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Data-Texts in the Sciences: The Evidence-Explanation Continuum.
Richard Duschl1, Lucy Avraamidou2, Nathália Helena Azevedo3
1Caruth Institute for Engineering Education, Southern Methodist University, Dallas, TX USA.
Science & Education
|May 4, 2021
Summary
This study introduces the Evidence-Explanation (EE) Continuum, a new model for science education. It emphasizes epistemological conversations and data transformations to enhance understanding of scientific practices.
Area of Science:
- Science Education Research
- Philosophy of Science
- Epistemology
Background:
- Current science education reform recommendations.
- Giere's (1986, 1999) views on model-based science.
- Need for an approach addressing conceptual, epistemological, and social knowledge domains.
Purpose of the Study:
- Propose the Evidence-Explanation (EE) Continuum as an alternative approach to science education.
- Emphasize epistemological conversations in data acquisition and transformation.
- Reveal the dynamics of scientific practices through data-texts.
Main Methods:
- Defining data-texts as steps in data transformation (observation to data, data to evidence, evidence to models, models to explanations).
- Analyzing the EE continuum's transition stages for meaning negotiation and knowledge construction.
- Focusing on epistemics of science for designing learning environments.
Main Results:
- The EE continuum unfolds evidence use in knowledge building.
- Data-texts reveal the dynamics of scientific practices.
- Stages of transition foster collective knowledge construction and meaning negotiation.
Conclusions:
- The EE continuum supports contemporary understandings of scientific practices.
- Focusing on epistemics of science enhances learners' insights into data and explanations.
- Implications for designing learning environments that mirror scientific inquiry.
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