DiKoLAN-SK – Development of a measurement instrument for academic self-concept of digitalization-related competencies in science education

Thoms, Lars-Jochen; Bruckermann, Till; Thyssen, Christoph; Meier, Monique; von Kotzebue, Lena; Arnold, Julia; Belova, Nadja; Lahme, Simon Z.; Heuckmann, Benedikt; Lenzer, Stefanie; Schorn, Bernadette; Hornberger, Marie; Finger, Alexander; ter Horst, Nicolai; Peter, Stefanie; Kremser, Erik; Ciprina, Steffen; Huwer, Johannes; Becker-Genschow, Sebastian

Zusammenfassung

Digital technologies can support knowledge acquisition and transfer, documentation of learning outcomes, and self-regulated and collaborative learning. In science education, they are used to scaffold experimentation, to collect and process measurements, and to support learning with simulations and modeling. To use digital technologies in science teaching in ways that promote learning, teachers require digitalization-related competencies and a well-developed academic self-concept regarding subject-specific digitalization-related competencies. However, existing self-report measures are typically domain-general — not aligned with science-specific frameworks such as DiKoLAN (Digital Competencies for Teaching in Science Education; German: Digitale Kompetenzen für das Lehramt in den Naturwissenschaften) — or focus on related but conceptually distinct constructs such as task- and situation-specific self-efficacy expectations. To address this gap, we define DiKoLAN-SK as a domain-specific academic self-concept regarding digitalization-related competencies for teaching science and develop and validate its corresponding measure, the DiKoLAN-SK questionnaire. The DiKoLAN-SK questionnaire enables domain-specific assessment of pre-service science teachers’ DiKoLAN-SK aligned with the DiKoLAN framework, thereby supporting diagnosis and evaluation in science teacher education. We tested comprehensibility and provided evidence of validity and reliability in a sample of pre-service teachers from Germany and Switzerland. Confirmatory factor analyses indicate that responses can reliably distinguish the DiKoLAN competency areas and competency levels as well as the four technology-related knowledge facets of the TPACK framework (Technological Pedagogical Content Knowledge). Known-groups comparisons (e.g., target school level, number of science subjects) provide additional validity evidence.