Metarepresentation: Native Competence and Targets for Instruction

By Andrea A. diSessa. 2004

572 citations recorded by the source corpus.

Subfields: Developmental and Educational Psychology, Education

View Paper: https://www.semanticscholar.org/paper/07fcf80ddc8fba7a62b59293a72a70dd64885b6f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Developmental and Educational Psychology

1. Christine D. Tippett
2. Vaughan Prain
3. Russell Tytler
4. Marcia C. Linn
5. Douglas B. Clark
6. Z. Zhang
7. David Weintrop
8. Bert van Oers

In Education

1. Christine D. Tippett
2. Vaughan Prain
3. Marcia C. Linn
4. Russell Tytler
5. Z. Zhang
6. Bert van Oers
7. Douglas B. Clark
8. P. van den Eeden

Citations in the corpus

• Learning Through Constructing Representations in Science: A framework of representational construction affordances (2012, methodology): “The examples in Figure 6 show the extended reasoning of each of the students in seeking consistency in their accounts, comprehensiveness, and explanatory clarity (diSessa, 2004).”
• Components of Spatial Intelligence (2010, unknown): “In research on children's scientific problem solving, diSessa (2004) introduced the term meta-representational competence to refer to the ability to create new representations, choose the best representation for a particular task, and understand why particular representations facilitate task…”
• Defining Computational Thinking for Mathematics and Science Classrooms (2016, methodology): “The use of data in classrooms also includes introducing the basics of statistics and probability so the data can be used to draw conclusions (Shaughnessy 2007) as well as developing fluency with both conventional and novel data visualizations (diSessa 2004).”
• What recent research on diagrams suggests about learning with rather than learning from visual representations in science (2016, background): “representations to support claims, make inferences, and make predictions (diSessa, 2004; Gilbert, 2008; Kozma & Russell, 2005).”
• A Framework for Re‐thinking Learning in Science from Recent Cognitive Science Perspectives (2010, methodology): “Our research has also been guided by various perspectives, including semiotic accounts of the representational nature of learning tasks in science (diSessa, 2004; Lemke, 2004), and socio-cultural theories of science learning as induction into the meaning-making practices of science communities (Lemk”
• Using Representations for Teaching and Learning in Science (2008, methodology): “Other classroom studies in this area (Cox, 1999; Greeno & Hall, 1997; Prain, Waldrip & Carolan, 2007; diSessa, 2004; Tytler Peterson & Prain 2006) have noted the importance of teacher and student negotiation of the meanings evident in verbal, visual, mathematical and gestural representations in scie”
• Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework (2013, background): “…about motion, while typically discrete and event-based, can be productively leveraged through appropriate scaffolding to generate correct understandings and representations of motion as a process of continuous change (diSessa et al. 1991b; diSessa 2001, 2004; Ford 2003; Sherin et al. 1993).”
• The Cognitive Science of Visual-Spatial Displays: Implications for Design (2011, background): “This type of understanding has been referred to as meta-representational competence (DiSessa, 2004) and cannot always be assumed.”
• Representational Translation With Concrete Models in Organic Chemistry (2012, background): “More generally, it is beneficial for students to develop meta-representational competence, which includes the ability to understand the advantages and disadvantages of particular representations and to select optimal representations for particular purposes (diSessa, 2004; diSessa & Sherin, 2000).”
• Are representations to be provided or generated in primary mathematics education? Effects on transfer (2009, background): “Previous research gave us some reason to believe that – under certain instructional conditions – self-constructed representations may be more productive in generating mathematical knowledge than ready-made, provided representations (Ainsworth, 2006; diSessa, 2004; Van Dijk, Van Oers, & Terwel, 2003)”
• Can generating representations enhance learning with dynamic visualizations (2011, background): “One explanation to the success is representational competency (diSessa, 2004).”