VI. SENSITIVE PERIODS

This chapter reviews sensitive periods in human brain development based on the literature on children raised in institutions. Sensitive experiences occur when experiences are uniquely influential for the development of neural circuitry. Because in humans, we make inferences about sensitive periods from evaluations of complex behaviors, we underestimate the occurrence of sensitive periods at the level of neural circuitry. Although we are most interested in complex behaviors, such as IQ or attachment or externalizing problems, many different sensitive periods at the level of circuits probably underlie these complex behaviors. Results from a number of studies suggest that across most, but not all, domains of development, institutional rearing limited to the first 4–6 months of life is associated with no significant increase risk for long‐term adverse effects relative to noninstitutionalized children. Beyond that, evidence for sensitive periods is less compelling, meaning that “the earlier the better” rule for enhanced caregiving is a reasonable conclusion at the current state of the science.     

II. DEVELOPMENT OF ADOPTED CHILDREN WITH HISTORIES OF EARLY ADVERSITY

This chapter first presents a review of research on the development of adopted children, focusing on meta‐analytic evidence and highlighting comparisons between adopted children with and without histories of early adversity. Some methodological issues arising from this literature are considered as well. Second, 7 longitudinal studies of adopted children's development are described, and the convergence of findings across the longitudinal studies and with the cross‐sectionally based meta‐analytic evidence is discussed. Third, the role of the adoptive family in supporting adopted children's development is explored.     

The views of Pasifika students in New Zealand about communicating mathematically

This paper focuses on the perspectives of Years 7 and 8 Pasifika students on mathematics learning, in particular their views about the communication of solution strategies with others (their peers), and their teacher. Pasifika students' ideas about the importance of communicating their mathematical reasoning and strategies to peers and teachers were analysed and themes identified. While most students were in agreement about the importance of explaining their thinking to others, there was considerable variation in the reasons they gave for their views. The findings are interpreted in relation to socio-cultural theory and recent writing about requirements for citizenship in the twenty-first century.     

Remote access laboratories in Australia and Europe

Remote access laboratories (RALs) were first developed in 1994 in Australia and Switzerland. The main purposes of developing them are to enable students to do their experiments at their own pace, time and locations and to enable students and teaching staff to get access to facilities beyond their institutions. Currently, most of the experiments carried out through RALs in Australia are heavily biased towards electrical, electronic and computer engineering disciplines. However, the experiments carried out through RALs in Europe had more variety, in addition to the traditional electrical, electronic and computer engineering disciplines, there were experiments in mechanical and mechatronic disciplines. It was found that RALs are now being developed aggressively in Australia and Europe and it can be argued that RALs will develop further and faster in the future with improving Internet technology. The rising costs of real experimental equipment will also speed up their development because by making the equipment remotely accessible, the cost can be shared by more universities or institutions and this will improve their cost-effectiveness. Their development would be particularly rapid in large countries with small populations such as Australia, Canada and Russia, because of the scale of economy. Reusability of software, interoperability in software implementation, computer supported collaborative learning and convergence with learning management systems are the required development of future RALs.     

On Subject Variations in Achievement Motivations: A Study in Business Subjects

Student achievement motivations are crucial in learning in two ways: as a determinant and an aim of learning. In this study, we focus on two related questions with regard to achievement motivations: to what extent are they subject-specific, and to what extent are they malleable? Answers to both questions are especially important when aiming to influence motivations. Malleability of motivation is studied by designing structural equation models that explain achievement motivation out of the most stable student characteristics one can think of: gender, and personality traits. Subject matter variability is studied by estimating these models for five main subject areas in a business program. The motivation construct we use is based on the expectancy-value model and distinguishes four different facets: cognitive competence, difficulty, task-value and affect. We find evidence for strong subject-specificity and considerable non-malleability of achievement motivation; part of that last aspect is a remarkably constant over-confidence gender gap present in the data of the calibration of competency beliefs and performance in all subject areas.     

Applying computerized-scoring models of written biological explanations across courses and colleges: prospects and limitations

Our study explored the prospects and limitations of using machine-learning software to score introductory biology students’ written explanations of evolutionary change. We investigated three research questions: 1) Do scoring models built using student responses at one university function effectively at another university? 2) How many human-scored student responses are needed to build scoring models suitable for cross-institutional application? 3) What factors limit computer-scoring efficacy, and how can these factors be mitigated? To answer these questions, two biology experts scored a corpus of 2556 short-answer explanations (from biology majors and nonmajors) at two universities for the presence or absence of five key concepts of evolution. Human- and computer-generated scores were compared using kappa agreement statistics. We found that machine-learning software was capable in most cases of accurately evaluating the degree of scientific sophistication in undergraduate majors’ and nonmajors’ written explanations of evolutionary change. In cases in which the software did not perform at the benchmark of “near-perfect” agreement (kappa > 0.80), we located the causes of poor performance and identified a series of strategies for their mitigation. Machine-learning software holds promise as an assessment tool for use in undergraduate biology education, but like most assessment tools, it is also characterized by limitations.     

Learning of musculoskeletal ligament stress testing in a gross anatomy laboratory

Human anatomy in physical therapy programs is a basic science course serving as a foundation for subsequent clinical courses. Integration of anatomy with a clinical emphasis throughout a curriculum provides opportunities for reinforcement of previously learned material. Considering the human cadaver laboratory as a fixed cost to our program, we sought opportunities to add value to the resource via vertical integration into a clinical skills course taught later in the curriculum. We designed an opportunity for second-year physical therapy students to revisit the human anatomy laboratory to study select clinical musculoskeletal tests and the associated anatomy in a clinically relevant context. Students performed select orthopedic ligament test on human cadavers, then incised specific structures and repeated the tests. Students were able to feel and visualize the function of pertinent anatomy associated with the clinical tests. Ninety-five percent of respondents reported that the ligament stress testing experience enhanced their understanding of orthopedic clinical tests with 91% reporting an enhanced understanding of anatomy related to specific clinical tests. Likewise, the experience was perceived as enjoyable and valuable with 86% of respondents reporting the experience as enjoyable and 100% responding the experience should continue as part of the curriculum.     

Stabilizability of complex complex-valued memristive neural networks using non-fragile sampled-data control

This paper investigates the stability and stabilizability of complex-valued memristive neural networks (CVMNNs) with random time-varying delays via non-fragile sampled-data control. Taking the influence of gain fluctuations into account, a non-fragile sampled-data controller is designed for CVMNNs. Compared with the existing control schemes, the one here is more applicable and can effectively save the communication resources. The assumption on activation functions of CVMNNs is relaxed by only needing the complex-valued activation functions satisfying the Lipschitz condition. By constructing a suitable Lyapunov–Krasovskii functional (LKF), new stability and stabilizability criteria are derived for CVMNNs. Different from the existing results with the maximum absolute values of memristive connection weights, our ones are based on the average values of the maximum and minimum of the memristive connection weights. Finally, numerical simulations are given to validate the effectiveness of the theoretical results.     

Image watermarking using separable fractional moments of Charlier–Meixner

This paper proposes a new set of discrete orthogonal separable moments of fractional order, named Fractional Charlier–Meixner Moments (FrCMMs). The latter are constructed from fractional Charlier polynomials (FrCPs) and fractional Meixner polynomials (FrMPs) proposed in this paper. The proposed FrMPs are constructed algebraically using the spectral decomposition of classical Meixner polynomials and singular value decomposition (SVD). The proposed FrCMMs generalize the separable moments of Charlier–Meixner of integer order (CMMs). In addition, FrCMMs are characterized by the polynomial parameters and by the fractional orders of the two fractional kernel functions of Charlier and Meixner, which allows them to be used efficiently for different applications such as local and global image reconstruction and image watermarking. Based on the proposed FrCMMs, a new watermarking scheme for copyright protection of digital images in the transform domain is proposed where the watermark is embedded in the FrCMM coefficients leading to an efficient watermarking scheme in terms of imperceptibility, robustness and security. The performances of the proposed moments are evaluated and compared with discrete fractional moments existing in the literature and with classical separable moments of integer order.     

An unsupervised heuristic-based approach for bibliographic metadata deduplication

Digital libraries of scientific articles contain collections of digital objects that are usually described by bibliographic metadata records. These records can be acquired from different sources and be represented using several metadata standards. These metadata standards may be heterogeneous in both, content and structure. All of this implies that many records may be duplicated in the repository, thus affecting the quality of services, such as searching and browsing. In this article we present an approach that identifies duplicated bibliographic metadata records in an efficient and effective way. We propose similarity functions especially designed for the digital library domain and experimentally evaluate them. Our results show that the proposed functions improve the quality of metadata deduplication up to 188% compared to four different baselines. We also show that our approach achieves statistical equivalent results when compared to a state-of-the-art method for replica identification based on genetic programming, without the burden and cost of any training process.