TY - JOUR
T1 - DiKoLAN-SK – Development of a measurement instrument for academic self-concept of digitalization-related competencies in science education
AU - Thoms, Lars-Jochen
AU - Bruckermann, Till
AU - Thyssen, Christoph
AU - Meier, Monique
AU - von Kotzebue, Lena
AU - Arnold, Julia
AU - Belova, Nadja
AU - Lahme, Simon Z.
AU - Heuckmann, Benedikt
AU - Lenzer, Stefanie
AU - Schorn, Bernadette
AU - Hornberger, Marie
AU - Finger, Alexander
AU - ter Horst, Nicolai
AU - Peter, Stefanie
AU - Kremser, Erik
AU - Ciprina, Steffen
AU - Huwer, Johannes
AU - Becker-Genschow, Sebastian
N1 - Publisher Copyright:
© 2026 The Authors
PY - 2026/2/23
Y1 - 2026/2/23
N2 - 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 teachereducation. 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.
AB - 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 teachereducation. 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.
KW - Academic self-concept
KW - Digital competencies
KW - Digitalization-related competencies
KW - DiKoLAN
KW - Measurement instrument
KW - Pre-service teachers
KW - Science education
KW - TPACK
UR - http://www.scopus.com/inward/record.url?scp=105030598864&partnerID=8YFLogxK
U2 - 10.1016/j.caeo.2026.100338
DO - 10.1016/j.caeo.2026.100338
M3 - Article
SN - 2666-5573
VL - 10
SP - 1
EP - 32
JO - Computers and Education Open
JF - Computers and Education Open
M1 - 100338
ER -