Children's acquisition of science terms: simple exposure is insufficient

The ability of school children (N = 233) to acquire new scientific vocabulary was examined. Children from two age groups (M = 4;8 and M = 6;5) were introduced to previously unknown words in an educational video. Word knowledge was assessed through accuracy and latency for production and comprehension over a nine month period. A draw and write task assessed acquisition of domain knowledge. Word learning was poorer than has previously been reported in the literature, and subject to influences of word type (domain-specificity) and word class. The results indicate that the acquisition of scientific terms is a complex process moderated by lexical, semantic and pragmatic factors

Beyond rules: The next generation of expert systems

The PARAGON Representation, Management, and Manipulation system is introduced. The concepts of knowledge representation, knowledge management, and knowledge manipulation are combined in a comprehensive system for solving real world problems requiring high levels of expertise in a real time environment. In most applications the complexity of the problem and the representation used to describe the domain knowledge tend to obscure the information from which solutions are derived. This inhibits the acquisition of domain knowledge verification/validation, places severe constraints on the ability to extend and maintain a knowledge base while making generic problem solving strategies difficult to develop. A unique hybrid system was developed to overcome these traditional limitations

Skillful Coaching: New Directions In Teaching Health Assessment

The purpose of this study was to examine differences in nursing student’s assessment skills before and after the implementation of a cognitive apprenticeship didactic approach including think-aloud and critical dialogue.  In the nursing care of clients, a through and accurate holistic health assessment is essential.  The content of the assessment task is taught in all nursing programs.  Students must be competent not only in performing the examination but also in evaluating the information obtained and integrating it with the health history and lab findings.  This extensive criteria makes assessment a challenging skill to master.  Problems arise when students in nursing attempt to apply the theoretical assessment approaches to the clinical setting with real clients.  Consequently, student’s performance outcomes are often unsatisfactory, as the teaching format usually focuses on the students’ acquisition of domain knowledge and psychomotor skills rather than on shaping the thinking processes involved.  Students’ common problems with physical assessment shows that they struggle to manipulate the abstract positions involved

A rule-general abductive learning by rough sets

In real-world tasks, there is usually a large amount of unlabeled data and labeled data. The task of combining the two to learn is known as semi-supervised learning. Experts can use logical rules to label unlabeled data, but this operation is costly. The combination of perception and reasoning has a good effect in processing such semi-supervised tasks with domain knowledge. However, acquiring domain knowledge and the correction, reduction and generation of rules remain complex problems to be solved. Rough set theory is an important method for solving knowledge processing in information systems. In this paper, we propose a rule general abductive learning by rough set (RS-ABL). By transforming the target concept and sub-concepts of rules into information tables, rough set theory is used to solve the acquisition of domain knowledge and the correction, reduction and generation of rules at a lower cost. This framework can also generate more extensive negative rules to enhance the breadth of the knowledge base. Compared with the traditional semi-supervised learning method, RS-ABL has higher accuracy in dealing with semi-supervised tasks

A Very Brief Introduction to Machine Learning With Applications to Communication Systems

Given the unprecedented availability of data and computing resources, there is widespread renewed interest in applying data-driven machine learning methods to problems for which the development of conventional engineering solutions is challenged by modelling or algorithmic deficiencies. This tutorial-style paper starts by addressing the questions of why and when such techniques can be useful. It then provides a high-level introduction to the basics of supervised and unsupervised learning. For both supervised and unsupervised learning, exemplifying applications to communication networks are discussed by distinguishing tasks carried out at the edge and at the cloud segments of the network at different layers of the protocol stack

Requirements for Information Extraction for Knowledge Management

Knowledge Management (KM) systems inherently suffer from the knowledge acquisition bottleneck - the difficulty of modeling and formalizing knowledge relevant for specific domains. A potential solution to this problem is Information Extraction (IE) technology. However, IE was originally developed for database population and there is a mismatch between what is required to successfully perform KM and what current IE technology provides. In this paper we begin to address this issue by outlining requirements for IE based KM

Work Experience and VET: Insights from the Connective Typology and the Recontextualisation Model

The chapter compares two models of work experience – connective typology of work experience and recontextualisation of knowledge model – and uses the term work experience to refer to the way that young people enrolled in both school- and apprenticeship-based VET learn to relate their experience of education as represented by the acquisition of domain knowledge and their experience of work as represented by occupational values, skill and knowledge to one another. The common link between the two models is that they accept the existence of a mediated relationship between education and work. The former explores this relationship from a boundary-crossing perspective, focusing on learners’ movement between education and work, and identifies the outcomes associated with different models of work experience. The latter focuses on the interplay between the manifestation of knowledge in the contexts of education and work and learners’ movement within and between both contexts. It differs from the connective typology, because it takes account of the mediated nature of the contexts of education and work as well as the process of learning through work experience. The chapter concludes by using the concept of recontextualisation to highlight how digital and mobile technologies could serve as resources to facilitate learning through work experience in school- and apprenticeship-based VET