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What is bad data? Some people consider it a technical phenomenon, like missing values or malformed records, but bad data includes a lot more. In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how they’ve recovered from nasty data problems. From cranky storage to poor representation to misguided policy, there are many paths to bad data. Bottom line? Bad data is data that gets in the way. This book explains effective ways to get around it. Among the many topics covered, you’ll discover how to: Test drive your data to see if it’s ready for analysis Work spreadsheet data into a usable form Handle encoding problems that lurk in text data Develop a successful web-scraping effort Use NLP tools to reveal the real sentiment of online reviews Address cloud computing issues that can impact your analysis effort Avoid policies that create data analysis roadblocks Take a systematic approach to data quality analysis
This handbook establishes current and consistent terms, definitions, and classification codes to maintain, collect, report, and exchange comparable information about students. The handbook is useful to public and private agencies and educational institutions, researchers, policy makers, and members of the public interested in student data. The definitions are consistent with current state and local practice, national standards movements, and federal reporting requirements as a consensus of what is considered "best practice" in data collection. The Handbook includes these chapters: (1) "Introduction"; (2) "The Handbook"; (3) "Building a Student Record System"; (4) "Data Elements and Definitions"; and (5) "Applications of the Handbook." Twelve appendixes present comprehensive lists of coding options, code designations, and the names of contributors to this edition. (SLD)
This handbook is an effort to establish current and consistent terms, definitions, and classification codes to maintain, collect, report, and exchange comparable information about students. It is a reference guide to those concerned with the collection of student data. Definitions reflect a consensus about best practice in the field of education as well as current Federal reporting requirements. Information is included about designing student record-keeping systems for schools, school districts, or other educational institutions. The handbook is not a data collection instrument, nor does it reflect any Federal data collection requirements. There are descriptions of applications of the handbook, including the development of surveys and design of automated record systems. Twelve appendixes provide lists of contributors and technical information about data reporting. (SLD)
Reference tool of terms and definitions for public and private education agencies involved in collection of student data. Includes information about designing student record-keeping systems for use by schools, school districts, other education institutions.
For more than two decades, the concept of student engagement has grown from simple attention in class to a construct comprised of cognitive, emotional, and behavioral components that embody and further develop motivation for learning. Similarly, the goals of student engagement have evolved from dropout prevention to improved outcomes for lifelong learning. This robust expansion has led to numerous lines of research across disciplines and are brought together clearly and comprehensively in the Handbook of Research on Student Engagement. The Handbook guides readers through the field’s rich history, sorts out its component constructs, and identifies knowledge gaps to be filled by future research. Grounding data in real-world learning situations, contributors analyze indicators and facilitators of student engagement, link engagement to motivation, and gauge the impact of family, peers, and teachers on engagement in elementary and secondary grades. Findings on the effectiveness of classroom interventions are discussed in detail. And because assessing engagement is still a relatively new endeavor, chapters on measurement methods and issues round out this important resource. Topical areas addressed in the Handbook include: Engagement across developmental stages. Self-efficacy in the engaged learner. Parental and social influences on engagement and achievement motivation. The engaging nature of teaching for competency development. The relationship between engagement and high-risk behavior in adolescents. Comparing methods for measuring student engagement. An essential guide to the expanding knowledge base, the Handbook of Research on Student Engagement serves as a valuable resource for researchers, scientist-practitioners, and graduate students in such varied fields as clinical child and school psychology, educational psychology, public health, teaching and teacher education, social work, and educational policy.
This handbook brings together in a single volume the groundbreaking work of scholars who have conducted studies of student experiences of school in Afghanistan, Australia, Canada, England, Ghana, Ireland, Pakistan, and the United States. Drawing extensively on students’ interpretations of their experiences in school as expressed in their own words, chapter authors offer insight into how students conceptualize and approach school. The book examines how students understand and address the ongoing social opportunities for and challenges in working with other students and teachers, and the multiple ways in which students shape and contribute to school improvement.
This handbook provides a thorough overview of the current state of knowledge in this area. The first part of the book includes nine surveys and tutorials on the principal data mining techniques that have been applied in education. The second part presents a set of 25 case studies that give a rich overview of the problems that EDM has addressed. With contributions by well-known researchers from a variety of fields, the book reflects the multidisciplinary nature of the EDM community. It helps education experts understand what types of questions EDM can address and helps data miners understand what types of questions are important to educational design and educational decision making.