It is a striking fact that in humans the greatest learnmg occurs precisely at that point in time- childhood- when the most dramatic maturational changes also occur. This report describes possible synergistic interactions between maturational change and the ability to learn a complex domain (language), as investigated in con-nectionist networks. The networks are trained to process complex sentences involving relative clauses, number agreement, and several types of verb argument structure. Training fails in the case of networks which are fully formed and ‘adultlike ’ in their capacity. Training succeeds only when networks begin with limited working memory and gradually ‘mature ’ to the adult state. This result suggests that rather than being a limitation, developmental restrictions on resources may constitute a necessary prerequisite for mastering certain complex domains. Specifically, successful learning may depend on starting small.

Scientific knowledge is dynamic in two senses: it changes and increases extremely rapidly, and it is thrust from the lab into the wider world and public forum almost as rapidly. This implies increasing demands on secondary school science education. Besides knowing key facts, concepts, and procedures, it is important for today’s students to understand the process by which the claims of science are generated, evaluated, and revised – an interplay between theoretical and empirical work (Dunbar & Klahr, 1989). The educational goals behind the work reported in this chapter are to improve students ’ understanding of this process and to facilitate students ’ acquisition of critical inquiry skills, while also meeting conventional subject matter learning objectives. In addition to the need to change what is taught, there are grounds to change how it is taught. Research shows that students learn better when they actively pursue understanding rather than passively

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Causal learning requires integrating constraints provided by domain-specific theories with domaingeneral statistical learning. In order to investigate the interaction between these factors, the authors presented preschoolers with stories pitting their existing theories against statistical evidence. Each child heard 2 stories in which 2 candidate causes co-occurred with an effect. Evidence was presented in the

This chapter introduces the field of learning sciences, and outlines some of its key findings in recent years. It explains that while the standard model of schooling was designed to prepare students for the industrial age, the global shift to the knowledge economy will require the rethinking of schooling in order to accommodate evolving needs. Several key findings of learning sciences research and how they align with the needs of the knowledge economy are explained.

Understanding how science students respond to anomalous data is essential to understanding knowledge acquisition in science classrooms. In this report, we present a detailed analysis of the ways in which scientists and science students respond to anomalous data. We postulate that there are seven distinct forms of response to anomalous data, only one of which is to accept the data and change theories. The other six responses involve discounting the data in various ways in order to protect the preinstructional theory. We analyze the factors that influence which of these seven forms of response a scientist or

Expert problem solving may be regarded as a process of understanding and modelling real world phenomena. Inductive thinking, empirical observations and deductive reasoning are crucial parts of this process. Experts and students differ in this respect, but they often show similarities in their problem-solving behaviour. Our research aims at identifying similarities and differences between experts and students in their mathematical problem solving with respect to argumentation and proof at the upper secondary level. Moreover, we will argue that adequate, as well as inadequate scientific models guiding the students ' argumentation are influenced by the practices in the mathematics classroom. Proof and Scientific Reasoning In the last few years there has been an intense discussion in mathematics education research on students ’ concepts of argumentation and proof. Both aspects are regarded as important for the understanding and application of mathematics. This positive attitude towards argumentation and proof is the result of an important debate among mathematics educators. It was Freudenthal who argued against geometrical proofs, particularly those in the form of classical Euclidean proofs. Accordingly, for many years proofs were regarded as superfluous in the mathematics classroom. It was conjectured that Euclidean proofs were far from providing any kind of mathematical insight, but were a means of initiation into a highly standardised and schematised type of argumentation cultivated only in school mathematics.

Two studies examined how parent explanation changes what children learn from everyday shared scientific thinking. In Study 1, children between ages 3- and 8-years-old explored a novel task solo or with parents. Analyses of children's performance on a subsequent posttest compared three groups: children exploring with parents who spontaneously explained to them; children exploring with parents who did not explain; and children exploring solo. Children whose parents had explained were most likely to have a conceptual as opposed to procedural understanding of the task. Study 2 examined the causal effect of parent explanations on children's understanding by randomly assigning children to conditions in which they were or were not provided explanation while exploring a novel task with an adult. Children who heard explanations were more likely to switch from procedural to conceptual understanding. Results are discussed with respect to the role of everyday explanation in the development of children's scientific thinking.

Years before encountering their first formal science lessons in elementary school, children may already be practicing scientific thinking on a weekly, if not daily, basis. In one recent survey, parents reported that their kindergartners engaged, on average, in more than 300 informal science education activities per year—watching science television shows, reading science-oriented books, and visiting museums and zoos (Korpan, Bisanz, Bisanz, Boehme & Lynch, 1997). This strikes us as a lot, but it is likely to pale in comparison to what young children may experience five years from now. Encouraged by findings suggesting that children’s out-of-school activities and learning environments are linked to motivation and success in the classroom (e.g., Gottfried, Fleming, & Gottfried, 1998), developers continue to expand the number of science-oriented museums, internet sites, books, and television shows specifically designed for young children. But what constitutes effective learning environments? What are the knowledge bases, processes, and practices that good informal science education

ABSTRACT: Current accounts of the development of scientific reasoning focus on individual children’s ability to coordinate the collection and evaluation of evidence with the creation of theories to explain the evidence. This observational study of parent–child interactions in a children’s museum demonstrated that parents shape and support children’s scientific thinking in everyday, nonobligatory activity. When children engaged an exhibit with parents, their exploration of evidence was observed to be longer, broader, and more focused on relevant comparisons than children who engaged the exhibit without their parents.