Abstract
Although it may appear trivial, the first step in developing representational competence is literally looking at a representation. This chapter focuses on eye tracking technology as a tool for assessing visual attention while using representations, particularly with regard to understanding the underlying cognitive processes of representational competence. This technology is not new, but its use is expanding in science education. We give an overview of how eye tracking technology works, what it can measure, and how this type of data can be used as evidence for representation use. In combination with verbal and written data, eye tracking technology might be able to more finely distinguish between novices and experts in the visual use of representations and capture levels of representational competence. We synthesize what has been learned from past uses of this technology in science education and provide insights for potential future uses as an assessment of representational competence to help further this field.
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Notes
- 1.
The five levels originally also include information about building representations. To focus on the visual aspect, this section will only mention aspects of reading representations besides level 1 for which no information about reading is provided. For a complete description of levels see Kozma and Russell (2005), p. 133.
- 2.
For a detailed history of eye tracking see e.g., Duchowski 2007, pp. 51–59
- 3.
This chapter will mainly focus on remote systems.
- 4.
- 5.
Duchowski (2007) points out that the eye-mind assumption might be limited and - under ideal conditions – eye tracking should be complemented by brain activity measures. He gives the example of astronomers who purposefully separate attention from gaze direction when they search for faint stars which cannot be spotted when directly looking at them. Furthermore, experts are able to perceive details of representations parafoveally as described in “Assessing Differences between Experts and Novices” of this chapter.
- 6.
There are also other types of eye movements like smooth pursuits (the eye follows a moving object) and nystagmus (counterbalancing head movements) which do not play a role in assessing RC.
- 7.
Ladder cladograms show a seemingly continuous line from the root to one taxon which is due to the high level of abstraction (see Figure 2.2 and 2.3). Students often have difficulties to understand the hierarchical character of ladder cladograms and rather perceive the “backbone” as a single line (Novick et al. 2012).
- 8.
The study describes RC with PTs both for tree reading and tree building. Since this chapter focuses on visual aspects, it will only address tree reading. For further information about tree building, see Halverson and Friedrichsen (2013).
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Ubben, I., Nitz, S., Daniel, K.L., Upmeier zu Belzen, A. (2018). Assessing Representational Competence with Eye Tracking Technology. In: Daniel, K. (eds) Towards a Framework for Representational Competence in Science Education. Models and Modeling in Science Education, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-319-89945-9_11
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