Characteristics of Modeling of Data as a Process of Creating Evidence and its Meaning on Science Education

Abstract

Across the world there is an increasing need for ‘data literacy’ for young students which can process expansively increasing. In this study, we explore science research practice focusing on characteristics of data modeling to identify affordances of educational approaches for data literacy in science education. Artifacts, such as research plans, lab seminar presentation materials, and master's thesis produced during the two-year science research, were collected as the main data source and the process of data modeling was analyzed according to a qualitative method. The results of the study include the characteristics of data modeling in scientific practice and its implications for data literacy in science education. First, the practice of scientists is not to gain data, but to do data modeling. Second, data modeling is a complex and dynamic activities that perform through a cooperative relationship between human actor and non-human actor such as materials and instruments. Third, data modeling is to make evidence through the production and stabilization of phenomena, and its process requires consistency with theory. The results of this study provide implications for data literacy education suitable for the context of scientific inquiry by unfolding the features of data modeling in the actual scientific research case.