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      Multidisciplinary digital competencies of pre-service vocational teachers

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          Abstract

          Developments of Industry 4.0 require a set of multidisciplinary digital competencies for future vocational teachers, consisting of specific knowledge, motivational aspects, cognitive abilities and skills to fulfill the demands of digitally interconnected work situations. The competence model that is adapted from future work scenarios of vocational apprentices in Industry 4.0 includes attitudes towards digitization and handling of digital devices, information literacy, application of digital security standards, virtual collaboration, digital problem solving as well as a demonstration of reflective judgment of one’s actions in an interconnected and digital environment. Structural equation modeling was used to assess N = 205 pre-service vocational teachers between 18 and 35 years of age. The findings indicate the relationship of the proposed dimensions, measured through external- and self-assessments validate the proposed structure of the multidisciplinary digital competencies. However, attitude towards digitization can predict the self-efficacy of the relevant Multidisciplinary Digital Competencies but not the actual achievement in an external assessed scenario. Nevertheless, this study confirms that self-assessed multidisciplinary digital competencies can predict achievement in an external and qualitative-assessed competence test. Fit indices show an acceptable model conception, the reliability and construct validity of the model were confirmed. Findings suggest that the attitude towards digitization and the application of digital security standards are important, whereas the ability to solve digital problems seems to have a weak relation to the general multidisciplinary digital competencies of pre-service vocational teachers.

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          Most cited references99

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          Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

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            Three approaches to qualitative content analysis.

            Content analysis is a widely used qualitative research technique. Rather than being a single method, current applications of content analysis show three distinct approaches: conventional, directed, or summative. All three approaches are used to interpret meaning from the content of text data and, hence, adhere to the naturalistic paradigm. The major differences among the approaches are coding schemes, origins of codes, and threats to trustworthiness. In conventional content analysis, coding categories are derived directly from the text data. With a directed approach, analysis starts with a theory or relevant research findings as guidance for initial codes. A summative content analysis involves counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context. The authors delineate analytic procedures specific to each approach and techniques addressing trustworthiness with hypothetical examples drawn from the area of end-of-life care.
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              Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification.

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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Empirical Research in Vocational Education and Training
                Empirical Res Voc Ed Train
                Springer Science and Business Media LLC
                1877-6345
                December 2021
                February 22 2021
                December 2021
                : 13
                : 1
                Article
                10.1186/s40461-021-00112-4
                b6ae9235-bd9a-4d97-b1bb-9dfb8d6a1c3d
                © 2021

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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