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Author: Alina A. von Davier
Publisher: Springer Nature
ISBN: 3030743942
Category : Education
Languages : en
Pages : 265
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Book Description
This book defines and describes a new discipline, named “computational psychometrics,” from the perspective of new methodologies for handling complex data from digital learning and assessment. The editors and the contributing authors discuss how new technology drastically increases the possibilities for the design and administration of learning and assessment systems, and how doing so significantly increases the variety, velocity, and volume of the resulting data. Then they introduce methods and strategies to address the new challenges, ranging from evidence identification and data modeling to the assessment and prediction of learners’ performance in complex settings, as in collaborative tasks, game/simulation-based tasks, and multimodal learning and assessment tasks. Computational psychometrics has thus been defined as a blend of theory-based psychometrics and data-driven approaches from machine learning, artificial intelligence, and data science. All these together provide a better methodological framework for analysing complex data from digital learning and assessments. The term “computational” has been widely adopted by many other areas, as with computational statistics, computational linguistics, and computational economics. In those contexts, “computational” has a meaning similar to the one proposed in this book: a data-driven and algorithm-focused perspective on foundations and theoretical approaches established previously, now extended and, when necessary, reconceived. This interdisciplinarity is already a proven success in many disciplines, from personalized medicine that uses computational statistics to personalized learning that uses, well, computational psychometrics. We expect that this volume will be of interest not just within but beyond the psychometric community. In this volume, experts in psychometrics, machine learning, artificial intelligence, data science and natural language processing illustrate their work, showing how the interdisciplinary expertise of each researcher blends into a coherent methodological framework to deal with complex data from complex virtual interfaces. In the chapters focusing on methodologies, the authors use real data examples to demonstrate how to implement the new methods in practice. The corresponding programming codes in R and Python have been included as snippets in the book and are also available in fuller form in the GitHub code repository that accompanies the book.
Author: Alina A. von Davier
Publisher: Springer Nature
ISBN: 3030743942
Category : Education
Languages : en
Pages : 265
Get Book Here
Book Description
This book defines and describes a new discipline, named “computational psychometrics,” from the perspective of new methodologies for handling complex data from digital learning and assessment. The editors and the contributing authors discuss how new technology drastically increases the possibilities for the design and administration of learning and assessment systems, and how doing so significantly increases the variety, velocity, and volume of the resulting data. Then they introduce methods and strategies to address the new challenges, ranging from evidence identification and data modeling to the assessment and prediction of learners’ performance in complex settings, as in collaborative tasks, game/simulation-based tasks, and multimodal learning and assessment tasks. Computational psychometrics has thus been defined as a blend of theory-based psychometrics and data-driven approaches from machine learning, artificial intelligence, and data science. All these together provide a better methodological framework for analysing complex data from digital learning and assessments. The term “computational” has been widely adopted by many other areas, as with computational statistics, computational linguistics, and computational economics. In those contexts, “computational” has a meaning similar to the one proposed in this book: a data-driven and algorithm-focused perspective on foundations and theoretical approaches established previously, now extended and, when necessary, reconceived. This interdisciplinarity is already a proven success in many disciplines, from personalized medicine that uses computational statistics to personalized learning that uses, well, computational psychometrics. We expect that this volume will be of interest not just within but beyond the psychometric community. In this volume, experts in psychometrics, machine learning, artificial intelligence, data science and natural language processing illustrate their work, showing how the interdisciplinary expertise of each researcher blends into a coherent methodological framework to deal with complex data from complex virtual interfaces. In the chapters focusing on methodologies, the authors use real data examples to demonstrate how to implement the new methods in practice. The corresponding programming codes in R and Python have been included as snippets in the book and are also available in fuller form in the GitHub code repository that accompanies the book.
Author: Patricia Martinková
Publisher: CRC Press
ISBN: 1000899179
Category : Psychology
Languages : en
Pages : 348
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Book Description
This book covers the computational aspects of psychometric methods involved in developing measurement instruments and analyzing measurement data in social sciences. It covers the main topics of psychometrics such as validity, reliability, item analysis, item response theory models, and computerized adaptive testing. The computational aspects comprise the statistical theory and models, comparison of estimation methods and algorithms, as well as an implementation with practical data examples in R and also in an interactive ShinyItemAnalysis application. Key Features: Statistical models and estimation methods involved in psychometric research Includes reproducible R code and examples with real datasets Interactive implementation in ShinyItemAnalysis application The book is targeted toward a wide range of researchers in the field of educational, psychological, and health-related measurements. It is also intended for those developing measurement instruments and for those collecting and analyzing data from behavioral measurements, who are searching for a deeper understanding of underlying models and further development of their analytical skills.
Author: Victoria Yaneva
Publisher: Taylor & Francis
ISBN: 1000904199
Category : Education
Languages : en
Pages : 339
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Book Description
Advancing Natural Language Processing in Educational Assessment examines the use of natural language technology in educational testing, measurement, and assessment. Recent developments in natural language processing (NLP) have enabled large-scale educational applications, though scholars and professionals may lack a shared understanding of the strengths and limitations of NLP in assessment as well as the challenges that testing organizations face in implementation. This first-of-its-kind book provides evidence-based practices for the use of NLP-based approaches to automated text and speech scoring, language proficiency assessment, technology-assisted item generation, gamification, learner feedback, and beyond. Spanning historical context, validity and fairness issues, emerging technologies, and implications for feedback and personalization, these chapters represent the most robust treatment yet about NLP for education measurement researchers, psychometricians, testing professionals, and policymakers. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-NonCommercial-No Derivatives 4.0 license.
Author: Anne Sinatra
Publisher: U.S. Army DEVCOM – Soldier Center
ISBN: 0997725834
Category : Computers
Languages : en
Pages : 160
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Book Description
This book is a resource for those who are new to intelligent tutoring systems (ITSs), as well as those with a great deal of experience with them. This is the tenth book in our Design Recommendations for Intelligent Tutoring Systems book series. The focus of this book is on Strengths, Weaknesses, Opportunities, and Threats (SWOT) Analyses of varying components of ITSs. Each chapter in the book represents a different topic area, and includes a SWOT analysis that is specific to that topic and how it relates to ITSs. This book can be read in order, or a reader can choose a specific topic area and move directly to that chapter. Each SWOT Analysis describes the current state of the topic area, and how the lessons learned from the analysis could be applied to the Generalized Intelligent Framework for Tutoring (GIFT) (Sottilare et al., 2012; Sottilare et al., 2017). GIFT is an ITS architecture that is open-source, modular, and domain independent (Sottilare et al., 2017). Each book in the design recommendations series has addressed a different ITS topic area, and how the work in each chapter can relate to and inform the GIFT architecture. GIFT has continually been in development, with features consistently being added to improve functionality, as well as reduce the skill requirement for authoring content in GIFT. GIFT is freely available in both downloadable and Cloud versions at https://www.GIFTtutoring.org.
Author: Cynthia Sistek
Publisher: Corwin Press
ISBN: 1071907433
Category : Education
Languages : en
Pages : 260
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Book Description
Help usher in a new era of student assessment This empowering guide revolutionizes the assessment process by putting students at the center. Dive into practical strategies and best practices for fostering social and emotional learning (SEL) competencies through student-centered assessments and discover how you can transform classrooms into inclusive spaces where learning thrives. Inside you′ll find Humanistic assessing practices to integrate into everyday teaching and learning Best practices for designing and implementing savvy SEL assessments Ways to develop a classroom that is student empowered and culturally relevant Rubrics, portfolios, and digital tools that demonstrate students’ competencies and knowledge through an SEL lens Explore dozens of practical examples, case studies, and field-tested activities that support research-based teaching and learning across the curriculum. Assessing Through the Lens of Social and Emotional Learning inspires educators to move beyond traditional testing to focus on nurturing and fostering skills that students will need for both academic and lifelong success.
Author: Lv, Zhihan
Publisher: IGI Global
ISBN:
Category : Computers
Languages : en
Pages : 332
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Book Description
The rapid adoption of deep learning models has resulted in many business services becoming model services, yet most AI systems lack the necessary automation and industrialization capabilities. This leads to heavy reliance on manual operation and maintenance, which not only consumes power but also causes resource wastage and stability issues during system mutations. The inadequate self-adaptation of AI systems poses significant challenges in terms of cost-effectiveness and operational stability. Principles and Applications of Adaptive Artificial Intelligence, edited by Zhihan Lv from Uppsala University, Sweden, offers a comprehensive solution to the self-adaptation problem in AI systems. It explores the latest concepts, technologies, and applications of Adaptive AI, equipping academic scholars and professionals with the necessary knowledge to overcome the challenges faced by traditional business logic transformed into model services. With its problem-solving approach, real-world case studies, and thorough analysis, the Handbook provides practitioners with practical ideas and solutions, while also serving as a valuable teaching material and reference guide for students and educators in AI-related disciplines. By emphasizing self-adaptation, continuous model iteration, and dynamic learning based on real-time feedback, the book empowers readers to significantly enhance the cost-effectiveness and operational stability of AI systems, making it an indispensable resource for researchers, professionals, and students seeking to revolutionize their research and applications in the field of Adaptive AI.
Author: PressGrup Academician Team
Publisher: Prof. Dr. Bilal Semih Bozdemir
ISBN:
Category : Education
Languages : en
Pages : 492
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Book Description
Psychology: Computer-Aided Numerical Methods Introduction to Numerical Methods in Psychology Advantages of Computer-Aided Numerical Analysis Data Collection and Preprocessing Linear Regression and Correlation Analysis Logistic Regression and Classification Principal Component Analysis (PCA) Cluster Analysis Time Series Analysis Bayesian Methods and Inference Monte Carlo Simulation Techniques Optimization Algorithms in Psychological Research Visualization and Interpretation of Results Practical Applications and Case Studies
Author: Marie Wiberg
Publisher: Springer Nature
ISBN: 3031045726
Category : Mathematics
Languages : en
Pages : 329
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Book Description
The volume represents presentations given at the 86th annual meeting of the Psychometric Society, held virtually on July 19–23, 2021. About 500 individuals contributed paper presentations, symposiums, poster presentations, pre-conference workshops, keynote presentations, and invited presentations. Since the 77th meeting, Springer has published the conference proceedings volume from this annual meeting to allow presenters to share their work and ideas with the wider research community, while still undergoing a thorough review process. This proceedings covers a diverse set of psychometric topics, including item response theory, Bayesian models, reliability, longitudinal measures, and cognitive diagnostic models.
Author: Hong Jiao
Publisher: IAP
ISBN:
Category : Computers
Languages : en
Pages : 242
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Book Description
With the exponential increase of digital assessment, different types of data in addition to item responses become available in the measurement process. One of the salient features in digital assessment is that process data can be easily collected. This non-conventional structured or unstructured data source may bring new perspectives to better understand the assessment products or accuracy and the process how an item product was attained. The analysis of the conventional and non-conventional assessment data calls for more methodology other than the latent trait modeling. Natural language processing (NLP) methods and machine learning algorithms have been successfully applied in automated scoring. It has been explored in providing diagnostic feedback to test-takers in writing assessment. Recently, machine learning algorithms have been explored for cheating detection and cognitive diagnosis. When the measurement field promote the use of assessment data to provide feedback to improve teaching and learning, it is the right time to explore new methodology and explore the value added from other data sources. This book presents the use cases of machine learning and NLP in improving the assessment theory and practices in high-stakes summative assessment, learning, and instruction. More specifically, experts from the field addressed the topics related to automated item generations, automated scoring, automated feedback in writing, explainability of automated scoring, equating, cheating and alarming response detection, adaptive testing, and applications in science assessment. This book demonstrates the utility of machine learning and NLP in assessment design and psychometric analysis.
Author: Tracy Kantrowitz
Publisher: Oxford University Press
ISBN: 0197611052
Category : Business & Economics
Languages : en
Pages : 585
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Book Description
"Technology-enhanced assessments for selection and development have flourished over the past several decades. Sophisticated assessment programs that weren't possible even a few years ago can now be assembled and launched on a global scale to measure almost any attribute in any language with greater realism, efficiency, and precision than ever before. Large-scale assessment applications have emerged where candidates are recruited online, automatically screened, assessed and prioritized, and presented with online interview questions based on the results of their assessments - all without any human contact. Many organizations have enthusiastically embraced these developments due to the obvious practical benefits and immediate payoff associated with increased efficiency and reduced costs to move candidates from recruitment through to selection"--
