@article{Karner2021infor-52750,
  year={2021},
  doi={10.1016/j.learninstruc.2020.101440},
  title={How does information consistency influence prospective teachers’ decisions about task difficulty assignments? : A within-subject experiment to explain data-based decision-making in heterogeneous classes},
  volume={74},
  issn={0959-4752},
  journal={Learning and Instruction},
  author={Kärner, Tobias and Warwas, Julia and Krannich, Maike and Weichsler, Niklas},
  note={Article Number: 101440}
}

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    <dc:contributor>Weichsler, Niklas</dc:contributor>
    <dc:contributor>Warwas, Julia</dc:contributor>
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    <dcterms:title>How does information consistency influence prospective teachers’ decisions about task difficulty assignments? : A within-subject experiment to explain data-based decision-making in heterogeneous classes</dcterms:title>
    <dc:contributor>Krannich, Maike</dc:contributor>
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    <dc:creator>Krannich, Maike</dc:creator>
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    <dcterms:abstract xml:lang="eng">In order to provide adaptive instruction in heterogeneous classes, teachers have to consider performance-related information for their decisions on which task difficulty level fits best for a particular student. Focusing on prospective teachers’ data-based decision-making, we aim to investigate their use of proximal and distal indicators of students’ ability and readiness to deal successfully with domain-specific tasks that are available for 32 student cases in a digital test environment. To address several hypotheses on the participants’ information-processing behaviors and decisions, we conducted a within-subject experiment in which we systematically varied the informational consistency of the presented student cases. We used a mixed-methods approach to measure observable information-processing behavior and decision-making. We assessed the effects of consistent/inconsistent cases on the amount, sequence, and perceived relevance of selected information, the type of processed information, the time needed for information processing, and the subjective confidence when making a decision via linear dynamic panel-data modeling.</dcterms:abstract>
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    <dc:creator>Kärner, Tobias</dc:creator>
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