Behavioral Economics and Developmental Science: A New Framework to Support Early Childhood Interventions

Public policies have actively responded to an emergent social and neuroscientific evidence base documenting the benefits of targeting services to children during the earliest period of their development. But problems of low utilization, inconsistent participation, and low retention continue to present themselves as challenges. Although most interventions recognize and address structural and psycho-social barriers to parent’s engagement, few take seriously the decision making roles of parents. Using insights from the behavioral sciences, we revisit assumptions about the presumed behavior of parents in a developmental context. We then describe ways in which this framework informs features of interventions that can be designed to augment the intended impacts of early development, education and care initiatives by improving parent engagement

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings

VulDeePecker: A Deep Learning-Based System for Vulnerability Detection

The automatic detection of software vulnerabilities is an important research problem. However, existing solutions to this problem rely on human experts to define features and often miss many vulnerabilities (i.e., incurring high false negative rate). In this paper, we initiate the study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features. Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection. In particular, we need to find representations of software programs that are suitable for deep learning. For this purpose, we propose using code gadgets to represent programs and then transform them into vectors, where a code gadget is a number of (not necessarily consecutive) lines of code that are semantically related to each other. This leads to the design and implementation of a deep learning-based vulnerability detection system, called Vulnerability Deep Pecker (VulDeePecker). In order to evaluate VulDeePecker, we present the first vulnerability dataset for deep learning approaches. Experimental results show that VulDeePecker can achieve much fewer false negatives (with reasonable false positives) than other approaches. We further apply VulDeePecker to 3 software products (namely Xen, Seamonkey, and Libav) and detect 4 vulnerabilities, which are not reported in the National Vulnerability Database but were "silently" patched by the vendors when releasing later versions of these products; in contrast, these vulnerabilities are almost entirely missed by the other vulnerability detection systems we experimented with

Test Case Generation for Object-Oriented Imperative Languages in CLP

Testing is a vital part of the software development process. Test Case Generation (TCG) is the process of automatically generating a collection of test cases which are applied to a system under test. White-box TCG is usually performed by means of symbolic execution, i.e., instead of executing the program on normal values (e.g., numbers), the program is executed on symbolic values representing arbitrary values. When dealing with an object-oriented (OO) imperative language, symbolic execution becomes challenging as, among other things, it must be able to backtrack, complex heap-allocated data structures should be created during the TCG process and features like inheritance, virtual invocations and exceptions have to be taken into account. Due to its inherent symbolic execution mechanism, we pursue in this paper that Constraint Logic Programming (CLP) has a promising unexploited application field in TCG. We will support our claim by developing a fully CLP-based framework to TCG of an OO imperative language, and by assessing it on a corresponding implementation on a set of challenging Java programs. A unique characteristic of our approach is that it handles all language features using only CLP and without the need of developing specific constraint operators (e.g., to model the heap)

Local search: A guide for the information retrieval practitioner

There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR

Preservice teachers’ observations of their mentors’ teaching strategies for differentiated learning

Tensions exist between teacher-centred and learner-centred approaches with constructivism as being favoured for learning in the 21st Century. There is little evidence of teaching strategies being used in the field for differentiating student learning. In addition, preservice teachers need to learn about teaching strategies for which observations of their mentor teachers can provide practical applications. This study explores 16 preservice teachers’ observations of their mentors’ teaching strategies over a four-week professional experience. They provided a minimum of five written observations during this period. Findings indicated that these preservice teachers observed their mentors’ practices and recorded four key teaching strategies used to differentiate learning, namely: (1) designating facilitators for students’ learning, including teacher, peers, parents, and support staff such as teachers aides, (2) managing student groups, (3) contexts for learning, and (4) using a range of teaching aids (visual, auditory, games) and resources. Preservice teachers’ observations of their mentor teachers indicated that they can commence at early stages for identifying teaching strategies and how they work for differentiating student learning

Health Information Technology and Caregiver Interaction: Building Healthy Ecosystems

This qualitative study explores the widely recognized role of the informal caregivers (ICGs) as key co-producers in the delivery of effective and sustainable healthcare systems. The central argument is that to enhance the quality of care in non-clinical settings and the healthcare ecosystem as a whole, developers of Health Information Technology (HIT) need to harness the knowledge and experiences of the ICGs to better align their products to practice. The paper has two aims: to improve the understandability of informal caregivers\u2019 role in non-traditional healthcare settings, and to identify and formulate valuable guidelines for the development of \u201cfit-for-use\u201d HIT solutions that acknowledge the needs of the ICGs