Search results

To examine how reading in electronic formats differs from traditional reading of print.

Concepts about digital print are discussed alongside research studies in fields related to multisensory technologies and electronic means of communication. A model of online reading is proposed integrating aspects of information foraging theory. Pedagogical applications are needed to integrate e-reading theory within classrooms.

With the varied text structures, directionality concerns, and interactive text features, our attention must turn to the theoretical foundations that underpin digital literacy learning today. Online foraging schemes can explain how information is sought and retrieved when reading new information from digital mediums.

Teachers must address the current, digital literacy needs of their students, thus preparing them for challenges in the 21st century. Varying text structures within digital formats as well as providing as-needed facilitation are the scaffolds that students need today. Using technologies such as digital games, tools, and contexts advances the mission of resource-based teaching and learning.

Tremendous measure of data lakes with the exponential mounting rate is produced by the present healthcare sector. The information from differing sources like electronic wellbeing record, clinical information, streaming information from sensors, biomedical image data, biomedical signal information, lab data, and so on brand it substantial as well as mind-boggling as far as changing information positions, which have stressed the abilities of prevailing regular database frameworks in terms of scalability, storage of unstructured data, concurrency, and cost. Big data solutions step in the picture by harnessing these colossal, assorted, and multipart data indexes to accomplish progressively important and learned patterns. The reconciliation of multimodal information seeking after removing the relationship among the unstructured information types is a hotly debated issue these days. Big data energizes in triumphing the bits of knowledge from these immense expanses of information. Big data is a term which is required to take care of the issues of volume, velocity, and variety generally seated in the medicinal services data. This work plans to exhibit a survey of the writing of big data arrangements in the medicinal services part, the potential changes, challenges, and accessible stages and philosophies to execute enormous information investigation in the healthcare sector. The work categories the big healthcare data (BHD) applications in five broad categories, followed by a prolific review of each sphere, and also offers some practical available real-life applications of BHD solutions.

This chapter reviews published studies of the use of pictorial information. Examining image user studies surfaces several research questions often addressed by this body of work, as well as some frequently encountered problems. These questions and problems organize this survey of the literature. Image user studies were included in two valuable reviews of digital image research and development, published by Christie Stephenson and Corinne Jörgensen in 1999 (Jörgensen, 1999; Stephenson, 1999). This overview considers research since that time, focusing on assessment that was not targeted at a single system or service. While attempting to incorporate some interesting research from the information and educational technology communities, this discussion of image delivery as an aspect of digital library development limits coverage of those important literatures.

Purpose — This chapter illustrates and explains the ambiguity and vagueness of the term social search and aims at describing and classifying the heterogeneous landscape of social search implementations on the WWW.

Methodology/approach — We have looked at different definitions as well as the context of social search by carrying out an extensive literature review, and tried to unify and enhance existing ideas and concepts. Our definition of social search is illustrated by a general review of existing social search engines, which are analyzed and described by their specific features and social aspects.

Findings — The chapter presents a discussion of social search as well as a comparison of existing social search engines.

Social implications — The definition of social search and the comparison of social search engines summarize the many ways people can search the web together and allow for an assessment of future developments in this area.

Originality/value of paper — Although different attempts to define social search have been made in the past, we present an argumentation that unifies some existing definitions and which is different from other interpretations of the social search concept. We present an overview and a comparison of the different genres of social search engines.

Neural networks, which provide the basis for deep learning, are a class of machine learning methods that are being applied to a diverse array of fields in business, health, technology, and research. In this chapter, we survey some of the key features of deep neural networks and aspects of their design and architecture. We give an overview of some of the different kinds of networks and their applications and highlight how these architectures are used for business applications such as recommender systems. We also provide a summary of some of the considerations needed for using neural network models and future directions in the field.

Purpose — To develop methodologies to evaluate search engines according to an individual's preference in an easy and reliable manner, and to formulate user-oriented metrics to compare freshness and duplication in search results.

Design/methodology/approach — A personalised evaluation model for comparing search engines is designed as a hierarchy of weighted parameters. These commonly found search engine features and performance measures are given quantitative and qualitative ratings by an individual user. Furthermore, three performance measurement metrics are formulated and presented as histograms for visual inspection. A methodology is introduced to quantitatively compare and recognise the different histogram patterns within the context of search engine performance.

Findings — Precision and recall are the fundamental measures used in many search engine evaluations due to their simplicity, fairness and reliability. Most recent evaluation models are user oriented and focus on relevance issues. Identifiable statistical patterns are found in performance measures of search engines.

Research limitations/implications — The specific parameters used in the evaluation model could be further refined. A larger scale user study would confirm the validity and usefulness of the model. The three performance measures presented give a reasonably informative overview of the characteristics of a search engine. However, additional performance parameters and their resulting statistical patterns would make the methodology more valuable to the users.

Practical implications — The easy-to-use personalised search engine evaluation model can be tailored to an individual's preference and needs simply by changing the weights and modifying the features considered. A user is able to get an idea of the characteristics of a search engine quickly using the quantitative measure of histogram patterns that represent the search performance metrics introduced.

Originality/value — The presented work is considered original as one of the first search engine evaluation models that can be personalised. This enables a Web searcher to choose an appropriate search engine for his/her needs and hence finding the right information in the shortest time with the least effort.

Purpose – The purpose of the chapter is to provide an analytical overview of information research in the United Kingdom and of the role of the Research Assessment Exercises (RAE) in shaping the form and structure of that research.

Design/methodology/approach – The approach adopted is a detailed content analysis of the submissions made to the last UK RAE. This analysis is carried out in relation to four broad subject categorisations, and specific analysis of accounts of research carried out in the departments and research groups.

Findings – The RAE have played a key role in promoting research specialisms in library and information studies (LIS) research in the United Kingdom. The former general approach to research in information studies has been replaced by more focused research activities carried out in a variety of research groups spread across a diverse range of disciplines and departments, from LIS, to business and management, information systems, and computing and engineering.

Research implications – The prospects for general LIS research departments may be increasingly limited, as research becomes concentrated in sub-groups within larger organisational structures, subverting both departmental lines and conventional subject boundaries.

Originality/value – This overview provides a novel synthesis of information research in the United Kingdom in relation to four broad categories of research in information studies and information science, information management and social informatics, information systems and information interaction, and social computing and computational informatics. The account brings together a fragmented field of research in a compact and intelligible form.