| Images of connected features: |
| | | Dynamically linked representations of ratios |  |
| | | Multiple representations for the rock cycle system |  |
| | | different modes of observations in the VSS |  |
| | | Representations of motion at changing speed |  |
| | | The model of constant rate of change in a linear functions |  |
| | | Two-dimensional map in the Virtual Solar System (VSS) |  |
| | | Combining visual and textual data in the Virtual Solar System (VSS) |  |
| | | Switch from Data to Graph View |  |
| | | Comparison of similar visualizations |  |
| | | Hands-on examples of molecular visualization content |  |
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Connections 
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| Description: |
| A powerful way to illustrate a complex phenomenon is to provide students with multiple representations of the phenomenon. These can be of various types including animations, graphs, symbolic illustrations, text, voice, and so on. Representations are not necessarily interactive and therefore are not necessarily visualizations. Using multiple representations enables diverse learners to find a representation that resonates with their ideas. Multiple representations also allow students to identify connections that are salient in one representation but not in another. Multiple representations become even more powerful when they are dynamically linked to each other and synchronized, so that changes in one representation cause appropriate changes in the other. In this manner, students can better understand connections between the various types of representations of a phenomenon and integrate ideas that each of these representations provokes and thus, these multiple representations can serve as pivotal cases. (Kali&Linn, in press) |
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Theoretical background:
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Design of examples to take advantage of symbolic, episodic, visual, verbal, kinesthetic, and other types of memory can improve learning because recall of one type of representation can support recall of another type of representation of the same material (Baddeley & Longman, 1978).
Researchers have designed learning environments to introduce models of many phenomena including water quality (Krajcik et al., 1998), mechanical systems (White & Frederiksen, 1998), and chemical systems (Kozma et al, 1996). These models require scaffolding to succeed.
Visualizations and representations can lead to understanding as well as to confusion (e.g., Hegarty et al., 1999; Tversky, 1977). Learners need opportunities to understand the visualization and to conduct their own experiments with the visualization. Foley (1999) reports that representing heat flow with color, although it sounds intuitive, actually makes less sense to students than when heat is represented using shades of gray. Collecting and displaying data using real time data collection also contributes to successful inquiry and benefits from iterative design studies. Linn and her colleagues (Linn & Songer, 1991; Linn & Hsi, 2000) have researched real time graphing of data (Lewis & Linn, 1994), analog-to-digital probeware (Linn & Songer, 1991) and interpretation of graphical representations. They found that students benefited far more from real time graphing if they first predicted the outcome and then tested their predictions than when they only did the tests.
Making thinking visible involves illuminating and modeling the processes of knowledge integration. Students can demonstrate their own thinking for themselves, their peers, and their teachers (e.g., Davis, Chapter 5). Classroom teachers can illustrate the complexity of scientific thinking (e.g., Bell, Chapter 6). Technologists can devise mechanisms for capturing complex interactions in visualizations (e.g., Clark, Chapter 8; Baumgartner, Chapter 11) and for displaying arguments (e.g., Bell, Chapter 6). Natural scientists can make their thinking visible to students in online forums and other venues (e.g., Bell, Chapter 10). And partnerships can negotiate instruction that offers students a repertoire of these approaches matched to curricular goals (e.g., Slotta, Chapter 9; Baumgartner, Chapter 11; Shear et al., Chapter 12). To some extent visible thinking can empower students to seek coherence and to consider all the alternatives. Students may also find the efforts at visible thinking inaccessible and end up avoiding knowledge integration. Models can also deter students from critical thinking and problem solving by either providing an illusion of comprehension or encouraging memorizing.
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| Tips (Challenges, Limitations, Tradeoffs, Pitfalls): |
Visualizations and representations can lead to understanding as well as to confusion. Learners need opportunities to understand the visualization and to conduct their own experiments with the visualization.
Students may also find the efforts at visible thinking inaccessible and end up avoiding knowledge integration. Models can also deter students from critical thinking and problem solving by either providing an illusion of comprehension or encouraging memorizing.
Modern technologies have stimulated the development of a vast array of visualizations that have yet to help learners. Often visualizations come from tools used by experts in the course of their research such as molecular modeling environments, computer assisted design environments, or geographical information systems. Experts spend long periods of time learning these tools and typically use them to test new ideas or implement complex solutions. The tools often take too long to learn and, when learned, lack the sort of feedback that would help learners with more basic understanding. Creating visualizations that meet the needs of learners remains a high priority for science education.
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| Summary of changes (wiki): |
Change in name to remove "linked" representation.
Change in description. |
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History
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