**2.1. Cognitive processes and mechanisms**

The main task for developmental researchers is to explain how children build on their intuitive curiosity about the world to become skilled scientific reasoners. *Curiosity*, defined as "the threshold of desired uncertainty in the environment that leads to exploratory behavior" (Jirout & Klahr, 2012, p. 150), will lead to information seeking. Information seeking activates a number of basic cognitive mechanisms that are used to extract (encode) information from the environment and then children (and adults) can act on this information in order to achieve a goal (i.e., use a strategy; Klahr, 2001; Kuhn, 2010). We turn our discussion to two such mechanisms and discuss how these mechanisms underlie the development of a specific type of information seeking: scientific reasoning.

A mechanistic account of the development of scientific reasoning includes information about the processes by which this change occurs, and how these processes lead to change over time (Klahr, 2001). Mechanisms can be described at varying levels (e.g., neurological, cognitive, interpersonal) and over different time scales. For example, neurological mechanisms (e.g., inhibition) operate at millisecond time scales (Burlea, Vidala, Tandonneta, & Hasbroucq, 2004) while learning mechanisms may operate over the course of minutes (e.g., inhibiting irrelevant information during problem solving; Becker, 2010). Many of the cognitive processes and mechanisms that account for learning and for problem solving across a variety of domains are important to the development of scientific reasoning skills and science knowledge acquisition. Many cognitive mechanisms have been identified as underlying scientific reasoning and other high-level cognition (e.g., analogy, statistical learning, categorization, imitation, inhibition; Goswami, 2008). However, due to space limitations we focus on what we argue are the two most critical mechanisms – *encoding* and *strategy development* – to illustrate the importance of individual level cognitive abilities.

## *2.1.1. Encoding*

62 Current Topics in Children's Learning and Cognition

In the following account, we suggest that despite the early emergence of many of the precursors of skilled scientific reasoning, its developmental trajectory is slow and requires instruction, support, and practice. In Section 2 of the chapter, we discuss cognitive and metacognitive factors. We focus on two mechanisms that play a critical role in all cognitive processes (i.e., encoding and strategy acquisition/selection). *Encoding* involves attention to relevant information; it is foundational in all reasoning. *Strategy use* involves intentional approaches to seeking new knowledge and synthesizing existing knowledge. These two mechanisms are key components for any type of intentional information seeking yet follow a slightly different development trajectory in the development of scientific reasoning skills. We then discuss the analogous development of metacognitive awareness of what is being encoded, and metastrategic skills for choosing and deploying hypothesis testing and inference strategies. In Section 3, we describe the role of contextual factors such as direct and scaffolded instruction, and the cultural tools that support the development of the cognitive

Effective scientific reasoning requires both deductive and inductive skills. Individuals must understand how to assess what is currently known or believed, develop testable questions, test hypotheses, and draw appropriate conclusions by coordinating empirical evidence and theory. Such reasoning also requires the ability to attend to information systematically and draw reasonable inferences from patterns that are observed. Further, it requires the ability to assess one's reasoning at each stage in the process. Here, we describe some of the key issues

The main task for developmental researchers is to explain how children build on their intuitive curiosity about the world to become skilled scientific reasoners. *Curiosity*, defined as "the threshold of desired uncertainty in the environment that leads to exploratory behavior" (Jirout & Klahr, 2012, p. 150), will lead to information seeking. Information seeking activates a number of basic cognitive mechanisms that are used to extract (encode) information from the environment and then children (and adults) can act on this information in order to achieve a goal (i.e., use a strategy; Klahr, 2001; Kuhn, 2010). We turn our discussion to two such mechanisms and discuss how these mechanisms underlie the

A mechanistic account of the development of scientific reasoning includes information about the processes by which this change occurs, and how these processes lead to change over time (Klahr, 2001). Mechanisms can be described at varying levels (e.g., neurological, cognitive, interpersonal) and over different time scales. For example, neurological mechanisms (e.g., inhibition) operate at millisecond time scales (Burlea, Vidala, Tandonneta, & Hasbroucq, 2004) while learning mechanisms may operate over the course of minutes (e.g., inhibiting irrelevant information during problem solving; Becker, 2010). Many of the

and metacognitive skills required for the emergence of scientific thinking.

in developing these cognitive and metacognitive scientific reasoning skills.

development of a specific type of information seeking: scientific reasoning.

**2. The development of scientific reasoning** 

**2.1. Cognitive processes and mechanisms** 

Encoding is the process of representing information and its context in memory as a result of attention to stimuli (Chen, 2007; Siegler, 1989). As such, it is a central mechanism in scientific reasoning because we must represent information before we can reason about it, and the quality and process of representation can affect reasoning. Importantly, there are significant developmental changes in the ability to encode the relevant features that will lead to sound reasoning and problem solving (Siegler, 1983; 1985). Encoding abilities improve with the acquisition of *encoding strategies* and with increases in children's *domain knowledge* (Siegler, 1989). Young children often encode irrelevant features due to limited domain knowledge (Gentner, Loewenstein, & Thompson, 2003). For example, when solving problems to make predictions about the state of a two-arm balance beam (i.e., tip left, tip right, or balance), children often erroneously encode distance to the fulcrum and amount of weight as a single factor, decreasing the likelihood of producing a correct solution (which requires weight and distance to be encoded and considered separately as causal factors, while recognizing noncausal factors such as color; Amsel, Goodman, Savoie, & Clark, 1996; Siegler, 1983). Increased domain knowledge helps children assess more effectively what information is and is not necessary to encode. Further, children's encoding often improves with the acquisition of encoding strategies. For example, if a child is attempting to recall the location of an item in a complex environment, she may err in encoding only the features of the object itself without encoding its relative position. With experience, she may encode the relations between the target item and other objects (e.g., the star is in front of the box), a strategy known as cue learning. Encoding object position and relative position increases the likelihood of later recall and is an example of how encoding better information is more important than simply encoding more information (Chen, 2007; Newcombe & Huttenlocher, 2000).

Effective encoding is dependent on directing attention to *relevant* information, which in turn leads to accurate representations that can guide reasoning. Across a variety of tasks, experts are more likely to attend to critical elements in problem solving, and less likely to attend to irrelevant information, compared to novices (Gobet, 2005). *Domain knowledge* plays an important role in helping to guide attention to important features. Parents often direct a child's attention to critical problem features during problem solving. For example, a parent may keep track of which items have been counted in order to help a child organize counting (Saxe, Guberman, & Gearhart, 1987). Instructional interventions in which children were directed towards critical elements in problem solving improved their attention to these

features (Kloos & Van Orden, 2005). Although domain knowledge is helpful in directing attention to critical features, it may sometimes limit novel reasoning in a domain and limit the extent to which attention is paid to disconfirming evidence (Li & Klahr, 2006). Finally, *self-generated activity* improves encoding. Self-generation of information from memory, rather than passive attention, is associated with more effective encoding because it recruits greater attentional resources than passive encoding (Chi, 2009).

Emergence of Scientific Reasoning 65

improved children's memory for these structures; for example, questions about a child's day at school help to structure this event and improved recall (Nelson, 1996). Children also learn new strategies by solving problems cooperatively with adults. In a sorting task, preschool children were more likely to improve their classification strategies after working with their mothers (Freund, 1990). Further, children who worked with their parents on a hypothesistesting task were more likely to identify causal variables than children who worked alone because parents helped children construct valid experiments, keep data records, and repeat

Children also acquire strategies by interacting with an adult modeling a novel strategy. Middle-school children acquired a reading comprehension strategy (e.g., anticipating the ending of a story) after seeing it modeled by their teacher (Palinscar, Brown, & Campione, 1993). Additionally, children can acquire new strategies from interactions with other children. Monitoring other children during problem solving improves a child's understanding of the task and appears to improve how they evaluate their own performance (Brownell & Carriger, 1991). Elementary school children who collaborated with other students to solve the balance-scale task outperformed students who worked alone (Pine & Messer, 1998). Ten-year-olds working in dyads were more likely to discuss their strategies than children working alone and these discussions were associated with

More than one strategy may be useful for solving a problem, which requires a means to select among candidate strategies. One suggestion is that this process occurs by adaptive selection. In adaptive selection, strategies that match features of the problem are candidates for selection. One component of selection is that newer strategies tend to have a slightly higher priority for use when compared to older strategies (Siegler, 1996). Successful selection is made on the basis of the effectiveness of the strategy and its cost (e.g., speed), and children tend to choose the fastest, most accurate strategy available (i.e., the most

Cognitive mechanisms provide the basic investigation and inferential tools used in scientific reasoning. The ability to reason about knowledge and the means for obtaining and evaluating knowledge provide powerful tools that augment children's reasoning. *Metacognitive abilities* such as these may help explain some of the discrepancies between early scientific reasoning abilities and limitations in older children, as well as some of the

Sodian, Zaitchik, and Carey (1991) argue that two basic skills related to early metacognitive acquisitions are needed for scientific reasoning. First, children need to understand that inferences can be drawn from evidence. The theory of mind literature (e.g., Wellman, Cross, & Watson, 2001) suggests that it is not until the age of 4 that children understand that beliefs and knowledge are based on perceptual experience (i.e., evidence). As noted earlier, experimental work demonstrates that preschoolers can use evidence to make judgments

generating better hypotheses than children working alone (Teasley, 1995).

developmental changes in encoding and strategy use.

**2.2. Metacognitive and metastrategic processes** 

experiments (Gleason & Schauble, 2000).

adaptive strategy).
