Excerpt: The papers that appear in this special issue bring together researchers working on probabilistic models of cognition with developmental psychologists, to consider how “rational constructivism” could shed light on some of the challenges of understanding cognitive development. Our goal in collecting these papers together is to illustrate that this new approach to the study of cognitive and language development has already shown a lot of promise – both computational modeling and empirical work have opened up new directions for research, and have contributed to theoretical and empirical advances in understanding learning and inference from infancy to adulthood. The rational constructivist view embodies two key ideas: one is the commitment that the learning mechanisms that best characterize learning and development from infants to adults are a set of rational, inferential, and statistical mechanisms that underlies probabilistic models of cognition. The application of these domain-general mechanisms may give rise to domain-specific knowledge. The second is to call into question both the nativist characterization of innate conceptual primitives (e.g., is object or agent an innate concept?), and the empiricist’s characterization of a newborn infant with nothing but perceptual primitives and associative learning mechanisms. It is an open question how best to think about the initial state of a human learner. Perhaps in addition to a set of perceptual (proto-conceptual?) primitives, the infant also has the capacity to represent variables, to track individuals, to form categories and higher-order units through statistical analyses, and maybe even the representational capacity for logical operators such as and/or/all/some – these capacities enable the infant to acquire more complex concepts and new learning biases. As such, this view departs from the traditional Piagetian view of development in at least two ways – development does not progress through stages, driven by qualitative changes in the child’s logical capacities, and development does not start with sensory-motor primitives and a lack of differentiation between the child and the world. Instead, the construction of new concepts and new learning biases is driven by rational inferential learning processes. At the moment, there is by no means any consensus on these issues. With further empirical and computational work, a more detailed explication will emerge.

What is the nature of human learning, and what insights can be gained from understanding early learning in infants and young children? This is an important question for understanding the human mind, the origins of knowledge, scientific reasoning, and how to best structure our educational environment. In this article, we argue for a new approach to cognitive development: rational constructivism. This view characterizes the child as a rational constructive learner, and it sees early learning as rational, statistical, and inferential. Empirical evidence for this approach has been accumulating rapidly, and a set of domain-general statistical and inferential mechanisms have been uncovered to explain why infants and young children learn so fast and so well.

Key words: rational constructivism, learning mechanisms, cognitive development

The authors in this chapter focus on a case study of how object representations in infants interact with early word learning, particularly the nature of the so-called ‘shape bias’. A short review of the controversies in this subfield is used to illustrate the two dominant views of cognitive development, which can be roughly classified as nativist or empiricist. Also presented are theoretical arguments and new empirical evidence for a rational constructivist view of cognitive development. The authors’ goal in this chapter is to argue for a new approach to the study of cognitive development, one that is strongly committed to both innate concepts and representations, as well as powerful inductive learning mechanisms. In addition to discussing the ‘shape bias’ and how it relates to object representations, generality of the approach is briefly discussed.

Key words: object representations, early word learning, shape bias, cognitive development, nativist, empiricist, rational constructivist, innate concepts, inductive learning mechanisms, infants

Although career adaptability and constructivist beliefs both capture important aspects of career construction, previous research has predominantly focused on career adaptability and ignored the importance of constructivist beliefs. Drawing on career construction theory and decision-making science, the current study proposes two factors (i.e., satisficing decision and agentic creation) of constructivist beliefs in career decision-making (CBCD) and develops and initially validates a scale measuring the two factors. Study 1 develops the CBCD Scale and supports the two-factor structure of the CBCD through exploratory factor analysis. Study 2 supports the internal consistency reliability of the CBCD and cross-validates the two-factor structure of the CBCD through confirmatory factor analysis. Additionally, Study 2 finds support for the incremental predictions of the CBCD for career indecision and career decision ambiguity management over and beyond career adaptability. The theoretical and practical implications of the CBCD are discussed, as are the limitations and suggestions for future research.

How can a teacher apply ideas from the constructivist learning model to classroom teaching? This article reviews the main ideas of the constructivist learning theory as well as the epistemological shift in the view of scientific knowledge, and suggests ways of applying these ideas to science teaching. Yager presents a teaching model developed by the National Center for Improving Science Education that is based on the constructivist learning model and includes four aspects: invitation, exploration, proposed explanation and solution, and taking action. Specific strategies that science teachers can use to help students construct their own meaning are listed. In addition, the article contains a self-check instrument that can be used to determine the extent to which a teacher is basing his or her practice on constructivist learning theory. Yager concludes with the recommendation that this model should also be used in preparing new teachers.

This chapter discusses a formal model of human and machine learning called participatory learning. This model allows the representation of machine learning in a constructivist framework. In this model, the learner’s previous beliefs play an important role in the assimilation of further information. A central aspect of the theory is the degree of compatibility between observations and belief. In a constructivist theory, learning is a bootstrap process. The name participatory learning highlights the fact that the learner’s current knowledge of the subject participates intimately in the learning process. Central to participatory learning is the idea that an exogenous observation has the greatest impact on learning when the observation is largely compatible with the present belief system. In particular, observations in conflict with current core constructs or strongly held beliefs are discounted. The role of arousal or anxiety can be thought of as salient and/or massed negative feedback.

The paper describes results of a teaching experiment with five high school (Year 10 and 11) students. Four qualitative characteristics were established: the first step of solution, main information extracted from the problem, generalisation from a problem and completion of solution. From these characteristics the corresponding quantitative indices were introduced and analysed. The structure of two of them, specific SFS and common SHP,are given in detail. Investigation of quantitative indices and their qualitative characteristics gives an opportunity to find out more about interrelations between different stages of the problem-solving process.

This article provides an overview of constructivism and its implications for classroom practices. To that end, it first describes the basic features of constructivism along with its major forms or variations. It then elucidates the constructivist view of knowledge, learning, teach- ing, and the relationship among these constructs. More specifically, it explains the assumptions and principles of constructivist pedagogy, bringing to the fore its core characteristics that differ fundamentally from other instructional paradigms. Last, the article presents how con- structivism as a learning theory can guide the process of learning and teaching in real classroom settings.