The value and valuing of continuing professional development: current dilemmas,future directions and the case for action research

This paper explores and challenges the rationale for current, mainstream approaches to teachers’ continuing professional development (CPD) within schooling systems. Such approaches are significantly influenced by neoliberal and managerial pressures, evident in advocacy for generic, individualistic models of teacher learning, often focused on specific state-sanctioned domains. The paper draws upon a précis of recent action research literature, and empirical research from Sweden, to argue for an alternative paradigm, based on the practices and principles of participatory and collaborative action research. Action research is not presented as a simplistic ‘method’ which can be ‘applied’ regardless of context, but is explicitly focused on situated, specific, local sites. While more managerial and neoliberal practices can close down debates necessary for effecting real improvements in practice, evidence suggests action research, in its emancipatory iterations, enables a rich conception of educational practice which cannot be ‘managed’ into existence by a simplistic application of ‘what works.’     

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.     

Autonomous cleaning system for Dubai International Airport

This paper outlines the development of a robotic system designed to clean the outer surface of elliptic tapered structures such as Dubai airport concourse. The designed system consists of two identical cleaning satellites connected to a main unit and provides cleaning coverage up to 83° tilt from the horizontal surface of the concourse. The controller design, sensors, localization, and employed communication techniques are presented in this paper along with the final results of the implemented prototype. The system features redundant locomotion to ensure safe and reliable operation while maintaining autonomy and flexibility.     

Demand articulation in emerging technologies: Intermediary user organisations as co-producers?

User involvement is assumed to be beneficial to innovation processes. Intermediary user organisations contribute to articulating societal demands for innovations. However, the learning processes inside these organisations are still not understood well. Therefore, this paper empirically investigates intermediaries using an event history approach. It yields characteristic learning mechanisms, e.g. concerning the management of expectations or actively building a case. If intermediaries overcome challenges regarding positioning, representation and the level of proactivity, they can play a precarious role in demand articulation in the context of new technologies.