Modeling of data: An interdisciplinary approach

Abstract

In the context of symbolic representation of data, learning to connect the data generated from macroscopic events with hypothesized changes taking place at the atomic level and the symbolic level used to describe these events is a difficult task for many students. Therefore, an interdisciplinary approach to modeling of data is one way to address this problem. This paper provides insights on modeling of data using DataStudio, a data logging and analysis tool. It concentrates on the overall instructional design of the interdisciplinary approach in the field of mathematics and chemistry that maximize the potential of DataStudio to enhance subject content and pedagogy. Modeling of physicochemical phenomena will be illustrated with examples. Implications for designing this interdisciplinary approach to manipulation and transformation of data will also be discussed.