This “objective or structural hermeneutics” offers the possibility to reveal the inherent structures of social interaction. By focusing on educational as much as on didactical issues in classroom interaction with regard to the data, we can analyse in depth the students’ actual comments and processing. These comments and reactions can in a third step be linked to the logic of the software. How data prescribes the action of the student is then empirically accessible.
The analyses of classroom interaction in four different schools in Germany and the analyses of the used learning software help to clarify how data become an important issue in pedagogical settings. By our research, we can differ between the prescribed picture of the student and his or her learning path on the one hand and the empirically observable adjustment of the students by following or skipping this path on the other hand. We can also enrich these data with interviews of teachers during the COVID-19 pandemic and their experiences with distance learning. The ongoing characteristic of data as neutral and an independent voice between the teacher and the student can be challenged by our research. The data produced by the learning software should give immediately feedback to the responses of the students as well as “react” and distribute the next task or text with regard to the students’ learning level. But, what we see, by looking closely to the software and the interaction of the students with the software is, that the so-called “adaptive” software “acts” in the reverse direction. It is the student who must understand the way the machine and its software is organized to follow its prescribed way of learning. By becoming familiar with the structure of the learning tool, he or she loses a close connection to the subject matter that should be treated and understand during school lesson. This kind of learning software and its data become a leading role under which teacher and students change their role and understanding. How to gain back power and set the technological tool in its pedagogical and social acceptable range, is an open and still unsolved question that is worth to be discussed at a European level.
Allert, H., Asmussen, M., Richter, C. (2017): Formen von Subjektivierung und Unbestimmtheit im Umgang mit datengetriebenen Lerntechnologien – eine praxistheoretische Position. Zeitschrift für Erziehungswissenschaft, 21. Jg., Heft 1, p. 142-158. Decuypere, M. (2019): Researching educational apps: ecologies, technologies, subjectivities and learning regimes. Learning, Media and Technology, 44, 4, p. 414-429. Eickelmann, B., Bos, W., Gerick, J., Goldhammer, F., Schaumburg, H., Schwippert, K., Senkbeil, M., Vahrenhold, J. (eds.) (2019): ICILS 2018 #Deutschland. Computer- und informationsbezogene Kompetenzen von Schülerinnen und Schülern im zweiten internationalen Vergleich und Kompetenzen im Bereich Computational Thinking. Münster, New York: Waxmann. Friesen, N. (2011): The place of the classroom and the space of the screen. Relational pedagogy and internet technology. New York: Lang. Jornitz, S., Leser, C. (2018): Mit Antolin punkten oder: Wie sich mit dem Leseförderprogramm der Bock zum Gärtner macht. Pädagogische Korrespondenz, Heft 57, p. 55-7. Jornitz, Sieglinde / Klinge, Denise (im Druck - 2021): „Bildung” as a forgotten aspect of algorithmic technologies. In: Parreira do Amaral, Marcelo/ Thompson, Christiane (2021): Geopolitical Transformations in Higher Education. Imagining, Fabricating and Contesting Innovation. Palgrave. Lorenz, R., Bos, W., Endberg, M., Eickelmann, B., Grafe, S., Vahrenhold, J. (eds.) (2017): Schule digital – der Länderindikator 2017. Schulische Medienbildung in der Sekundarstufe I mit besonderem Fokus auf MINT-Fächer im Bundesländervergleich und Trends von 2015 bis 2017. Münster, New York: Waxmann. Macgilchrist, F. (2018). The “digital subject” of 21st century education: On datafication, educational technology and subject formation. In: Peter Pericles Trifonas & Susan Jagger (Eds.), Routledge Handbook of Cultural Studies in Education. New York: Routledge. Manolev, J., Sullivan, A., & Slee, R. (2019). The datafication of discipline: ClassDojo, surveillance and a performative classroom culture. Learning, Media and Technology, 44, 1, p. 36-51. Oevermann, U. (1987): Structures of Meaning and Obejctive Hermeneutics. In: meja, V., Misgeld, D., Stehr, N. (eds.): Modern German Sociology. New York: CUP, p. 436-447. Rabenstein, K., Idel, T-S., Reh, S., Ricken, N. (2018): Funktion und Bedeutung der Schulklasse im individualisierten Unterricht. Beobachtungen zu Selbst-Anderen-Verhältnissen aus ethnographischen Fallstudien. Zeitschrift für Pädagogik, 64, 2, p. 179-197. Selwyn, N. (2011): Schools and schooling in the digital age. A critical analysis. 1. ed. London: Routledge (Foundations and futures of education). Williamson, B. (2017). Big Data in Education. The digital future of learning, policy and practice. Los Angeles: Sage. Williamson, B. (2019). Killer Apps for the Classroom? Developing Critical Perspectives on ClassDojo and the ‘Ed-tech’ Industry. Journal of Professional Learning, 2, 1-10.
00. Central Events (Keynotes, EERA-Panel, EERJ Round Table, Invited Sessions)
Network 1. Continuing Professional Development: Learning for Individuals, Leaders, and Organisations
Network 2. Vocational Education and Training (VETNET)
Network 3. Curriculum Innovation
Network 4. Inclusive Education
Network 5. Children and Youth at Risk and Urban Education
Network 6. Open Learning: Media, Environments and Cultures
Network 7. Social Justice and Intercultural Education
Network 8. Research on Health Education
Network 9. Assessment, Evaluation, Testing and Measurement
Network 10. Teacher Education Research
Network 11. Educational Effectiveness and Quality Assurance
Network 12. LISnet - Library and Information Science Network
Network 13. Philosophy of Education
Network 14. Communities, Families and Schooling in Educational Research
Network 15. Research Partnerships in Education
Network 16. ICT in Education and Training
Network 17. Histories of Education
Network 18. Research in Sport Pedagogy
Network 19. Ethnography
Network 20. Research in Innovative Intercultural Learning Environments
Network 22. Research in Higher Education
Network 23. Policy Studies and Politics of Education
Network 24. Mathematics Education Research
Network 25. Research on Children's Rights in Education
Network 26. Educational Leadership
Network 27. Didactics – Learning and Teaching
The programme is updated regularly (each day in the morning)
- Search for keywords and phrases in "Text Search"
- Restrict in which part of the abstracts to search in "Where to search"
- Search for authors and in the respective field.
- For planning your conference attendance you may want to use the conference app, which will be issued some weeks before the conference
- If you are a session chair, best look up your chairing duties in the conference system (Conftool) or the app.