Improving Student Information Search

Metacognition is a set of active mental processes that allows users to monitor, regulate, and direct their personal cognitive strategies. Improving Student Information Search traces the impact of a tutorial on education graduate students’ problem-solving in online research databases. The tutorial centres on idea tactics developed by Bates that represent metacognitive strategies designed to improve information search outcomes. The first half of the book explores the role of metacognition in problem-solving, especially for education graduate students. It also discusses the use of metacognitive scaffolds for improving students’ problem-solving. The second half of the book presents the mixed method study, including the development of the tutorial, its impact on seven graduate students’ search behaviour and outcomes, and suggestions for adapting the tutorial for other users. Provides metacognitive strategies to improve students’ information search outcomes Incorporates tips to enhance database search skills in digital libraries Includes seminal studies on information behaviour

Research has identified the importance of helping students develop the ability to monitor their own comprehension and to make their thinking processes explicit, and indeed demonstrates that metacognitive teaching strategies greatly improve student engagement with course material. This book -- by presenting principles that teachers in higher education can put into practice in their own classrooms -- explains how to lay the ground for this engagement, and help students become self-regulated learners actively employing metacognitive and reflective strategies in their education. Key elements include embedding metacognitive instruction in the content matter; being explicit about the usefulness of metacognitive activities to provide the incentive for students to commit to the extra effort; as well as following through consistently. Recognizing that few teachers have a deep understanding of metacognition and how it functions, and still fewer have developed methods for integrating it into their curriculum, this book offers a hands-on, user-friendly guide for implementing metacognitive and reflective pedagogy in a range of disciplines. Offering seven practitioner examples from the sciences, technology, engineering and mathematics (STEM) fields, the social sciences and the humanities, along with sample syllabi, course materials, and student examples, this volume offers a range of strategies for incorporating these pedagogical approaches in college classrooms, as well as theoretical rationales for the strategies presented. By providing successful models from courses in a broad spectrum of disciplines, the editors and contributors reassure readers that they need not reinvent the wheel or fear the unknown, but can instead adapt tested interventions that aid learning and have been shown to improve both instructor and student satisfaction and engagement.

The fourth edition of this standard student text, Organizing Knowledge, incorporates extensive revisions reflecting the increasing shift towards a networked and digital information environment, and its impact on documents, information, knowledge, users and managers. Offering a broad-based overview of the approaches and tools used in the structuring and dissemination of knowledge, it is written in an accessible style and well illustrated with figures and examples. The book has been structured into three parts and twelve chapters and has been thoroughly updated throughout. Part I discusses the nature, structuring and description of knowledge. Part II, with its five chapters, lies at the core of the book focusing as it does on access to information. Part III explores different types of knowledge organization systems and considers some of the management issues associated with such systems. Each chapter includes learning objectives, a chapter summary and a list of references for further reading. This is a key introductory text for undergraduate and postgraduate students of information management.

As the role and practices of the academic library are evolving, so too is the relationship between the library and other areas of the university. This volume explores the library’s relationship with students, including the library-based learner, creating engaging classroom experiences, the library as an extension of the classroom, and more.

This book gathers selected papers from the KES-IDT-2020 Conference, held as a Virtual Conference on June 17–19, 2020. The aim of the annual conference was to present and discuss the latest research results, and to generate new ideas in the field of intelligent decision-making. However, the range of topics discussed during the conference was definitely broader and covered methods in e.g. classification, prediction, data analysis, big data, data science, decision support, knowledge engineering, and modeling in such diverse areas as finance, cybersecurity, economics, health, management and transportation. The Problems in Industry 4.0 and IoT are also addressed. The book contains several sections devoted to specific topics, such as Intelligent Data Processing and its Applications High-Dimensional Data Analysis and its Applications Multi-Criteria Decision Analysis – Theory and Applications Large-Scale Systems for Intelligent Decision-Making and Knowledge Engineering Decision Technologies and Related Topics in Big Data Analysis of Social and Financial Issues Decision-Making Theory for Economics

The seven-volume set LNCS 12137, 12138, 12139, 12140, 12141, 12142, and 12143 constitutes the proceedings of the 20th International Conference on Computational Science, ICCS 2020, held in Amsterdam, The Netherlands, in June 2020.* The total of 101 papers and 248 workshop papers presented in this book set were carefully reviewed and selected from 719 submissions (230 submissions to the main track and 489 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track Part III: Track of Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Track of Agent-Based Simulations, Adaptive Algorithms and Solvers; Track of Applications of Computational Methods in Artificial Intelligence and Machine Learning; Track of Biomedical and Bioinformatics Challenges for Computer Science Part IV: Track of Classifier Learning from Difficult Data; Track of Complex Social Systems through the Lens of Computational Science; Track of Computational Health; Track of Computational Methods for Emerging Problems in (Dis-)Information Analysis Part V: Track of Computational Optimization, Modelling and Simulation; Track of Computational Science in IoT and Smart Systems; Track of Computer Graphics, Image Processing and Artificial Intelligence Part VI: Track of Data Driven Computational Sciences; Track of Machine Learning and Data Assimilation for Dynamical Systems; Track of Meshfree Methods in Computational Sciences; Track of Multiscale Modelling and Simulation; Track of Quantum Computing Workshop Part VII: Track of Simulations of Flow and Transport: Modeling, Algorithms and Computation; Track of Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Track of Software Engineering for Computational Science; Track of Solving Problems with Uncertainties; Track of Teaching Computational Science; Track of UNcErtainty QUantIficatiOn for ComputationAl modeLs *The conference was canceled due to the COVID-19 pandemic.

A companion to the ASCD best-seller Improving Student Learning One Teacher at a Time, this breakthrough approach to supervision offers principals a simple, positive way to help teachers make the right adjustments in curriculum, instruction, assessment, and feedback -- the four areas of practice that make the most difference in how learners learn.

This book offers a coherent research-based overview and analysis of theories and practices in using data to improve student learning. It clarifies what 'use of data' means and differentiates the different levels of decision-making in education (relating to the system, district, school, classroom, or individual student). The relationship between data and decision-making is considered and various movements in the use of data to improve student learning are analysed, especially from the perspective of their assumptions and effects. This leads to a focus on effective educational decision-making as a social process requiring collaboration among all relevant participants. It also requires a clear understanding of educational aims, and these are seen to transcend what can be assessed by standardised tests. The consequences of this analysis for decision processes are explored and conclusions are drawn about what principles might best guide educational practice as well as what ambiguities remain. Throughout, the focus is on what existing research says about each of the issues explored.