Visualization: A Metacognitive Skill in Science and Science Education

By J. Gilbert. 2005

454 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Education

View Paper: https://www.semanticscholar.org/paper/c0a30e3b76be3b26d1e9afda1c91f4644fd0a191

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Citations in the corpus

The malleability of spatial skills: a meta-analysis of training studies. (2013, background): “pdf *Feng, J. (2006). Cognitive training using action video games: A new”
Learning at the Symbolic Level (2009, background): “A more sophisticated understanding is linked to an appreciation of the interaction of thermodynamic and kinetic considerations and is likely to be dependent upon the ability to visualise some form of mental model involving molecular collisions and interactions ( Gilbert, 2005 ).”
What recent research on diagrams suggests about learning with rather than learning from visual representations in science (2016, background): “Research in the area of visualisations (e.g. Gilbert, 2007, 2008) has explored external representations that can be seen by and shared with others.”
Influence apart from Adoption: How Interaction between Programming and Scientific Practices Shapes Modes of Inquiry in Four Oceanography Teams (2016, background): “Gilbert makes the case for visualization as a crucial metacognitive skill in science and, inextricably, science education [22].”
What Teachers of Science Need to Know about Models: An overview (2011, background): “…authors consider modelbased views of scientific theory and scientific enquiry valid in depicting the practice of science (Bailer-Jones, 2002; Giere, 1988, 1999a; Gilbert, 2005a; Grandy, 2003; Magnani & Nersessian, 2002; Magnani, Nersessian, & Thagard, 1999; van der Valk, van Driel, & de Vos, 2007).”
Examining the Impact of Student Use of Multiple Modal Representations in Constructing Arguments in Organic Chemistry Laboratory Classes (2010, unknown): “As argued by Gilbert (2005) , using “codes of representations” is a major task for students— especially in organic chemistry, for which the general language is visual mode representations such as IR (Infrared Resonance) and NMR (Nuclear Magnetic Resonance) spectrum as well as symbolic mode represen”
Experimenting with electromagnetism using augmented reality: Impact on flow student experience and educational effectiveness (2014, background): “…has been recognized as a technology with great potential for science learning (Bujak et al., 2013; Cheng & Tsai, 2012;Wu et al., 2013) as it provides newways of tactile and visual interactions which could be useful to improve learning outcomes (Cheng & Tsai, 2012; Gilbert, 2005; Rapp, 2005).”
Conceptual Resources for Learning Science: Issues of transience and grain‐size in cognition and cognitive structure (2008, background): “simulate such situations on the basis of prior experience (Georgiou, 2005), and so visualise mooted scenarios (Gilbert, 2005), rather than formalised knowledge”
Development of an Instrument Designed to Investigate Elements of Science Students’ Metacognition, Self‐Efficacy and Learning Processes: The SEMLI‐S (2008, background): “This issue is evident within the science education literature where metacognition has been defined as, for example, knowledge, control and awareness of learning processes (Baird, 1990; Thomas & McRobbie, 2001) and the ability to think about one’s thinking (Gilbert, 2005), with Blank (2000) positing ”