The new generation of tests is faced with new challenges. In the K?12 setting, the new learning targets are intended to assess higher?order thinking skills and prepare students to be ready for college and career and to keep American students competitive with their international peers. In addition, the new generation of state tests requires the use of technology in item delivery and embedding assessment in real?world, authentic, situations. It further requires accurate assessment of students at all ability levels. One of the most important questions is how to maintain test fairness in the new assessments with technology innovative items and technology delivered tests. In the traditional testing programs such as licensure and certification tests and college admission tests, test fairness has constantly been a key psychometric issue in test development and this continues to be the case with the national testing programs. As test fairness needs to be addressed throughout the whole process of test development, experts from state, admission, and licensure tests will address test fairness challenges in the new generation assessment. The book chapters clarify misconceptions of test fairness including the use of admission test results in cohort comparison, the use of international assessment results in trend evaluation, whether standardization and fairness necessarily mean uniformity when test?takers have different cultural backgrounds, and whether standardization can insure fairness. More technically, chapters also address issues related to how compromised items and test fairness are related to classification decisions, how accessibility in item development and accommodation could be mingled with technology, how to assess special populations with dyslexia, using Blinder?Oaxaca Decomposition for differential item functioning detection, and differential feature functioning in automated scoring. Overall, this book addresses test fairness issues in state assessment, college admission testing, international assessment, and licensure tests. Fairness is discussed in the context of culture and special populations. Further, fairness related to performance assessment and automated scoring is a focus as well. This book provides a very good source of information related to test fairness issues in test development in the new generation of assessment where technology is highly involved.

Modelling student growth has been a federal policy requirement under No Child Left Behind (NCLB). In addition to tracking student growth, the latest Race To The Top (RTTP) federal education policy stipulates the evaluation of teacher effectiveness from the perspective of added value that teachers contribute to student learning and growth. Student growth modelling and teacher value-added modelling are complex. The complexity stems, in part, from issues due to non-random assignment of students into classes and schools, measurement error in students' achievement scores that are utilized to evaluate the added value of teachers, multidimensionality of the measured construct across multiple grades, and the inclusion of covariates. National experts at the Twelfth Annual Maryland Assessment Research Center's Conference on "Value Added Modeling and Growth Modeling with Particular Application to Teacher and School Effectiveness" present the latest developments and methods to tackle these issues. This book includes chapters based on these conference presentations. Further, the book provides some answers to questions such as what makes a good growth model? What criteria should be used in evaluating growth models? How should outputs from growth models be utilized? How auxiliary teacher information could be utilized to improve value added? How multiple sources of student information could be accumulated to estimate teacher effectiveness? Whether student-level and school-level covariates should be included? And what are the impacts of the potential heterogeneity of teacher effects across students of different aptitudes or other differing characteristics on growth modelling and teacher evaluation? Overall, this book addresses reliability and validity issues in growth modelling and value added modelling and presents the latest development in this area. In addition, some persistent issues have been approached from a new perspective. This edited volume provides a very good source of information related to the current explorations in student growth and teacher effectiveness evaluation.

This book introduces theories and practices for using assessment data to enhance learning and instruction. Topics include reshaping the homework review process, iterative learning engineering, learning progressions, learning maps, score report designing, the use of psychosocial data, and the combination of adaptive testing and adaptive learning. In addition, studies proposing new methods and strategies, technical details about the collection and maintenance of process data, and examples illustrating proposed methods and software are included. Chapters 1, 4, 6, 8, and 9 discuss how to make valid interpretations of results and achieve more efficient instructions from various sources of data. Chapters 3 and 7 propose and evaluate new methods to promote students' learning by using evidence-based iterative learning engineering and supporting the teachers' use of assessment data, respectively. Chapter 2 provides technical details on the collection, storage, and security protection of process data. Chapter 5 introduces software for automating some aspects of developmental education and the use of predictive modeling. Chapter 10 describes the barriers to using psychosocial data for formative assessment purposes. Chapter 11 describes a conceptual framework for adaptive learning and testing and gives an example of a functional learning and assessment system. In summary, the book includes comprehensive perspectives of the recent development and challenges of using test data for formative assessment purposes. The chapters provide innovative theoretical frameworks, new perspectives on the use of data with technology, and how to build new methods based on existing theories. This book is a useful resource to researchers who are interested in using data and technology to inform decision making, facilitate instructional utility, and achieve better learning outcomes.

The general theme of this book is to present innovative psychometric modeling and methods. In particular, this book includes research and successful examples of modeling techniques for new data sources from digital assessments, such as eye-tracking data, hint uses, and process data from game-based assessments. In addition, innovative psychometric modeling approaches, such as graphical models, item tree models, network analysis, and cognitive diagnostic models, are included. Chapters 1, 2, 4 and 6 are about psychometric models and methods for learning analytics. The first two chapters focus on advanced cognitive diagnostic models for tracking learning and the improvement of attribute classification accuracy. Chapter 4 demonstrates the use of network analysis for learning analytics. Chapter 6 introduces the conjunctive root causes model for the understanding of prerequisite skills in learning. Chapters 3, 5, 8, 9 are about innovative psychometric techniques to model process data. Specifically, Chapters 3 and 5 illustrate the usage of generalized linear mixed effect models and item tree models to analyze eye-tracking data. Chapter 8 discusses the modeling approach of hint uses and response accuracy in learning environment. Chapter 9 demonstrates the identification of observable outcomes in the game-based assessments. Chapters 7 and 10 introduce innovative latent variable modeling approaches, including the graphical and generalized linear model approach and the dynamic modeling approach. In summary, the book includes theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to model and analyze multiple data sources from digital assessments. When computer-based assessments are emerging and evolving, it is important that researchers can expand and improve the methods for modeling and analyzing new data sources. This book provides a useful resource to researchers who are interested in the development of psychometric methods to solve issues in this digital assessment age.

Assessment innovation tied to technology is greatly needed in a wide variety of assessment applications. This book adopts an interdisciplinary perspective to learn from advances in developing technology enhanced innovative assessments from multiple fields. The book chapters address the development of virtual assessments including game?based assessment, simulation?based assessment, and narrative based assessment as well as how simulation and game based assessments serve both formative and summative purposes. Further, chapters address the critical challenge of integrating assessment directly into the learning process so that teacher effectiveness and student learning can be enhanced. Two chapters specifically address the psychometric challenges related to innovative items. One chapter talks about evaluating the psychometric properties of innovative items while the other chapter presents a new psychometric model for calibrating innovative items embedded in multiple contexts. In addition, validity issues are addressed related to technologyenhanced innovative assessment. It is hoped that the book provides readers with rich and useful information about the development of several types of virtual assessments from multiple perspectives. The authors include experts from industry where innovative items have been used for many years and experts from research institutes and universities who have done pioneering work related to developing innovative items with formative applications to facilitate learning. In addition, expert advice has been provided on validating such work.

The Race To The Top program strongly advocates the use of computer technology in assessments. It dramatically promotes computer-based testing, linear or adaptive, in K-12 state assessment programs. Moreover, assessment requirements driven by this federal initiative exponentially increase the complexity in assessment design and test development. This book provides readers with a review of the history and basics of computer-based tests. It also offers a macro perspective for designing such assessment systems in the K-12 setting as well as a micro perspective on new challenges such as innovative items, scoring of such items, cognitive diagnosis, and vertical scaling for growth modelling and value added approaches to assessment. The editors' goal is to provide readers with necessary information to create a smarter computer-based testing system by following the advice and experience of experts from education as well as other industries. This book is based on a conference (http://marces.org/workshop.htm) held by the Maryland Assessment Research Centre for Education Success. It presents multiple perspectives including test vendors and state departments of education, in designing and implementing a computer-based test in the K-12 setting. The design and implementation of such a system requires deliberate planning and thorough considerations. The advice and experiences presented in this book serve as a guide to practitioners and as a good source of information for quality control. The technical issues discussed in this book are relatively new and unique to K-12 large-scale computer-based testing programs, especially due to the recent federal policy. Several chapters provide possible solutions to psychometricians dealing with the technical challenges related to innovative items, cognitive diagnosis, and growth modelling in computer-based linear or adaptive tests in the K-12 setting.

The Race To The Top (RTTP) federal education policy fostered a new generation of state tests. This policy advocated adopting common core standards which set a higher level of learning targets for students in the US K?12 education. These standards are intended to assess higher order thinking skills and prepare students ready for college and career. In the meantime, they are aligned with those for international assessments which keep US students abreast of their international peers. Furthermore, the new generation of state tests requires the use of technology enhanced items to align student assessments with student learning environment. Computer technology is indispensable to accomplish this goal. Computer based tests related to common core standards are different from previous state computer based tests in two important aspects, one is that the current version requires accurate assessment of students along all ability levels and the other is that it promotes the use of an efficient test delivery system, essentially the use of computerized adaptive assessment in K?12 state testing programs. In addition to providing summative information about student learning, the new common core tests add formative assessment component in the whole assessment system to provide timely feedback to students and teachers during the process of student learning and teacher instruction. As with its predecessor, the new assessment policy also holds teachers and schools accountable for student learning. With the requirements by the new federal education policy, states formed two consortia: Partnership for Assessment of Readiness for College and Careers (PARCC) and Smarter?Balanced Assessment Consortium (SBAC) to develop assessments in alignment with the new common core standards. This book is based on the presentations made at the Thirteenth Annual Maryland Assessment Research Center's Conference on "The Next Generation of Testing: Common Core Standards, Smarter?Balanced, PARCC, and the Nationwide Testing Movement". Experts from the consortia and nationwide overviewed the intention, history and the current status of this nationwide testing movement. Item development, test design, and transition from old state tests to the new consortia tests are discussed. Test scoring and reporting are specially highlighted in the book. The challenges related to standard setting for the new test, especially in the CAT environment and linking performance standards from state tests with consortium tests were explored. The issues of utilizing the consortium test results to evaluate students' college and career readiness is another topic addressed in the book. The last chapters address the critical issue of validity in the new generation of state testing programs. Overall, this book presents the latest status of the development of the two consortium assessment systems. It addresses the most challenging issues related to the next generation of state testing programs including development of innovative items assessing higher order thinking skills, scoring of such items, standard setting and linkage with the old state specific standards, and validity issues. This edited book provides a very good source of information related to the consortium tests based on the common core standards.

The general theme of this book is to present the applications of artificial intelligence (AI) in test development. In particular, this book includes research and successful examples of using AI technology in automated item generation, automated test assembly, automated scoring, and computerized adaptive testing. By utilizing artificial intelligence, the efficiency of item development, test form construction, test delivery, and scoring could be dramatically increased. Chapters on automated item generation offer different perspectives related to generating a large number of items with controlled psychometric properties including the latest development of using machine learning methods. Automated scoring is illustrated for different types of assessments such as speaking and writing from both methodological aspects and practical considerations. Further, automated test assembly is elaborated for the conventional linear tests from both classical test theory and item response theory perspectives. Item pool design and assembly for the linear-on-the-fly tests elaborates more complications in practice when test security is a big concern. Finally, several chapters focus on computerized adaptive testing (CAT) at either item or module levels. CAT is further illustrated as an effective approach to increasing test-takers' engagement in testing. In summary, the book includes both theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to automate the process of test development. While some of these automation processes have become common practices such as automated test assembly, automated scoring, and computerized adaptive testing, some others such as automated item generation calls for more research and exploration. When new AI methods are emerging and evolving, it is expected that researchers can expand and improve the methods for automating different steps in test development to enhance the automation features and practitioners can adopt quality automation procedures to improve assessment practices.

The general theme of this book is to encourage the use of relevant methodology in data mining which is or could be applied to the interplay of education, statistics and computer science to solve psychometric issues and challenges in the new generation of assessments. In addition to item response data, other data collected in the process of assessment and learning will be utilized to help solve psychometric challenges and facilitate learning and other educational applications. Process data include those collected or available for collection during the process of assessment and instructional phase such as responding sequence data, log files, the use of help features, the content of web searches, etc. Some book chapters present the general exploration of process data in large -scale assessment. Further, other chapters also address how to integrate psychometrics and learning analytics in assessment and survey, how to use data mining techniques for security and cheating detection, how to use more assessment results to facilitate student's learning and guide teacher's instructional efforts. The book includes both theoretical and methodological presentations that might guide the future in this area, as well as illustrations of efforts to implement big data analytics that might be instructive to those in the field of learning and psychometrics. The context of the effort is diverse, including K-12, higher education, financial planning, and survey utilization. It is hoped that readers can learn from different disciplines, especially those who are specialized in assessment, would be critical to expand the ideas of what we can do with data analytics for informing assessment practices.

The general theme of this book is to present the applications of artificial intelligence (AI) in test development. In particular, this book includes research and successful examples of using AI technology in automated item generation, automated test assembly, automated scoring, and computerized adaptive testing. By utilizing artificial intelligence, the efficiency of item development, test form construction, test delivery, and scoring could be dramatically increased. Chapters on automated item generation offer different perspectives related to generating a large number of items with controlled psychometric properties including the latest development of using machine learning methods. Automated scoring is illustrated for different types of assessments such as speaking and writing from both methodological aspects and practical considerations. Further, automated test assembly is elaborated for the conventional linear tests from both classical test theory and item response theory perspectives. Item pool design and assembly for the linear-on-the-fly tests elaborates more complications in practice when test security is a big concern. Finally, several chapters focus on computerized adaptive testing (CAT) at either item or module levels. CAT is further illustrated as an effective approach to increasing test-takers' engagement in testing. In summary, the book includes both theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to automate the process of test development. While some of these automation processes have become common practices such as automated test assembly, automated scoring, and computerized adaptive testing, some others such as automated item generation calls for more research and exploration. When new AI methods are emerging and evolving, it is expected that researchers can expand and improve the methods for automating different steps in test development to enhance the automation features and practitioners can adopt quality automation procedures to improve assessment practices.

The general theme of this book is to present innovative psychometric modeling and methods. In particular, this book includes research and successful examples of modeling techniques for new data sources from digital assessments, such as eye-tracking data, hint uses, and process data from game-based assessments. In addition, innovative psychometric modeling approaches, such as graphical models, item tree models, network analysis, and cognitive diagnostic models, are included. Chapters 1, 2, 4 and 6 are about psychometric models and methods for learning analytics. The first two chapters focus on advanced cognitive diagnostic models for tracking learning and the improvement of attribute classification accuracy. Chapter 4 demonstrates the use of network analysis for learning analytics. Chapter 6 introduces the conjunctive root causes model for the understanding of prerequisite skills in learning. Chapters 3, 5, 8, 9 are about innovative psychometric techniques to model process data. Specifically, Chapters 3 and 5 illustrate the usage of generalized linear mixed effect models and item tree models to analyze eye-tracking data. Chapter 8 discusses the modeling approach of hint uses and response accuracy in learning environment. Chapter 9 demonstrates the identification of observable outcomes in the game-based assessments. Chapters 7 and 10 introduce innovative latent variable modeling approaches, including the graphical and generalized linear model approach and the dynamic modeling approach. In summary, the book includes theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to model and analyze multiple data sources from digital assessments. When computer-based assessments are emerging and evolving, it is important that researchers can expand and improve the methods for modeling and analyzing new data sources. This book provides a useful resource to researchers who are interested in the development of psychometric methods to solve issues in this digital assessment age.

This book introduces theories and practices for using assessment data to enhance learning and instruction. Topics include reshaping the homework review process, iterative learning engineering, learning progressions, learning maps, score report designing, the use of psychosocial data, and the combination of adaptive testing and adaptive learning. In addition, studies proposing new methods and strategies, technical details about the collection and maintenance of process data, and examples illustrating proposed methods and software are included. Chapters 1, 4, 6, 8, and 9 discuss how to make valid interpretations of results and achieve more efficient instructions from various sources of data. Chapters 3 and 7 propose and evaluate new methods to promote students' learning by using evidence-based iterative learning engineering and supporting the teachers' use of assessment data, respectively. Chapter 2 provides technical details on the collection, storage, and security protection of process data. Chapter 5 introduces software for automating some aspects of developmental education and the use of predictive modeling. Chapter 10 describes the barriers to using psychosocial data for formative assessment purposes. Chapter 11 describes a conceptual framework for adaptive learning and testing and gives an example of a functional learning and assessment system. In summary, the book includes comprehensive perspectives of the recent development and challenges of using test data for formative assessment purposes. The chapters provide innovative theoretical frameworks, new perspectives on the use of data with technology, and how to build new methods based on existing theories. This book is a useful resource to researchers who are interested in using data and technology to inform decision making, facilitate instructional utility, and achieve better learning outcomes.

The general theme of this book is to encourage the use of relevant methodology in data mining which is or could be applied to the interplay of education, statistics and computer science to solve psychometric issues and challenges in the new generation of assessments. In addition to item response data, other data collected in the process of assessment and learning will be utilized to help solve psychometric challenges and facilitate learning and other educational applications. Process data include those collected or available for collection during the process of assessment and instructional phase such as responding sequence data, log files, the use of help features, the content of web searches, etc. Some book chapters present the general exploration of process data in large -scale assessment. Further, other chapters also address how to integrate psychometrics and learning analytics in assessment and survey, how to use data mining techniques for security and cheating detection, how to use more assessment results to facilitate student's learning and guide teacher's instructional efforts. The book includes both theoretical and methodological presentations that might guide the future in this area, as well as illustrations of efforts to implement big data analytics that might be instructive to those in the field of learning and psychometrics. The context of the effort is diverse, including K-12, higher education, financial planning, and survey utilization. It is hoped that readers can learn from different disciplines, especially those who are specialized in assessment, would be critical to expand the ideas of what we can do with data analytics for informing assessment practices.

Assessment innovation tied to technology is greatly needed in a wide variety of assessment applications. This book adopts an interdisciplinary perspective to learn from advances in developing technology enhanced innovative assessments from multiple fields. The book chapters address the development of virtual assessments including game?based assessment, simulation?based assessment, and narrative based assessment as well as how simulation and game based assessments serve both formative and summative purposes. Further, chapters address the critical challenge of integrating assessment directly into the learning process so that teacher effectiveness and student learning can be enhanced. Two chapters specifically address the psychometric challenges related to innovative items. One chapter talks about evaluating the psychometric properties of innovative items while the other chapter presents a new psychometric model for calibrating innovative items embedded in multiple contexts. In addition, validity issues are addressed related to technologyenhanced innovative assessment. It is hoped that the book provides readers with rich and useful information about the development of several types of virtual assessments from multiple perspectives. The authors include experts from industry where innovative items have been used for many years and experts from research institutes and universities who have done pioneering work related to developing innovative items with formative applications to facilitate learning. In addition, expert advice has been provided on validating such work.

The Race To The Top (RTTP) federal education policy fostered a new generation of state tests. This policy advocated adopting common core standards which set a higher level of learning targets for students in the US K?12 education. These standards are intended to assess higher order thinking skills and prepare students ready for college and career. In the meantime, they are aligned with those for international assessments which keep US students abreast of their international peers. Furthermore, the new generation of state tests requires the use of technology enhanced items to align student assessments with student learning environment. Computer technology is indispensable to accomplish this goal. Computer based tests related to common core standards are different from previous state computer based tests in two important aspects, one is that the current version requires accurate assessment of students along all ability levels and the other is that it promotes the use of an efficient test delivery system, essentially the use of computerized adaptive assessment in K?12 state testing programs. In addition to providing summative information about student learning, the new common core tests add formative assessment component in the whole assessment system to provide timely feedback to students and teachers during the process of student learning and teacher instruction. As with its predecessor, the new assessment policy also holds teachers and schools accountable for student learning. With the requirements by the new federal education policy, states formed two consortia: Partnership for Assessment of Readiness for College and Careers (PARCC) and Smarter?Balanced Assessment Consortium (SBAC) to develop assessments in alignment with the new common core standards. This book is based on the presentations made at the Thirteenth Annual Maryland Assessment Research Center's Conference on "The Next Generation of Testing: Common Core Standards, Smarter?Balanced, PARCC, and the Nationwide Testing Movement". Experts from the consortia and nationwide overviewed the intention, history and the current status of this nationwide testing movement. Item development, test design, and transition from old state tests to the new consortia tests are discussed. Test scoring and reporting are specially highlighted in the book. The challenges related to standard setting for the new test, especially in the CAT environment and linking performance standards from state tests with consortium tests were explored. The issues of utilizing the consortium test results to evaluate students' college and career readiness is another topic addressed in the book. The last chapters address the critical issue of validity in the new generation of state testing programs. Overall, this book presents the latest status of the development of the two consortium assessment systems. It addresses the most challenging issues related to the next generation of state testing programs including development of innovative items assessing higher order thinking skills, scoring of such items, standard setting and linkage with the old state specific standards, and validity issues. This edited book provides a very good source of information related to the consortium tests based on the common core standards.

The new generation of tests is faced with new challenges. In the K?12 setting, the new learning targets are intended to assess higher?order thinking skills and prepare students to be ready for college and career and to keep American students competitive with their international peers. In addition, the new generation of state tests requires the use of technology in item delivery and embedding assessment in real?world, authentic, situations. It further requires accurate assessment of students at all ability levels. One of the most important questions is how to maintain test fairness in the new assessments with technology innovative items and technology delivered tests. In the traditional testing programs such as licensure and certification tests and college admission tests, test fairness has constantly been a key psychometric issue in test development and this continues to be the case with the national testing programs. As test fairness needs to be addressed throughout the whole process of test development, experts from state, admission, and licensure tests will address test fairness challenges in the new generation assessment. The book chapters clarify misconceptions of test fairness including the use of admission test results in cohort comparison, the use of international assessment results in trend evaluation, whether standardization and fairness necessarily mean uniformity when test?takers have different cultural backgrounds, and whether standardization can insure fairness. More technically, chapters also address issues related to how compromised items and test fairness are related to classification decisions, how accessibility in item development and accommodation could be mingled with technology, how to assess special populations with dyslexia, using Blinder?Oaxaca Decomposition for differential item functioning detection, and differential feature functioning in automated scoring. Overall, this book addresses test fairness issues in state assessment, college admission testing, international assessment, and licensure tests. Fairness is discussed in the context of culture and special populations. Further, fairness related to performance assessment and automated scoring is a focus as well. This book provides a very good source of information related to test fairness issues in test development in the new generation of assessment where technology is highly involved.

Modelling student growth has been a federal policy requirement under No Child Left Behind (NCLB). In addition to tracking student growth, the latest Race To The Top (RTTP) federal education policy stipulates the evaluation of teacher effectiveness from the perspective of added value that teachers contribute to student learning and growth. Student growth modelling and teacher value-added modelling are complex. The complexity stems, in part, from issues due to non-random assignment of students into classes and schools, measurement error in students' achievement scores that are utilized to evaluate the added value of teachers, multidimensionality of the measured construct across multiple grades, and the inclusion of covariates. National experts at the Twelfth Annual Maryland Assessment Research Center's Conference on "Value Added Modeling and Growth Modeling with Particular Application to Teacher and School Effectiveness" present the latest developments and methods to tackle these issues. This book includes chapters based on these conference presentations. Further, the book provides some answers to questions such as what makes a good growth model? What criteria should be used in evaluating growth models? How should outputs from growth models be utilized? How auxiliary teacher information could be utilized to improve value added? How multiple sources of student information could be accumulated to estimate teacher effectiveness? Whether student-level and school-level covariates should be included? And what are the impacts of the potential heterogeneity of teacher effects across students of different aptitudes or other differing characteristics on growth modelling and teacher evaluation? Overall, this book addresses reliability and validity issues in growth modelling and value added modelling and presents the latest development in this area. In addition, some persistent issues have been approached from a new perspective. This edited volume provides a very good source of information related to the current explorations in student growth and teacher effectiveness evaluation.

The Race To The Top program strongly advocates the use of computer technology in assessments. It dramatically promotes computer-based testing, linear or adaptive, in K-12 state assessment programs. Moreover, assessment requirements driven by this federal initiative exponentially increase the complexity in assessment design and test development. This book provides readers with a review of the history and basics of computer-based tests. It also offers a macro perspective for designing such assessment systems in the K-12 setting as well as a micro perspective on new challenges such as innovative items, scoring of such items, cognitive diagnosis, and vertical scaling for growth modelling and value added approaches to assessment. The editors' goal is to provide readers with necessary information to create a smarter computer-based testing system by following the advice and experience of experts from education as well as other industries. This book is based on a conference (http://marces.org/workshop.htm) held by the Maryland Assessment Research Centre for Education Success. It presents multiple perspectives including test vendors and state departments of education, in designing and implementing a computer-based test in the K-12 setting. The design and implementation of such a system requires deliberate planning and thorough considerations. The advice and experiences presented in this book serve as a guide to practitioners and as a good source of information for quality control. The technical issues discussed in this book are relatively new and unique to K-12 large-scale computer-based testing programs, especially due to the recent federal policy. Several chapters provide possible solutions to psychometricians dealing with the technical challenges related to innovative items, cognitive diagnosis, and growth modelling in computer-based linear or adaptive tests in the K-12 setting.