Publikation: DiKoLAN-SK : Development of a measurement instrument for academic self-concept of digitalization-related competencies in science education
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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 N = 286 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.
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THOMS, Lars-Jochen, Till BRUCKERMANN, Christoph THYSSEN, Monique MEIER, Lena VON KOTZEBUE, Julia ARNOLD, Nadja BELOVA, Simon Z. LAHME, Benedikt HEUCKMANN, Stefanie LENZER, Bernadette SCHORN, Marie HORNBERGER, Alexander FINGER, Nicolai TER HORST, Stefanie PETER, Erik KREMSER, Steffen CIPRINA, Johannes HUWER, Sebastian BECKER-GENSCHOW, 2026. DiKoLAN-SK : Development of a measurement instrument for academic self-concept of digitalization-related competencies in science education. In: Computers and Education Open. Elsevier. 2026, 10, S. 100338-100338. ISSN 2666-5573. eISSN 2666-5573. Verfügbar unter: doi: 10.1016/j.caeo.2026.100338BibTex
@article{Thoms2026-06DiKoL-76276,
title={DiKoLAN-SK : Development of a measurement instrument for academic self-concept of digitalization-related competencies in science education},
year={2026},
doi={10.1016/j.caeo.2026.100338},
volume={10},
issn={2666-5573},
journal={Computers and Education Open},
pages={100338--100338},
author={Thoms, Lars-Jochen and Bruckermann, Till and Thyssen, Christoph and Meier, Monique and von Kotzebue, Lena and Arnold, Julia and Belova, Nadja and Lahme, Simon Z. and Heuckmann, Benedikt and Lenzer, Stefanie and Schorn, Bernadette and Hornberger, Marie and Finger, Alexander and ter Horst, Nicolai and Peter, Stefanie and Kremser, Erik and Ciprina, Steffen and Huwer, Johannes and Becker-Genschow, Sebastian},
note={Article Number: 100338}
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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.
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