Understanding Dynamic Learning with Open Data: Exploring Naive Positionality in Education

Potential Abstract: This research article aims to investigate the potential of dynamic learning using open data in the context of education, specifically focusing on the implications of naive positionality. The integration of open data in educational settings has the capacity to provide students and educators with vast amounts of real-world data for analysis and learning. However, little attention has been given to the impact of students’ prior knowledge, perspectives, and biases when engaging with open data. This study seeks to fill this gap by examining how learners’ naive positionality influences their interactions with dynamic learning environments driven by open data.

The research design of this study employs a mixed methods approach, combining qualitative and quantitative data collection methods. The qualitative component involves semi-structured interviews with students to explore their perceptions, experiences, and challenges while working with open data and dynamic learning environments. The quantitative component utilizes pre- and post-tests to measure students’ knowledge and understanding in relation to the subject matter covered in the dynamic learning activities.

The findings of this research will contribute to a better understanding of how students’ naive positionality influences their learning experiences in dynamic learning environments with open data. By identifying the challenges and opportunities that arise from this integration, educators can design more effective instructional strategies that address students’ diverse perspectives and enhance their critical thinking skills. Additionally, this study will shed light on the factors that either facilitate or hinder the use of open data in educational contexts, providing valuable insights for policy-makers and curriculum developers.

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