Cultivating Model-Based Reasoning in Science Education

By R. Lehrer, Leona Schauble. 2005

332 citations recorded by the source corpus.

Subfields: Developmental and Educational Psychology, Management Science and Operations Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/71a07d9bd88047a2d1e9889b7cf422e04b40d3f0

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In Developmental and Educational Psychology

In Management Science and Operations Research

In Sociology and Political Science

Citations in the corpus

Commentary on the Final Report of the National Mathematics Advisory Panel (2008, background): “The efforts by Lehrer and Schauble (2006) to engage very young students in creating mathematical representations and models demonstrate what is possible in order to deeply affect students’ understanding of mathematics.”
Drawing to reason and learn in science (2020, background): “…and through a variety of representations, and stress the importance of students and teachers negotiating realized meanings in these processes across modes (Ford & Forman, 2006; Greeno & Hall, 1997; Lehrer & Schauble, 2006a, 2006b; Tytler, Peterson, & Prain, 2006; Waldrip, Prain, & Carolan, 2010).”
Making classroom assessment more accountable to scientific reasoning: A case for attending to mechanistic thinking (2009, background): “In their work helping students develop modeling skills, Lehrer and Schauble have described models as analogical representations of various kinds (inscriptions, computational, and so on) that are “extensible, general, and mathematically powerful” (Lehrer & Schauble, 2005, p. 383).”
Handbook of Research on Learning and Instruction (2011, background): “In this section we describe several research studies examining two skill learning variables, feedback and contextual interference, influential in motor skill learning.”
Styles of Scientific Reasoning: A Cultural Rationale for Science Education? (2017, background): “Indeed, many would argue that it is the defining activity of science (Crombie, 1994; Lehrer & Schauble, 2006; Nercessian, 2008; Oakeshott, 1933).”
Inventing a “Mid Level” to Make Ends Meet: Reasoning between the Levels of Complexity (2008, background): “Lemke (2001) has reviewed the scientific discussion of “mid levels”: he described such levels as emerging between every two levels, particularly for socialecological systems.”
Learning progressions and teaching sequences: a review and analysis (2011, background): “Lehrer and Schauble (2006) see engagement in resemblance representation tasks as an entrée to modelling.”
Examining Teachers’ Instructional Moves Aimed at Developing Students’ Ideas and Questions in Learner-Centered Science Classrooms (2012, background): “This science-as-practice approach (Lehrer and Schauble 2006) places students into the roles of scientists who conduct serious investigations on topics that are relevant to their lives and interests.”
Instructional-Design Theories and Models: Building a Common Knowledge Base. Volume III. (2009, background): “An excellent summary of social issues is given by Lehrer and Schauble (2006).”
Learning Through Constructing Representations in Science: A framework of representational construction affordances (2012, background): “Researchers in science classrooms have identified the crucial role of representational competence in developing conceptual learning (e.g. diSessa, 2004; Lehrer & Schauble, 2006a, 2006b; Lemke, 2003, 2004).”
Middle-School Science Students’ Scientific Modelling Performances Across Content Areas and Within a Learning Progression (2013, background): “Lehrer and Schuable (2003, 2006) define ‘model’ as a presentation that serves as arguments that explain the natural world.”
Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework (2013, background): “, multi-layered abstraction) is the application programming interface (API) of a software component, and the underlying layer of the component’s implementation (Wing 2008, 2006). Similarly, Schmidt (2006) points out that software researchers and developers typically engage in creating abstractions t”
Defining Computational Thinking for Mathematics and Science Classrooms (2016, background): “Science education is considered to be inseparably intertwined with the development of epistemic and representational practices (Kaput 1998; Lehrer and Schauble 2006; Wilensky and Papert 2010; Wilkerson-Jerde et al. 2015), yet these practices are rarely made explicit as part of”