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Data for learning : building a smart education data system
Data are a crucial ingredient in any successful education system, but building and sustaining a data system are challenging tasks. Many countries around the world have spent significant resources but still struggle to accomplish a functioning Education Management Information System (EMIS). On the other hand, countries that have created successful systems are harnessing the power of data to improve education outcomes. Increasingly, EMISs are moving away from using data narrowly for counting students and schools. Instead, they use data to drive system-wide innovations, accountability, professionalization, and, most important, quality and learning. This broader use of data also benefits classroom instruction and support at schools. An effective data system ensures that education cycles, from preschool to tertiary, are aligned and that the education system is monitored so it can achieve its ultimate goal-- producing graduates able to successfully transition into the labor market and contribute to the overall national economy. This publication sheds light on challenges in building a data system and provide actionable direction on how to navigate the complex issues associated with education data for better learning outcomes and beyond. It details the key ingredients of successful data systems, including tangible examples, common pitfalls, and good practices. It is a resource for policy makers working to craft the vision and strategic road map of an EMIS, as well as a handbook to assist teams and decision makers in avoiding common mistakes.
Book
Understanding educational statistics using Microsoft Excel and SPSS
The book begins with an introduction to descriptive and inferential statistics and then proceeds to acquaint readers with the various functions for working with quantitative data in the Microsoft Excel environment, such as spreadsheet navigation; sorting and filtering; and creating pivot tables. Subsequent chapters treat the procedures that are commonly-employed when working with data across various fields of social science research, including: Single-sample tests; repeated measure tests; independent t-tests; one way ANOVA and factorial ANOVA; correlation; bivariate regression; Chi Square; multiple regression. Individual chapters are devoted to specific procedures, each ending with a lab exercise that highlights the importance of that procedure by posing a research question, examining the question through its application in Excel and SPSS, and concluding with a brief research report that outlines key findings drawn from the results. Real-world examples and data from modern educational research are used throughout the book, and a related Web site features additional data sets, examples, and labs, allowing readers to reinforce their comprehension of the material. (DIPF/Orig.).
eBook
Data Science in Education Using R
Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like?
If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a “learn by doing” approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job.
This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development.
eBook
Equity in Data
Building a better data culture can be the path to better results and greater equity in schools. But what do we mean by data? Your students are not just statistics. They aren't simply a set of numbers or faceless dots on a proficiency scale. They are vibrant collections of experiences, thoughts, perspectives, emotions, wants, and dreams. And taken collectively, all of that information is data--and should be valued as such. \"Equity in Data\" not only unpacks the problematic nature of current approaches to data but also helps educators demystify and democratize data. It shows how we can bake equity into our data work and illuminate the disparities, stories, and truths that make our schools safer and stronger--and that help our students grow and thrive. To this end, the authors introduce a four-part framework for how to create an equitable data culture (along with a complementary set of data principles). They demonstrate how we can rethink our approach to data in the interest of equity by making five shifts: (1) Expand our understanding of data; (2) Strengthen our knowledge of data principles; (3) Break through our fear of data; (4) Decolonize our data gathering processes; and (5) Turn data into meaningful, equitable action. We have an opportunity to realign school data with what students want out of their educational experiences. When we put equity first, we put students first.
eBook
Lessons learned from World Bank education management information system operations : portfolio review 1998-2014
Provides an overview of the World Bank's portfolio in the area of Education Management Information Systems (EMISs) over the course of 17 years, from 1998 to 2014. It seeks to identify overall trends and characteristics of World Bank support in this area, with the intent of informing future project preparation and analytical work. The portfolio review revealed that although several good practices were evident, operational performance of EMIS activities fell short of expectations, with widespread deficiencies that ranged from unclear definitions and understanding of the EMIS to ineffective implementation and utilization. Examples of successful activities include the development of an EMIS to manage teachers and provide access to education (for example, Afghanistan); utilization of an EMIS as a management tool (for example, Bosnia and Herzegovina); creation of an online EMIS to improve access to education data (for example, Honduras); use of an EMIS to strengthen teaching and learning (for example, Guatemala and Lithuania); and use of an EMIS as a management tool for schools (for example, Malaysia). These success stories highlight how a well-implemented EMIS can improve the performance of an education system. The challenges that have been identified as contributing to the shortcomings are related to the following: Misalignment of activities and unrealistic EMIS goals; Institutionalization of the EMIS; Sustainability challenges resulting from inconsistent leadership; Missed integration opportunities; Private players in education; EMIS at the local level. Future projects could benefit from the SABER (Systems Approach for Better Education Results)-EMIS Assessment Framework. The SABER-EMIS Framework focuses on the need for a strong enabling environment, system soundness, quality data, and effective utilization as the key factors essential for the successful implementation of an EMIS. Initial needs assessment of a country's EMIS can play a critical role in benchmarking countries and provide a valuable foundation for the design of new projects.
Book
Data for Learning
Data are a crucial ingredient in any successful education system, but building and sustaining a data system are challenging tasks. Many countries around the world have spent significant resources but still struggle to accomplish a functioning Education Management Information System (EMIS). On the other hand, countries that have created successful systems are harnessing the power of data to improve education outcomes. Increasingly, EMISs are moving away from using data narrowly for counting students and schools. Instead, they use data to drive system-wide innovations, accountability, professionalization, and, most important, quality and learning. This broader use of data also benefits classroom instruction and support at schools. An effective data system ensures that education cycles, from preschool to tertiary, are aligned and that the education system is monitored so it can achieve its ultimate goal-- producing graduates able to successfully transition into the labor market and contribute to the overall national economy. This publication sheds light on challenges in building a data system and provide actionable direction on how to navigate the complex issues associated with education data for better learning outcomes and beyond. It details the key ingredients of successful data systems, including tangible examples, common pitfalls, and good practices. It is a resource for policy makers working to craft the vision and strategic road map of an EMIS, as well as a handbook to assist teams and decision makers in avoiding common mistakes.
eBook
Data mining and learning analytics : applications in educational research
Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining's four guiding principles- prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM's emerging role in helping to advance educational research-from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.
eBook