*3.2.1. Cultural resources to facilitate encoding and strategy use*

The sociocultural perspective highlights the role that language, speech, symbols, signs, number systems, objects, and tools play in individual cognitive development (Lemke, 2001). As highlighted in previous examples, adult and peer collaboration, dialogue, and other elements of the social environment are important mediators. In this section, we highlight some of the verbal, visual, and numerical elements of the physical context that support the emergence of scientific reasoning.

Most studies of scientific reasoning include some type of *verbal* and *pictorial representation* as an aid to reasoning. As encoding is the first step in solving problems and reasoning, the use of such supports reduces cognitive load. In studies of hypothesis testing strategies with children (e.g., Croker & Buchanan, 2011; Tschirgi, 1980), for example, multivariable situations are described both verbally and with the help of pictures that represent variables (e.g., type of beverage), levels of the variable (e.g., cola vs. milk), and hypothesis-testing strategies (see Figure 1, panel A). In classic work by Kuhn, Amsel, and O'Loughlin (1988), a picture is provided that includes the outcomes (children depicted as healthy or sick) along with the levels of four dichotomous variables (e.g., orange/apple, baked potato/French fries, see Kuhn et al., 1988, pp. 40-41). In fact, most studies that include children as participants provide pictorial supports (e.g., Ruffman et al., 1993; Koerber, Sodian, Thoermer, & Nett, 2005). Even at levels of increasing cognitive development and expertise, diagrams and visual aids are regularly used to support reasoning (e.g., Schunn & Dunbar, 1996; Trafton & Trickett, 2001; Veermans, van Joolingen, & de Jong, 2006).

**Figure 1.** Panel A illustrates the type of pictorial support that accompanies the verbal description of a hypothesis-testing task (from Croker & Buchanan, 2011). Panel B shows an example of a physical apparatus (from Triona & Klahr, 2007). Panel C shows a screenshot from an intelligent tutor designed to teach how to control variables in experimental design (Siler & Klahr, 2012; see http://tedserver.psy.cmu.edu/demo/ted4.html, for a demonstration of the tutor).

Various elements of *number and number systems* are extremely important in science. Sophisticated scientific reasoning requires an understanding of data and the evaluation of numerical data. Early work on evidence evaluation (e.g., Shaklee, Holt, Elek, & Hall, 1988) included 2 x 2 contingency tables to examine the types of strategies children and adults used (e.g., comparing numbers in particular cells, the "sums of diagonals" strategy). Masnick and Morris (2008) used data tables to present evidence to be evaluated, and varied features of the presentation (e.g., sample size, variability of data). When asked to make decisions without the use of statistical tools, even third- and sixth-graders had rudimentary skills in detecting trends, overlapping data points, and the magnitude of differences. By sixth grade, participants had developing ideas about the importance of variability and the presence of outliers for drawing conclusions from numerical data.

72 Current Topics in Children's Learning and Cognition

emergence of scientific reasoning.

*3.2.1. Cultural resources to facilitate encoding and strategy use* 

Trickett, 2001; Veermans, van Joolingen, & de Jong, 2006).

hypotheses.

at producing all possible combinations necessary to manipulate and isolate variables to test

The sociocultural perspective highlights the role that language, speech, symbols, signs, number systems, objects, and tools play in individual cognitive development (Lemke, 2001). As highlighted in previous examples, adult and peer collaboration, dialogue, and other elements of the social environment are important mediators. In this section, we highlight some of the verbal, visual, and numerical elements of the physical context that support the

Most studies of scientific reasoning include some type of *verbal* and *pictorial representation* as an aid to reasoning. As encoding is the first step in solving problems and reasoning, the use of such supports reduces cognitive load. In studies of hypothesis testing strategies with children (e.g., Croker & Buchanan, 2011; Tschirgi, 1980), for example, multivariable situations are described both verbally and with the help of pictures that represent variables (e.g., type of beverage), levels of the variable (e.g., cola vs. milk), and hypothesis-testing strategies (see Figure 1, panel A). In classic work by Kuhn, Amsel, and O'Loughlin (1988), a picture is provided that includes the outcomes (children depicted as healthy or sick) along with the levels of four dichotomous variables (e.g., orange/apple, baked potato/French fries, see Kuhn et al., 1988, pp. 40-41). In fact, most studies that include children as participants provide pictorial supports (e.g., Ruffman et al., 1993; Koerber, Sodian, Thoermer, & Nett, 2005). Even at levels of increasing cognitive development and expertise, diagrams and visual aids are regularly used to support reasoning (e.g., Schunn & Dunbar, 1996; Trafton &

**Figure 1.** Panel A illustrates the type of pictorial support that accompanies the verbal description of a hypothesis-testing task (from Croker & Buchanan, 2011). Panel B shows an example of a physical apparatus (from Triona & Klahr, 2007). Panel C shows a screenshot from an intelligent tutor designed to

(A) (B) (C)

teach how to control variables in experimental design (Siler & Klahr, 2012; see http://tedserver.psy.cmu.edu/demo/ted4.html, for a demonstration of the tutor). Although language, symbols, and number systems are used as canonical examples of cultural tools and resources within the socio-cultural tradition (Lemke, 2001), recent advances in *computing and computer simulation* are having a huge impact on the development and teaching of scientific reasoning. Although many studies have incorporated the use of physical systems (Figure 1, panel B) such as the canal task (Gleason & Schauble, 2000), the ramps task (e.g., Masnick & Klahr, 2003), mixing chemicals (Kuhn & Ho, 1980), and globes (Vosniadou, Skopeliti, & Ikospentaki, 2005), there is an increase in the use of interactive computer simulations (see Figure 1, panel C). Simulations have been developed for electric circuits (Schauble, Glaser, Raghavan, & Reiner, 1992), genetics (Echevarria, 2003), earthquakes (Azmitia & Crowley, 2001), flooding risk (Keselman, 2003), human memory (Schunn & Anderson, 1999), and visual search (Métrailler, Reijnen, Kneser, & Opwis, 2008). Non-traditional science domains have also been used to develop inquiry skills. Examples include factors that affect TV enjoyment (Kuhn et al., 1995), CD catalog sales (Dean & Kuhn, 2007), athletic performance (Lazonder, Wilhelm, & Van Lieburg, 2009), and shoe store sales (Lazonder, Hagemans, & de Jong, 2010).

Computer simulations allow visualization of phenomena that are not directly observable in the classroom (e.g., atomic structure, planetary motion). Other advantages include that they are less prone to measurement error in apparatus set up, and that they can be programmed to record all actions taken (and their latencies). Moreover, many systems include a scaffolded method for participants to keep and consult records and notes. Importantly, there is evidence that simulated environments provide the same advantages as isomorphic "hands on" apparatus (Klahr, Triona, & Williams, 2007; Triona & Klahr, 2007).

New lines of research are taking advantage of advances in computing and intelligent computer systems. Kuhn (2011b) recently examined how to facilitate reasoning about multivariable causality, and the problems associated with the visualization of outcomes resulting from multiple causes (e.g., the causes for different cancer rates by geographical area). Participants had access to software that produces a visual display of data points that represent main effects and their interactions. Similarly, Klahr and colleagues (Siler, Mowery, Magaro, Willows, & Klahr, 2010) have developed an intelligent tutor to teach experimentation

strategies (see Figure 1, panel C). The use of intelligent tutors provides the unique opportunity of personally tailored learning and feedback experiences, dependent on each student's pattern of errors. This immediate feedback can be particularly useful in helping develop metacognitive skills (e.g., Roll, Alaven, McLaren, & Koedinger, 2011) and facilitate effective student collaboration (Diziol, Walker, Rummel, & Koedinger, 2010).

Emergence of Scientific Reasoning 75

**Author details** 

Bradley J. Morris

Amy M. Masnick *Hofstra University, USA* 

**5. References** 

*Kent State University, USA* 

*Illinois State University, USA* 

**Acknowledgements** 

Steve Croker and Corinne Zimmerman

All authors contributed equally to the manuscript. The authors thank Eric Amsel, Deanna

Alfieri, L., Brooks, P. J., Aldrich, N. J., & Tenenbaum, H. R. (2011). Does discovery-based

Amsel, E., Goodman, G., Savoie, D., & Clark, M. (1996). The development of reasoning about causal and noncausal influences on levers. *Child Development, 67*, 1624-1646. Amsel, E., Klaczynski, P. A., Johnston, A., Bench, S., Close, J., Sadler, E., & Walker, R. (2008). A dual-process account of the development of scientific reasoning: The nature and development of metacognitive intercession skills. *Cognitive Development, 23,* 452-471. Azmitia, M. & Crowley, K. (2001). The rhythms of scientific thinking: A study of collaboration in an earthquake microworld. In K. Crowley, C.D., Schunn, & T. Okada (Eds). *Designing for science: Implications from everyday, classroom, and professional settings*

Becker, S. I. (2010). The role of target-distractor relationships in guiding attention and the eyes in visual search. *Journal of Experimental Psychology: General, 139*, 247–265. Bonawitz, E., Shafto, P., Gweon, H., Goodman, N. D., Spelke, E., & Schulz, L. (2011). The double-edged sword of pedagogy: Instruction limits spontaneous exploration and

Brownell, C. A., & Carriger, M. S. (1991). Collaborations among toddler peers: Individual contributions in social contexts. In L. B. Resnick, J. M. Levine, & S. D. Teasley (Eds.), *Perspectives on socially shared cognitio*n (pp. 365-383). Washington, D. C.: American

Burlea, C., Vidala, F., Tandonneta, C., & Hasbroucq, T. (2004). Physiological evidence for response inhibition in choice reaction time tasks. *Brain and Cognition, 56*, 153-64. Butler, L., & Markman, E. (2012a). Finding the cause: Verbal framing helps children extract causal evidence embedded in a complex scene. *Journal of Cognition and Development*, *13*,

instruction enhance learning?. *Journal Of Educational Psychology*, *103*, 1-18.

Kuhn, and Jamie Jirout for comments on a previous version of this chapter.

(pp. 51-81). Mahwah, NJ: Lawrence Erlbaum.

discovery. *Cognition*, *120*, 322-330.

Psychological Association.

38-66.

Tweney, Doherty, and Mynatt (1981) noted some time ago that most tasks used to study scientific thinking were artificial because real investigations require *aided* cognition. However, as can be seen by several exemplars, even lab studies include support and assistance for many of the known cognitive limitations faced by both children and adults.
