Re-examining the power of video motion analysis to promote the reading and creating of kinematic graphs

Today, scientists employ a variety of visual representations or inscriptions to operate on the external world in order to facilitate their understanding of it. The use of the term 'inscription' was suggested by Roth and McGinn (1998) in order to avoid the association that a representation portrays mental content. Tversky (2005) distinguishes between visual representations or inscriptions, that are naturally visual such as maps, architectural plans, flora and fauna and mechanical devices, and those designed to present concepts which are not inherently visual such as diagrams and graphs. Ironically, it seems that to understand the real world we “see” scientists use abstract inscriptions that are not inherently visual. Inscriptions are types of transformations that materialize or visualize an entity into another format or mode (Latour, 1987). They convey information, organize data, demonstrate patterns and relationships, and communicate scientific knowledge (Wu & Krajcik, 2006). Moreover, scientific knowledge is constructed through manipulating a variety of inscriptions (Knorr-Cetina, 1983, Lynch & Woolgar, 1990), and therefore they are integral (Lemke, 1998) and central (Bowen & Roth, 2005) to the practice of science in general and to physics in particular. Indeed, “In the case of physics, it is generally acknowledged that it is practically impossible to address many basic content areas without intense use of graphic representations” (Testa, et al., 2002, p. 235). Another support to the importance of visual representations in physics can be found from the cognitive psychology research domain. For example, Kozhevnikov et al. (2007) argued that the majority of physics problems involve manipulation of spatial representations in the form of graphs, diagrams, or physical models. As well, the United States Employment Service includes physics in its list of occupations requiring a high level of spatial ability, that is, the ability to perform spatial transformations of mental images or their parts (Dictionary of Occupational Titles, 1991).

Among the variety of inscriptions, the most common is the graph which depicts the relationships between continuous variables in pictorial form (Mckenzie & Padilla, 1986). Graphs, depicting a physical event, summarize large amounts of information in an economical way (Latour, 1987), while still allowing details to be resolved. They allow a glimpse of trends which cannot easily be recognized in a table of the same data (Beichner, 1994). Indeed, Mokros and Tinker (1987) note that graphs allow scientists to use their powerful visual pattern recognition facilities to see trends and spot subtle differences in shape. In addition, Bowen and Roth (1995) argue that scatter-plots, best-fit functions, and other graphs in Cartesian coordinates are ideal for representing the continuous co-variation of two variables that would be difficult to express in words. In fact, it has been argued that there is no other statistical tool as powerful as graphs for facilitating pattern recognition in complex data. Probably from the above reasons, "Line graph construction and interpretation are very important because they are an integral part of experimentation, the heart of science” (McKenzie & Padilla, 1986, p. 572) and graphs tend to be most convincing evidence to scientists (Latour, 1987).

Due to their importance to science, graphic and symbolic representations as essential communication tools are well established in schools and there is a growing consensus on their didactic advantages (Testa et al., 2002, p. 235). Furthermore, according to Brungardt and Zollman (1995), graphing skills are essential to understand scientific information. The importance of graphs in school is well emphasized also in educational reform documents. Bowen and Roth (2005) summarize from the National Council of Teacher of Mathematics (NCTM, 1989) the following detailed list of actions, relating to skills required for graph reading and creating, in which students ought to be competent:

• Describe and represent relationships with tables, graphs, and rules (p. 98).

• Analyze functional relationships to explain how a change in one quantity results in a change in another (p. 98).

• Systematically collect, organize, and describe data (p. 105).

• Estimate, make, and use measurements to describe and compare phenomena (p. 116).

• Construct, read, and interpret tables, charts, and graphs (p. 105).

• Make inferences and convincing arguments that are based on data analysis (p. 105).

• Evaluate arguments that are based on data analysis (p. 105).

• Represent situations and number patterns with tables, graphs, verbal rules, and equations and explore the interrelationships of these representations (p. 102).

• Analyze tables and graphs to identify properties and relationships (p. 102).