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Bayesian Networks in Educational Assessment

Bayesian Networks in Educational Assessment
Author: Russell G. Almond
Publisher: Springer
Total Pages: 678
Release: 2015-03-10
Genre: Social Science
ISBN: 1493921258

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Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments. Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume’s grounding in Evidence-Centered Design (ECD) framework for assessment design. This “design forward” approach enables designers to take full advantage of Bayes nets’ modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as an integral component of a principled design process, and illustrates the ideas with an in-depth look at the BioMass project: An interactive, standards-based, web-delivered demonstration assessment of science inquiry in genetics. This book is both a resource for professionals interested in assessment and advanced students. Its clear exposition, worked-through numerical examples, and demonstrations from real and didactic applications provide invaluable illustrations of how to use Bayes nets in educational assessment. Exercises follow each chapter, and the online companion site provides a glossary, data sets and problem setups, and links to computational resources.


Bayesian Networks in Educational Assessment

Bayesian Networks in Educational Assessment
Author: Russell G. Almond
Publisher: Springer
Total Pages: 662
Release: 2015-03-11
Genre: Social Science
ISBN: 9781493921263

Download Bayesian Networks in Educational Assessment Book in PDF, ePub and Kindle

Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments. Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume’s grounding in Evidence-Centered Design (ECD) framework for assessment design. This “design forward” approach enables designers to take full advantage of Bayes nets’ modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as an integral component of a principled design process, and illustrates the ideas with an in-depth look at the BioMass project: An interactive, standards-based, web-delivered demonstration assessment of science inquiry in genetics. This book is both a resource for professionals interested in assessment and advanced students. Its clear exposition, worked-through numerical examples, and demonstrations from real and didactic applications provide invaluable illustrations of how to use Bayes nets in educational assessment. Exercises follow each chapter, and the online companion site provides a glossary, data sets and problem setups, and links to computational resources.


Knowing What Students Know

Knowing What Students Know
Author: National Research Council
Publisher: National Academies Press
Total Pages: 383
Release: 2001-10-27
Genre: Education
ISBN: 0309293227

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Education is a hot topic. From the stage of presidential debates to tonight's dinner table, it is an issue that most Americans are deeply concerned about. While there are many strategies for improving the educational process, we need a way to find out what works and what doesn't work as well. Educational assessment seeks to determine just how well students are learning and is an integral part of our quest for improved education. The nation is pinning greater expectations on educational assessment than ever before. We look to these assessment tools when documenting whether students and institutions are truly meeting education goals. But we must stop and ask a crucial question: What kind of assessment is most effective? At a time when traditional testing is subject to increasing criticism, research suggests that new, exciting approaches to assessment may be on the horizon. Advances in the sciences of how people learn and how to measure such learning offer the hope of developing new kinds of assessments-assessments that help students succeed in school by making as clear as possible the nature of their accomplishments and the progress of their learning. Knowing What Students Know essentially explains how expanding knowledge in the scientific fields of human learning and educational measurement can form the foundations of an improved approach to assessment. These advances suggest ways that the targets of assessment-what students know and how well they know it-as well as the methods used to make inferences about student learning can be made more valid and instructionally useful. Principles for designing and using these new kinds of assessments are presented, and examples are used to illustrate the principles. Implications for policy, practice, and research are also explored. With the promise of a productive research-based approach to assessment of student learning, Knowing What Students Know will be important to education administrators, assessment designers, teachers and teacher educators, and education advocates.


Automated Scoring of Complex Tasks in Computer-based Testing

Automated Scoring of Complex Tasks in Computer-based Testing
Author: David M. Williamson
Publisher: Psychology Press
Total Pages: 437
Release: 2006
Genre: Education
ISBN: 0805846344

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This is the first volume to provide the latest methods and examples of "best practices" in the design, implementation, and evaluation of automated scoring for complex assessments. The contributing authors, all noted leaders in the field, introduce each m


Innovative Assessment for the 21st Century

Innovative Assessment for the 21st Century
Author: Valerie J. Shute
Publisher: Springer Science & Business Media
Total Pages: 260
Release: 2010-09-08
Genre: Education
ISBN: 1441965300

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In today’s rapidly changing and information-rich world, students are not acquiring adequate knowledge and skills to prepare them for careers in mathematics, science, and technology with the traditional approach to assessment and instruction. New competencies (e.g., information communication and technology skills) are needed to deal successfully with the deluge of data. In order to accomplish this, new "educationally valuable" skills must be acknowledged and assessed. Toward this end, the skills we value and support for a society producing knowledge workers, not simply service workers, must be identified, together with methods for their measurement. Innovative Assessment for the 21st Century explores the faces of future assessment—and ask hard questions, such as: What would an assessment that captures all of the above attributes look like? Should it be standardized? What is the role of the professional teacher?


Addressing Inequities in Modern Educational Assessment

Addressing Inequities in Modern Educational Assessment
Author: Jorge Sainz
Publisher: Springer Nature
Total Pages: 214
Release: 2023-11-14
Genre: Study Aids
ISBN: 3031458028

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This edited book focuses on the central and up-to-date issues that represent some of the most relevant challenges and limitations of International Learning Assessments (ILSAs). It specifically sheds light on the general effects of the discontinuation of face-to-face education on students at diverse academic levels during the COVID-19 pandemic. Through the lens of the most relevant experts of the field, it highlights the asymmetric effects on students based on family income, education level, or employment, imposing a heavy toll on those with less resources. It raises issues regarding the impact on the mental health of students and education professionals due to school closures. Modern education is shifting its focus from just scores to detailed, qualitative feedback. Such comprehensive insights help students identify both strengths and growth areas, making assessments more constructive. This book is aimed for researchers in the field of educational assessments, graduate and postgraduate students, policy makers, and anyone interested in making informed decisions regarding ILSAs. COVID-19 brought immense educational challenges, it also presented an unprecedented opportunity: to reassess and reform existing systems, steering towards assessments that are both fair and empowering.


Learning Bayesian Networks

Learning Bayesian Networks
Author: Richard E. Neapolitan
Publisher: Prentice Hall
Total Pages: 704
Release: 2004
Genre: Computers
ISBN:

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In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the information. Some of the topics discussed include Pearl's message passing algorithm, Parameter Learning: 2 Alternatives, Parameter Learning r Alternatives, Bayesian Structure Learning, and Constraint-Based Learning. For expert systems developers and decision theorists.


Risk Assessment and Decision Analysis with Bayesian Networks

Risk Assessment and Decision Analysis with Bayesian Networks
Author: Norman Fenton
Publisher: CRC Press
Total Pages: 672
Release: 2018-09-03
Genre: Mathematics
ISBN: 1351978969

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Since the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information, cybersecurity, debunking bad statistics, and much more. Focusing on practical real-world problem-solving and model building, as opposed to algorithms and theory, it explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide more powerful insights and better decision making than is possible from purely data-driven solutions. Features Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, forensics, cybersecurity and more Introduces all necessary mathematics, probability, and statistics as needed Establishes the basics of probability, risk, and building and using Bayesian network models, before going into the detailed applications A dedicated website contains exercises and worked solutions for all chapters along with numerous other resources. The AgenaRisk software contains a model library with executable versions of all of the models in the book. Lecture slides are freely available to accredited academic teachers adopting the book on their course.