This book discusses conceptual and pragmatic issues in the assessment of statistical knowledge and reasoning skills among students at the college and precollege levels, and the use of assessments to improve instruction. It is designed primarily for academic audiences involved in teaching statistics and mathematics, and in teacher education and training. The book is divided in four sections: (I) Assessment goals and frameworks, (2) Assessing conceptual understanding of statistical ideas, (3) Innovative models for classroom assessments, and (4) Assessing understanding of probability.

Increased attention is being paid to the need for statistically educated citizens: statistics is now included in the K-12 mathematics curriculum, increasing numbers of students are taking courses in high school, and introductory statistics courses are required in college. However, increasing the amount of instruction is not sufficient to prepare statistically literate citizens. A major change is needed in how statistics is taught. To bring about this change, three dimensions of teacher knowledge need to be addressed: their knowledge of statistical content, their pedagogical knowledge, and their statistical-pedagogical knowledge, i.e., their specific knowledge about how to teach statistics. This book is written for mathematics and statistics educators and researchers. It summarizes the research and highlights the important concepts for teachers to emphasize, and shows the interrelationships among concepts. It makes specific suggestions regarding how to build classroom activities, integrate technological tools, and assess students’ learning. This is a unique book. While providing a wealth of examples through lessons and data sets, it is also the best attempt by members of our profession to integrate suggestions from research findings with statistics concepts and pedagogy. The book’s message about the importance of listening to research is loud and clear, as is its message about alternative ways of teaching statistics. This book will impact instructors, giving them pause to consider: "Is what I’m doing now really the best thing for my students? What could I do better?" J. Michael Shaughnessy, Professor, Dept of Mathematical Sciences, Portland State University, USA This is a much-needed text for linking research and practice in teaching statistics. The authors have provided a comprehensive overview of the current state-of-the-art in statistics education research. The insights they have gleaned from the literature should be tremendously helpful for those involved in teaching and researching introductory courses. Randall E. Groth, Assistant Professor of Mathematics Education, Salisbury University, USA

This book has two primary goals. On the level of theory development, the book clarifies the nature of an emerging "models and modeling perspective" about teaching, learning, and problem solving in mathematics and science education. On the level of emphasizing practical problems, it clarifies the nature of some of the most important elementary-but-powerful mathematical or scientific understandings and abilities that Americans are likely to need as foundations for success in the present and future technology-based information age. Beyond Constructivism: Models and Modeling Perspectives on Mathematics Problem Solving, Learning, and Teaching features an innovative Web site housing online appendices for each chapter, designed to supplement the print chapters with digital resources that include example problems, relevant research tools and video clips, as well as transcripts and other samples of students' work: http://tcct.soe.purdue.edu/booksULandULjournals/modelsULandUL modeling/ This is an essential volume for graduate-level courses in mathematics and science education, cognition and learning, and critical and creative thinking, as well as a valuable resource for researchers and practitioners in these areas.

Research methods and statistics are central to the development of professional competence and evidence based psychological practice. (Noun, masculine) research on the development of psychological literacy. Despite this, many psychology students express little interest in, and in some cases of active dislike of, learning research methods and statistics. This ebook brings together current research, innovative evidence-based practice, and critical discourse.

How can excellence in the teaching of research methods be encouraged and ensured? This question has become increasingly important following the adoption of research methodology as a core part of many postgraduate and undergraduate courses. There has, however, been little discussion about the aims and methods of teaching the subject. In this volume; a number of authors from a variety of countries and disciplines employ their knowledge and experience towards the development of a 'pedagogical culture' in research methods. Their aim is to establish the extent of common concerns and challenges and to demonstrate ways in which these are being met. Intended to provide both a stimulus and source materials for the development of a more substantial and systematic literature in the field, the book will be of great interest to all those teaching research methods courses within social science disciplines.

An update to the original 1992 publication, this two-volume set unites current research to provide new conceptualizations of research problems, and to suggest possible research programs to move the field forward. In studying the existing research, the authors found that the community has maintained its focus on problems of learning, teaching, teacher education, assessment, technology, and social and cultural aspects of mathematics education, while some new areas of interest have emerged or been expanded. This set allows educators to step back and look at each of these areas to see where mathematics education research has been and where it should be going to enable the field to answer the questions about education that practitioners, policy makers, and politicians are asking.

This volume provides a contemporary glance at the drastically expanding field of delivering large-scale education to unprecedented numbers of learners. It compiles papers presented at the CELDA (Cognition and Exploratory Learning in the Digital Age) conference, which has a goal of continuing to address these challenges and promote the effective use of new tools and technologies to support teaching, learning and assessment. Given the emerging global trend to exploit the potential of existing digital technologies to improve the teaching, learning and assessment experiences for all learners in real-life contexts, this topic is a unifying theme for this volume. The book showcases how emerging educational technologies and innovative practices have been used to address core global educational challenges. It provides state-of-the-art insights and case studies of exploiting innovative learning technologies, including Massive Open Online Courses and educational data analytics, to address key global challenges spanning from online Teacher Education to large-scale coding competence development. This volume will be of interest to academics and professional practitioners working in the area of digital technology integration in teaching, learning and assessment, as well as those interested in specific conference themes (e.g., designing and assessing learning in online environments, assessing learning in complex domains) and presenters, invited speakers, and participants of the CELDA conference.

We are in the era of large-scale science. In oncology there is a huge number of data sets grouping information on cancer genomes, transcriptomes, clinical data, and more. The challenge of big data in cancer is to integrate all this diversity of data collected into a unique platform that can be analyzed, leading to the generation of readable files. The possibility of harnessing information from all the accumulated data leads to an improvement in cancer patient treatment and outcome. Solving the big data problem in oncology has multiple facets. Big data in Oncology: Impact, Challenges, and Risk Assessment brings together insights from emerging sophisticated information and communication technologies such as artificial intelligence, data science, and big data analytics for cancer management. This book focuses on targeted disease treatment using big data analytics. It provides information about targeted treatment in oncology, challenges and application of big data in cancer therapy. Recent developments in the fields of artificial intelligence, machine learning, medical imaging, personalized medicine, computing and data analytics for improved patient care. Description of the application of big data with AI to discover new targeting points for cancer treatment. Summary of several risk assessments in the field of oncology using big data. Focus on prediction of doses in oncology using big data The most targeted or relevant audience is academics, research scholars, health care professionals, hospital management, pharmaceutical chemists, the biomedical industry, software engineers and IT professionals.