Evidence-Based Professional Development of Science Teachers in Two Countries

The focus of this collaborative research project of King?s College London, and the Weizmann Institute, Israel. project is on investigating the ways in which teachers can demonstrate accomplished teaching in a specific domain of science and on the teacher learning that is generated through continuing professional development programs (CPD) that lead towards such practice. The interest lies in what processes and inputs are required to help secondary school science teachers develop expertise in a specific aspect of science teaching. `It focuses on the design of the CPD programmes and examines the importance of an evidence-based approach through portfolioconstruction in which professional dialogue pathes the way for teacher learning. The set of papers highlight the need to set professional challenge while tailoring CPD to teachers? needs to create the environment in which teachers can advance and transform their practice. The cross-culture perspective added to the richness of the development and enabled the researchers to examine which aspects were fundamental to the design by considering similarities and differences between the domains

Constructing commons in the cultural environment

This Article sets out a framework for investigating sharing and resource-pooling arrangements for information- and knowledge-based works. We argue that adapting the approach pioneered by Elinor Ostrom and her collaborators to commons arrangements in the natural environment provides a template for examining the construction of commons in the cultural environment. The approach promises to lead to a better understanding of how participants in commons and pooling arrangements structure their interactions in relation to the environments in which they are embedded, in relation to information and knowledge resources that they produce and use, and in relation to one another

Synthesis of Attributed Feature Models From Product Descriptions: Foundations

Feature modeling is a widely used formalism to characterize a set of products (also called configurations). As a manual elaboration is a long and arduous task, numerous techniques have been proposed to reverse engineer feature models from various kinds of artefacts. But none of them synthesize feature attributes (or constraints over attributes) despite the practical relevance of attributes for documenting the different values across a range of products. In this report, we develop an algorithm for synthesizing attributed feature models given a set of product descriptions. We present sound, complete, and parametrizable techniques for computing all possible hierarchies, feature groups, placements of feature attributes, domain values, and constraints. We perform a complexity analysis w.r.t. number of features, attributes, configurations, and domain size. We also evaluate the scalability of our synthesis procedure using randomized configuration matrices. This report is a first step that aims to describe the foundations for synthesizing attributed feature models

Interactive situation modelling in knowledge intensive domains

Interactive Situation Modelling (ISM) method, a semi-methodological approach, is proposed to tackle issues associated with modelling complex knowledge intensive domains, which cannot be easily modelled using traditional approaches. This paper presents the background and implementation of ISM within a complex domain, where synthesizing knowledge from various sources is critical, and is based on the principles of ethnography within a constructivist framework. Although the motivation for the reported work comes from the application presented in the paper, the actual scope of the paper covers a wide range of issues related to modelling complex systems. The author firstly reviews approaches used for modelling knowledge intensive domains, preceded by a brief discussion about two main issues: symmetry of ignorance and system behaviour, which are often confronted when applying modelling approaches to business domains. The ISM process is then characterized and critiqued with lessons from an exemplar presented to illustrate its effectiveness

A review of domain adaptation without target labels

Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: how can a classifier learn from a source domain and generalize to a target domain? We present a categorization of approaches, divided into, what we refer to as, sample-based, feature-based and inference-based methods. Sample-based methods focus on weighting individual observations during training based on their importance to the target domain. Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure. Additionally, we review a number of conditions that allow for formulating bounds on the cross-domain generalization error. Our categorization highlights recurring ideas and raises questions important to further research.Comment: 20 pages, 5 figure