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A Choice Outstanding Academic Title for 2014! Sponsored by the Association of College and Research Libraries (ACRL), this one-of-a-kind book demonstrates the best tools, resources, and techniques for discovering, selecting, and integrating interactive open educational resources (OERs) into the teaching and learning process. The author examines many of the best repositories and digital library websites for finding high quality materials, explaining in depth the best practices for effectively searching these repositories and the various methods for evaluating, selecting, and integrating the resources into the instructor’s curriculum and course assignments, as well as the institution’s learning management system.
2018 Outstanding Academic Title, Choice Ambitious Science Teaching outlines a powerful framework for science teaching to ensure that instruction is rigorous and equitable for students from all backgrounds. The practices presented in the book are being used in schools and districts that seek to improve science teaching at scale, and a wide range of science subjects and grade levels are represented. The book is organized around four sets of core teaching practices: planning for engagement with big ideas; eliciting student thinking; supporting changes in students’ thinking; and drawing together evidence-based explanations. Discussion of each practice includes tools and routines that teachers can use to support students’ participation, transcripts of actual student-teacher dialogue and descriptions of teachers’ thinking as it unfolds, and examples of student work. The book also provides explicit guidance for “opportunity to learn” strategies that can help scaffold the participation of diverse students. Since the success of these practices depends so heavily on discourse among students, Ambitious Science Teaching includes chapters on productive classroom talk. Science-specific skills such as modeling and scientific argument are also covered. Drawing on the emerging research on core teaching practices and their extensive work with preservice and in-service teachers, Ambitious Science Teaching presents a coherent and aligned set of resources for educators striving to meet the considerable challenges that have been set for them.
Employ cognitive theory in the classroom every day Research into how we learn has opened the door for utilizing cognitive theory to facilitate better student learning. But that's easier said than done. Many books about cognitive theory introduce radical but impractical theories, failing to make the connection to the classroom. In Small Teaching, James Lang presents a strategy for improving student learning with a series of modest but powerful changes that make a big difference—many of which can be put into practice in a single class period. These strategies are designed to bridge the chasm between primary research and the classroom environment in a way that can be implemented by any faculty in any discipline, and even integrated into pre-existing teaching techniques. Learn, for example: How does one become good at retrieving knowledge from memory? How does making predictions now help us learn in the future? How do instructors instill fixed or growth mindsets in their students? Each chapter introduces a basic concept in cognitive theory, explains when and how it should be employed, and provides firm examples of how the intervention has been or could be used in a variety of disciplines. Small teaching techniques include brief classroom or online learning activities, one-time interventions, and small modifications in course design or communication with students.
This book – in conjunction with the volumes LNCS 8588 and LNBI 8590 – constitutes the refereed proceedings of the 10th International Conference on Intelligent Computing, ICIC 2014, held in Taiyuan, China, in August 2014. The 85 papers of this volume were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections such as soft computing; artificial bee colony algorithms; unsupervised learning; kernel methods and supporting vector machines; machine learning; fuzzy theory and algorithms; image processing; intelligent computing in computer vision; intelligent computing in communication networks; intelligent image/document retrievals; intelligent data analysis and prediction; intelligent agent and Web applications; intelligent fault diagnosis; knowledge representation/reasoning; knowledge discovery and data mining; natural language processing and computational linguistics; next gen sequencing and metagenomics; intelligent computing in scheduling and engineering optimization; advanced modeling, control and optimization techniques for complex engineering systems; complex networks and their applications; time series forecasting and analysis using artificial neural networks; computer human interaction using multiple visual cues and intelligent computing; biometric system and security for intelligent computing.