al., 2001). In addition to basic knowledge about and practice in spatial thinking, students need guidance and support in working through first projects, and appropriate guidance can have significant impacts on student learning. In helping students to research Galapagos finches, Reiser and Tabak (http://www.letus.org/bguile/finches/finch_overview.html) created data tables to represent results, and the tables helped students to reach more compelling conclusions. In studying how students interpret weather maps, Edelson (1999) found that the map colors did not convey information effectively without substantial support. This support included simplification of the displays, presenting outlines of continents, and designing activities to communicate interactions between land and sea and the impact of such interactions on wind, temperature, and the day-night cycle. Hoadley (2004), in collaboration with Cuthbert et al. (2002), asked students to design a desert house that was cool during the day and warm at night. Many students failed to connect the day-night cycle to their designs and/or combined design elements without considering how they interacted.

Students are slow to learn how to monitor their own progress, frequently misjudging their abilities and progress. They cannot easily make links between representations and observed phenomena. For example, in the GenScope project, students used software to explore topics in genetics illustrated by a fictitious dragon species. Students were expected to transfer understanding from the dragon software to the case of worms studied using paper and pencil materials. The first study, however, failed to demonstrate any significant impact from the software use (Lobato, 2003). In a replication, the researchers added a “Dragon Investigations” module based on paper-and-pencil activities. It encouraged students to reflect on parallels between dragons and worms, and to monitor their performance. This approach was more successful.

Students need support—scaffolding and guidance—to allow them to bring their fragmented knowledge to bear on a compelling problem. Central to the support process is the use of static or animated representations, although studies have revealed a series of barriers to understanding such representations.

4.4 THE TRANSFER OF SPATIAL THINKING ACROSS SUBJECTS IN THE CURRICULUM

Central to all of these instructional design efforts to meet the many challenges of fostering spatial literacy is a basic question: To what extent is student learning of spatial knowledge, spatial ways of thinking and acting, and spatial capabilities specific to a particular domain of knowledge? If, for example, the learning of spatial thinking is inherently domain specific, then the instructional challenge is different than it would be if learning in one domain readily transfers to and supports learning in another—and very different—domain. We turn next to this fundamental question of transfer. Can well-learned spatial thinking skills transfer to reasoning about and solving new problems in a different area?

De Corte (2003, p. 142) has noted that “the concept [of transfer] has been very controversial, conceptually as well as empirically.” That learning frequently does not transfer to situations in which it is relevant is a puzzle to researchers and a concern for educators. Knowledge and skills that have been well learned, as indexed by performance at the end of instruction, often fail to transfer to new times and contexts in which they would be helpful. Box 4.1, based on De Corte’s work, contrasts a traditional view of transfer with a modern reconceptualization.

Before we discuss the roles that spatial imagery and spatial representations may play in fostering transfer of spatial thinking, we must distinguish between the ideas of “near” and “far” transfer. The former refers to the transfer of what has been learned to tasks and settings that resemble the original learning situations perceptually and in terms of their obvious themes. Far transfer refers to situations in which what has been learned is successfully applied to situations where there is less perceptual similarity (see the discussion of Tetris, in Section 4.2.4).



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