Overview
- Presents a new discipline specific to educational assessment and crystalizes the integration of several methodologies in a unique way
- Extends hard-won psychometric insights to a larger universe of constructs, data types, and technological environments
- Provides the substantive context for harnessing the power of advanced data analytic methods to the particular problems of assessment
- Facilitates the development of new tests and applications by providing code for R and Python
Part of the book series: Methodology of Educational Measurement and Assessment (MEMA)
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Table of contents (14 chapters)
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Front Matter
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Conceptualization
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Front Matter
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Methodology
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Front Matter
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Keywords
- Methodologies of educational assessments
- Assessments in virtual settings
- Traditional assessments
- Center for Advanced Psychometrics
- Evidence identification
- Data modelling
- Prediction of students’ success
- Stochastic processes theory
- Computer-science-based-methods
- Theory-based psychometric approaches
- Code in R
- Code in Python
- Analyzing big data
About this book
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.
Editors and Affiliations
About the editors
Bibliographic Information
Book Title: Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessment
Book Subtitle: With Examples in R and Python
Editors: Alina A. von Davier, Robert J. Mislevy, Jiangang Hao
Series Title: Methodology of Educational Measurement and Assessment
DOI: https://doi.org/10.1007/978-3-030-74394-9
Publisher: Springer Cham
eBook Packages: Education, Education (R0)
Copyright Information: Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-74393-2Published: 14 December 2021
Softcover ISBN: 978-3-030-74396-3Published: 15 December 2022
eBook ISBN: 978-3-030-74394-9Published: 01 January 2022
Series ISSN: 2367-170X
Series E-ISSN: 2367-1718
Edition Number: 1
Number of Pages: X, 262
Number of Illustrations: 1 b/w illustrations
Topics: Assessment, Testing and Evaluation, Psychometrics, Statistics for Social Sciences, Humanities, Law