In today’s digital era, the ability to effectively search for, evaluate, process, and present online information is critical in higher education. Despite its importance, teacher educators often lack validated instruments tailored to assess these competencies. This study describes the adaptation, development, and initial validation of a Situational Judgement Test (SJT) based on the PIKE-E test—originally developed in Spanish—and grounded in the Information Problem Solving using the Internet (IPS-I) model. Following translation, cultural adaptation, andexpert review, we piloted the instrument with 39 teacher educators. Quantitative and qualitative analyses indicate that, while respondents excelled in information selection and processing, they encountered challenges in search and critical evaluation tasks. The instrument demonstrated promising content and construct validity, though we recommend further refinement and larger-scale validation.

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Measuring Teacher Educators’ Information Problem Solving Skills: Development and Validation of a Situational Judgement Test

  • Klaas-Jan Lammers,
  • Jos van Helvoort,
  • Iwan Wopereis,
  • Nynke Bos

摘要

In today’s digital era, the ability to effectively search for, evaluate, process, and present online information is critical in higher education. Despite its importance, teacher educators often lack validated instruments tailored to assess these competencies. This study describes the adaptation, development, and initial validation of a Situational Judgement Test (SJT) based on the PIKE-E test—originally developed in Spanish—and grounded in the Information Problem Solving using the Internet (IPS-I) model. Following translation, cultural adaptation, andexpert review, we piloted the instrument with 39 teacher educators. Quantitative and qualitative analyses indicate that, while respondents excelled in information selection and processing, they encountered challenges in search and critical evaluation tasks. The instrument demonstrated promising content and construct validity, though we recommend further refinement and larger-scale validation.