Number sense : the underpinning understanding for early quantitative literacy

The fundamental meaning of Quantitative Literacy (QL) as the application of quantitative knowledge or reasoning in new/unfamiliar contexts is problematic because how we acquire knowledge, and transfer it to new situations, is not straightforward. This article argues that in the early development of QL, there is a specific corpus of numerical knowledge which learners need to integrate into their thinking, and to which teachers should attend. The paper is a rebuttal to historically prevalent (and simplistic) views that the terrain of early numerical understanding is little more than simple counting devoid of cognitive complexity. Rather, the knowledge upon which early QL develops comprises interdependent dimensions: Number Knowledge, Counting Skills and Principles, Nonverbal Calculation, Number Combinations and Story Problems - summarised as Number Sense. In order to derive the findings for this manuscript, a realist synthesis of recent Education and Psychology literature was conducted. The findings are of use not only when teaching very young children, but also when teaching learners who are experiencing learning difficulties through the absence of prerequisite numerical knowledge. As well distilling fundamental quantitative knowledge for teachers to integrate into practice, the review emphasises that improved pedagogy is less a function of literal applications of reported interventions, on the grounds of perceived efficacy elsewhere, but based in refinements of teachers' understandings. Because teachers need to adapt instructional sequences to the actual thinking and learning of learners in their charge, they need knowledge that allows them to develop their own theoretical understanding rather than didactic exhortations

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Developing Social-Emotional Skills for the Labor Market: The PRACTICE Model

Although there is a general agreement in the literature of the importance of social-emotional skills for labor market success, there is little consensus on the specific skills that should be acquired or how and when to teach them. The psychology, economics, policy research, and program implementation literatures all touch on these issues, but they are not sufficiently integrated to provide policy direction. The objective of this paper is to provide a coherent framework and related policies and programs that bridge the psychology, economics, and education literature, specifically that related to skills employers value, non-cognitive skills that predict positive labor market outcomes, and skills targeted by psycho-educational prevention and intervention programs. The paper uses as its base a list of social-emotional skills that employers value, classifies these into eight subgroups (summarized by PRACTICE), then uses the psychology literature—drawing from the concepts of psycho-social and neuro-biological readiness and age-appropriate contexts—to map the age and context in which each skill subset is developed. The paper uses examples of successful interventions to illustrate the pedagogical process. The paper concludes that the social-emotional skills employers value can be effectively taught when aligned with the optimal stage for each skill development, middle childhood is the optimal stage for development of PRACTICE skills, and a broad international evidence base on effective program interventions at the right stage can guide policy makers to incorporate social-emotional learning into their school curriculum

Computational Theories of Curiosity-Driven Learning

What are the functions of curiosity? What are the mechanisms of curiosity-driven learning? We approach these questions about the living using concepts and tools from machine learning and developmental robotics. We argue that curiosity-driven learning enables organisms to make discoveries to solve complex problems with rare or deceptive rewards. By fostering exploration and discovery of a diversity of behavioural skills, and ignoring these rewards, curiosity can be efficient to bootstrap learning when there is no information, or deceptive information, about local improvement towards these problems. We also explain the key role of curiosity for efficient learning of world models. We review both normative and heuristic computational frameworks used to understand the mechanisms of curiosity in humans, conceptualizing the child as a sense-making organism. These frameworks enable us to discuss the bi-directional causal links between curiosity and learning, and to provide new hypotheses about the fundamental role of curiosity in self-organizing developmental structures through curriculum learning. We present various developmental robotics experiments that study these mechanisms in action, both supporting these hypotheses to understand better curiosity in humans and opening new research avenues in machine learning and artificial intelligence. Finally, we discuss challenges for the design of experimental paradigms for studying curiosity in psychology and cognitive neuroscience. Keywords: Curiosity, intrinsic motivation, lifelong learning, predictions, world model, rewards, free-energy principle, learning progress, machine learning, AI, developmental robotics, development, curriculum learning, self-organization.Comment: To appear in "The New Science of Curiosity", ed. G. Gordon, Nova Science Publisher

Talent Development in Achievement Domains: A Psychological Framework for Within- and Cross-Domain Research

Achievement in different domains, such as academics, music, or visual arts, plays a central role in all modern societies. Different psychological models aim to describe and explain achievement and its development in different domains. However, there remains a need for a framework that guides empirical research within and across different domains. With the talent-development-in-achievement-domains (TAD) framework, we provide a general talent-development framework applicable to a wide range of achievement domains. The overarching aim of this framework is to support empirical research by focusing on measurable psychological constructs and their meaning at different levels of talent development. Furthermore, the TAD framework can be used for constructing domain-specific talent-development models. With examples for the application of the TAD framework to the domains of mathematics, music, and visual arts, the review provided supports the suitability of the TAD framework for domain-specific model construction and indicates numerous research gaps and open questions that should be addressed in future research