Data-Texts in the Sciences: The Evidence-Explanation Continuum

Richard Duschl, Lucy Avraamidou*, Nathália Helena Azevedo

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
122 Downloads (Pure)

Abstract

Grounded within current reform recommendations and built upon Giere’s views (1986, 1999) on model-based science, we propose an alternative approach to science education which we refer to as the Evidence-Explanation (EE) Continuum. The approach addresses conceptual, epistemological, and social domains of knowledge, and places emphasis on the epistemological conversations about data acquisitions and transformations in the sciences. The steps of data transformation, which we refer to as data-texts, we argue, unfold the processes of using evidence during knowledge building and reveal the dynamics of scientific practices. Data-texts involve (a) obtaining observations/measurements to become data; (b) selecting and interpreting data to become evidence; (c) using evidence to ascertain patterns and develop models; and (d) utilizing the patterns and models to propose and refine explanations. Throughout the transformations of the EE continuum, there are stages of transition that foster the engagement of learners in negotiations of meaning and collective construction of knowledge. A focus on the EE continuum facilitates the emergence of further insights, both by questioning the nature of the data and its multiple possibilities for change and representations and by reflecting on the nature of the explanations. The shift of emphasis to the epistemics of science holds implications for the design of learning environments that support learners in developing contemporary understandings of the nature and processes of scientific practices.

Original languageEnglish
Pages (from-to)1159–1181
Number of pages23
JournalScience and Education
Volume30
Early online date28-Apr-2021
DOIs
Publication statusPublished - 2021

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