My intent in this paper is to offer a view of the current state of the art in information processing (IP) approaches to the study of cognitive development. I will provide sufficient substance to indicate what this approach might yield in the way of insight or understanding about the nature of intelligence, although I will not attempt to explicitly define intelligence. It is clear that we are building models of systems which exhibit what is generally considered to be intelligent behavior. However, the fundamental nature of these models is still incompletely understood. Thus, the first order of business appears to be to describe their characteristics, and most of this chapter is directed to that task. Perhaps others can attempt the mapping between definitions of intelligence, either extant or new, and the components of these models.

"Active learning" has been used to describe classrooms that have varied widely with respect to instructional topics, age of learners, and the procedures used to operationalize the general notion of the term. In most cases, the specific variant of active learning under investigation has been more effective than the particular control used for comparison. The goal of the current study was to unambiguously describe, implement, and assess 4 different active learning implementations that varied based on the instructional technique employed by the teacher. The specific topic taught was the procedure for constructing experiments in which a single causal factor is clearly identified and there are no confounds. The procedure is commonly known in the literature on early scientific thinking as the "control of variables strategy" (CVS). The sample consisted of 145 3rd- and 4th-grade students from 3 schools. Students in each grade at each school were randomly assigned to 1 of 4 active learning conditions. Learning of CVS was measured through a hands-on, active learning activity and a written pre- and posttest. Results indicated that compared to minimal guidance/minimal guidance/activity, modeling/direct guidance/activity resulted in significantly higher levels of CVS knowledge on the hands-on activity. When examining student learning from pre- to posttest, students in all conditions had significant learning gains. However, the largest effect sizes were for modeling/direct guidance/activity followed by modeling/modeling/activity, and the weakest effect size was for minimal guidance/minimal guidance/ activity. Thus, more direct/explicit forms of active learning promoted higher learning of CVS than more inquiry-based forms. Educational Impact and Implications Statement Our article presents a scientific investigation of different implementations of active learning. Active learning is a popular instructional method that has not been the subject of well-controlled experimental studies that investigate which features of active learning lead to greater student learning than with other forms of active learning or with more passive forms of instruction. We implemented four forms of active learning in the context of teaching elementary students how to design simple experiments and found each form of active learning to differ in its effectiveness for student learning. Students experienced higher levels of learning when active learning was implemented with more direct/explicit forms of instruction rather than with more inquiry-based forms of instruction. Thus, active learning is an approach whose features need to be systematically isolated and studied to identify how and why active learning can be effective; moving beyond the typical "active learning versus lecture" contrast most often studied in the research literature is an important and needed endeavor.

Question asking plays a fundamental role in learning, and the cognitive development literature contains many studies of specific types of question-asking skills. However, little is known about the developmental course across different aspects of question asking, of which we explore: (a) the ability to ask questions that enable children to solve a specific problem, (b) the ability to ask questions that will increase general understanding about a topic, (c) the ability to recognize the relevance of information yielded by another person's answer to a question, and (d) children's general levels of curiosity. The current study includes four tasks assessing preschool through first-grade children's curiosity and performance on the three different types of question-asking tasks listed above. We observed significant development between kindergarten and first grade in children's question-asking and significant correlations among the different question-asking tasks. Children who generated more questions for problem solving were better at recognizing effective questions, and generating questions for learning was related to generating problem-solving questions. Both the ability to recognize effective questions and to generate questions for learning were positively correlated with our measure of children's curiosity. The results and implications are discussed for understanding the development of question-asking skills and the role of curiosity as a fundamental motivator of children's question asking.

The production of a scientifically literate population is a fundamental goal of our educational system. The justifications for that goal, and descriptions of paths toward it, have been reiterated many times in recent decades, as exemplified by major policy statements and specific recommendations from prestigious organizations ranging from "Benchmarks of Scientific Literacy" (AAAS, 1993) to the recent "Framework for K-12 Science Education" (NRC, 2012). Consequently, substantial effort has been devoted to determining how to increase the likelihood that, as students progress through school, they will acquire at least a rudimentary understanding of fundamental domain-general scientific concepts and procedures, as well as a nontrivial amount of domain-specific concepts. However, given the vast number of those procedures and concepts, it is not surprising that the full science curriculum presented to students from pre-school through high school has often been characterized as "a mile wide and an inch deep" (Li, Klahr, & Siler, 2006; Santau et al., 2014). Thus, the challenge facing researchers interested in improving science education is to enhance the quality and generality of the answers to two related questions: What is scientific thinking? and How can it be taught? In this chapter, we attempt to answer the first question by presenting a brief summary of a broad framework that characterizes the essential aspects of scientific thinking and reviewing the developmental origins of scientific thinking. We answer the second question by describing a few representative examples of research on teaching science in specific domains, such as physics, biology, and earth sciences - organized according to the framework - and selected from the extensive literature on different ways to improve children's basic ability to think scientifically.

Learning to Solve Complex Propositions: Does knowledge of truth-values bootstrap modal operators? Bradley J. Morris (bjmorris@pitt.edu) University of Pittsburgh, LRDC, 3939 OiHara St. Pittsburgh, PA 15260 USA David Klahr (klahr@andrew.cmu.edu) Department of Psychology, Carnegie Mellon University Pittsburgh, PA 15213 USA Abstract Evaluating complex propositions requires evaluating truth- values and assigning modal operators. Previous research suggested that evaluating truth-values may be the key to assigning modal operators. This study placed 111 third and fifth grade children in one of three training conditions: no training, training truth-value assignment, and training truth- value and modal operator assignment. The results indicate that truth-value assignment training is sufficient to significantly improve childrenis evaluations of complex propositions. Reasoning with complex propositions (statements using AND, OR, NOT, IF) forms the basis of much higher-order thinking. There are three reasoning classes associated with processing propositions: evaluating a proposition as true or false (truth-values), evaluating whether a conclusion follows from the premises (validity), and judgments about possibility and necessity (modal operators). While much research has focused on judgments about validity (for a recent review see Markovits & Barrouillet, 2002), we will focus on a less-researched area: evaluating truth-values and assigning modal operators. The assignment of truth-values entails determining the truth or falsity of a statement (Johnson-Laird, 1983). The complexity of assigning truth-values depends on the number of elements being evaluated (i.e., how many items need to be evaluated) and the number of states under evaluation (the number of combinations and their associated truth-values). To better illustrate this point we will provide examples of two representative tasks. The first is sentence verification. In a sentence verification task, subjects typically are given a simple proposition (e.g., the star is white) to evaluate either with their existing knowledge or with some reference materials (e.g., picture of a white star). There are two possible values for each proposition: true or false. Because there is only one element under consideration, the evaluation is based on semantic properties (Roberts, Wood, & Gilmore 1994). The second type of task, evaluating complex propositions such as conjunctions and disjunctions, is more complicated than sentence verification because it requires the evaluation of two elements and four possible states. For example, when evaluating a conjunction (e.g., the star is white and the circle is blue), each single proposition has its own truth-value (white star; blue circle). Additionally, the statement as a whole is only true if both single propositions are true, thus there is only one of the four possible resulting combinations that results in an assignment of itruei for the entire statement. Assigning modal operators is determining when a statement is possible or necessary (Johnson-Laird, 1983). Like the assignment of truth-values, the assignment of modal operators differs in complexity depending on the nature of the task. Modal operators can be assigned on statements such as iA brother is a boyi in which by definition the statement is necessarily true (Miller, Custer, & Nassau, 2000). In this task the assignment is based on purely semantic factors. A more difficult task is assigning modal operators for complex propositions such as contradictions and tautologies. To determine that a tautology is always true (possible) and that a contradiction is always false (impossible) requires evaluating the semantics and syntax of the statement. That is, one must consider the truth-value of the connective and whether the semantic elements match any of the possible truth-values. Thus, a contradiction will always be false because one of the two propositions will always be false and an AND statement requires both elements to be true for the entire statement to be true. Development of Truth-values and Modal Operators Very little attention has been given to how children coordinate assigning truth-values and assigning modal operators. That is, are these processes related and if so, how? Perhaps gains on one phase do not correspond to gains on the other, thus we will call this possibility the separate phase hypothesis. Much previous research on either process has focused on a single process without examining the other typically reporting performance in one without respect to changes in the other (Ruffman, 1998; Braine & Rumain, 1981; Osherson & Markman, 1976; Paris, 1974). Thus, perhaps the two are not related. The only theoretical position that has examined both processes, mental logic, states that the two processes are part of a single inferential schema that is acquired with language (Braine & OiBrien, 1997). Once activated, these schemas fire a series of inferential rules that produce a

Learning scientists often use Donald Stokes's influential characterization of the relation between basic and applied research in his book Pasteur's Quadrant to suggest that most of the work in the learning sciences lies, or should lie, at the intersection of both types of research, that is, in the cell that is epitomized by Pasteur's work (use-inspired basic research) rather than the cells epitomized by either Bohr (pure basic) or Edison (pure applied). This essay makes three points: (a) Stokes had a broader view that also considered the temporal flow between and among the different cells in his famous diagram; (b) Stokes argued against the relative valuation of either type of research (basic or applied); and (c) the learning sciences currently contain exemplars of all four of the cells in Stokes's famous 2 × 2 matrix, and this diversity has enriched the field, and can continue to do so, as long as work in Pasteur's quadrant is not viewed as the only worthwhile type of learning sciences research.

AbstractFor almost a century, psychologists interested in cognitive development have devised empirical investigations to uncover the trajectory of scientific thinking, and they have explored a variety of methods for enriching children's understanding of scientific procedures and concepts. Topics have ranged from the origins of early childhood curiosity, through the development of everyday understanding of scientific phenomena, to the practices necessary to advance our knowledge about the natural world. In this chapter, we review the empirical research that has informed us about the ways in which the child is—and is not —like a scientist and the ways in which scientific thinking needs to be fostered and supported via educational, social, and cultural scaffolds. We define scientific thinking by drawing on two relatively distinct lines of inquiry, present a taxonomy to categorize these lines of inquiry and the cognitive skills involved, describe illustrative examples of experimental studies of scientific thinking, and then summarize what has been learned about the similarities and differences between children's scientific thinking and mature scientific thinking.

Cognitive Universals " Fiiirre! " If someone were to shout that while you were in the midst of reading this essay, you would, like most people, stop reading and look around the room for the source of the shout, or the fire itself. You would also consider whether or not the likelihood of a fire was sufficiently high to cause you to take appropriate action e.g., locate a fire extinguisher, call the fire department, or leave the room. Of course you have not been sitting around waiting for someone to yell " fire! ". You were engaged in some task (unrelated to fires) and yet were able to react to that stimulus. In contrast, if you were a police officer who was being trained at a firing range, you would be doing exactly that (i.e., waiting for someone to yell " fire! "), and when you heard " fire " you would pull the trigger on your weapon, rather than look around for the source of the cry. In both situations, two kinds of knowledge would be brought to bear in responding. On the one hand you would have facts (e.g., that someone yelled " fire " , that you are in the library or on a firing range, etc.), and on the other, you would have skills for how to respond (e.g., how to find an exit, how to fire a gun, etc.). Furthermore, responding could involve both pre-existing knowledge of both types (e.g., that you are on a firing range, or how to fire a gun), and newly acquired knowledge of both types (e.g., that someone yelled fire, or where to look for the exit in this particular room). This example illustrates the cognitive universals that builders of production system models believe to be fundamentally important aspects of intelligent behavior (both human and artificial). First, at any point in time there are always many possible actions from which one must be

An important, but as yet unresolved pedagogical question is whether discovery-oriented or direct instruction methods lead to greater learning and transfer. We address this issue in a study with 101 fourth and fifth grade students that contrasts two distinct instructional methods. One is a blend of discovery and direct instruction called Invent-then-Tell (IT), and the other is a version of direct instruction called Tell-then-Practice (TP). The relative effectiveness of these methods is compared in the context of learning a critical inquiry skill—the control-of-variables strategy. Previous research has demonstrated the success of IT over TP for teaching deep domain structures, while other research has demonstrated the superiority of direct instruction for teaching simple experimental design, a domain-general inquiry skill. In the present study, students in both conditions made equally large gains on an immediate assessment of their application and conceptual understanding of experimental design, and they also performed similarly on a test of far transfer. These results were fairly consistent across school populations with various levels of prior achievement and socioeconomic status. Findings suggest that broad claims about the relative effectiveness of these two distinct methods should be conditionalized by particular instructional contexts, such as the type of knowledge being taught.

The ability to use numerical evidence to revise beliefs about the physical world is an essential component of scientific reasoning that begins to develop in middle childhood. In 2 studies, we explored how data variability and consistency with participants' initial beliefs about causal factors associated with pendulums affected their ability to revise those beliefs. Children (9-11years old) and college-aged adults ran experiments in which they generated, recorded, and interpreted data so as to identify factors that might affect the period of a pendulum. In Study 1, several children and most adults used observed evidence to revise their initial understanding, but participants were more likely to change incorrect noncausal beliefs to causal beliefs than the reverse. In Study 2, we oriented participants toward either an engineering goal (to get an effect) or a science goal (to discover the causal structure of the domain) and presented them with variable data about potentially causal factors. Science goals produced more belief revision than engineering goals. Numerical data, when presented in context, with appropriate structure, can help children and adults reexamine their beliefs and initiate and support the process of conceptual change and robust scientific thinking.

One obstacle to understanding abstract concepts such as the “control of variables” strategy (CVS) is the tendency for learners to focus on surface rather than deep features in instructional materials. However, in tasks such as learning CVS, these same surface features may also support understanding, provided learners realize the underlying task goal. In this study, we explored the effect of surface features in textually described experiments on middle-school students’ understanding of CVS. We investigated whether the amount of surface detail—or surface-level concreteness—of experiments interacts with student tendency to focus on deep or surface features. As predicted, deep focusers showed better posttest performance when given all concrete examples (concrete-only condition) than when subsequent examples became more concrete (abstract-fading condition) or less concrete (concrete-fading condition). Concrete representations helped deep focusers understand the rationale for controlling variables. Although surface focusers who were given only concrete examples showed better understanding on some measures, they generally failed to develop complete explicit understanding of CVS, including its rationale. Consequently, surface focusers showed similarly poor transfer across conditions. Although students generally benefited from concrete representations, surface focusers may need more support to develop sufficiently coherent understandings that facilitate transfer. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Competing trends in early childhood education emphasize the need for strong curricular approaches and for unfettered exploration. We propose an approach to early learning that avoids this false dichotomy: guided play. Guided play takes advantage of children’s natural abilities to learn through play by allowing them to express their autonomy within a prepared environment and with adult scaffolding. We provide examples of how guided-play situations have been implemented in past work, as well as evidence that guided play is successful for education across a range of content—perhaps even more successful than other pedagogical approaches.

Competing trends in early childhood education emphasize the need for strong curricular approaches and for unfettered exploration. We propose an approach to early learning that avoids this false dichotomy: guided play. Guided play takes advantage of children's natural abilities to learn through play by allowing them to express their autonomy within a prepared environment and with adult scaffolding. We provide examples of how guided-play situations have been implemented in past work, as well as evidence that guided play is successful for education across a range of contentperhaps even more successful than other pedagogical approaches.

Children, especially in the preschool years, learn a tremendous amount through play. Research on guided play demonstrates how schools can couple a curriculum-centered preschool program with a developmentally appropriate pedagogical approach to classroom teaching. However, to fully test this claim, we need a clear definition of the term “guided play”: In guided play, the adult structures the play environment, but the child maintains control within that environment. Guided play can lead to dramatically better learning outcomes than didactic situations. If you tell them, children will learn. But if you guide them, children are more likely to actively explore and learn more.

In 2014, New York City implemented a badly needed and bold initiative: It vastly expanded its prekindergarten offerings, with the promise of serving every 4-year-old in the city. The goal is to boost every child's academic and school-readiness skills by using play. This initiative provides the perfect opportunity to consider the relationship between and learning, and the way in which intrinsically links them. Research on demonstrates how it is possible to couple a curriculum-centered preschool program with a developmentally appropriate pedagogical approach to classroom teaching. The notion of was first introduced to the literature in order to bridge the oft-discussed yet false dichotomy between and learning (Hirsh-Pasek & Golinkoff, 2011). Children, especially in the preschool years, learn a tremendous amount through play. However, to fully test this claim, we need a clear definition of guided play so we can distinguish it from other types of play. This article does that. It also explains how learning through occurs and why is most effective for achieving specified learning goals in areas such as reading readiness and number sense. Guided defined When we think of in young children, we usually think of free play, where children can do anything they want with any materials they want, without intervention from adults. There is mounting evidence that free is highly beneficial for various aspects of children's development (Hirsh-Pasek et al., 2008; Singer, Golinkoff, & Hirsh-Pasek, 2006). Children who more have better social skills (Singer & Singer, 2009), demonstrate better self-regulation (Diamond & Lee, 2011), and are more creative thinkers (Dansky, 1980). Although these links are largely correlational (Lillard et al., 2013), they suggest that has value for the development of well-adjusted, creative individuals who will be prepared to solve challenging problems. But not all is created equal. While free is a wonderful realm for children to explore their social and self-regulatory skills, research suggests that it might not be the best way to achieve educational outcomes (Fisher et al., 2010). It's easy to see why this is the case: Although children engaged in unfettered exploration could potentially stumble on the information that a teacher is trying to impart, it would lead to haphazard success at best. Guided is the best way to incorporate into early curricula without compromising educational goals, while allowing children to enjoy school. What's the difference between and free play? To help characterize this distinction, we offer a two-by-two grid (see Table 1), that categorizes types of according to who initiates them and who directs them. Free is both child-initiated and child-directed; children decide what to and how. When is both adult-initiated and adult-directed, it's really a form of direct instruction, where adults are telling children what actions to take. When is child-initiated but adult-directed, this is co-opted play: Children start out in charge, but adults take over and begin to set the agenda for the scenario, without providing space for children's autonomy. Finally, is a blend of adult initiation and child direction. In play, it's crucial that children direct the action because it gives them the autonomy to make decisions about what to do in any given moment. They are in control of what happens next and in what they wish to explore and how. Children do not just perceive that they are in control; in play, they truly can decide what to do next and how to respond. This is an important feature of because even children are sensitive to the difference between circumstances where they lead and those where they are given an educational experience dis guised as play--what one might call chocolate-covered broccoli. …

Two suggestions for instruction in chemical equilibrium are presented, along with the evidence that supports these suggestions. The first is to use diagrams to connect chemical reactions to the effects of reactions on concentrations. The second is the use of the majority and minority species (M&M) strategy to analyze chemical equilibrium systems. Two studies are presented in support of these suggestions.