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  • © 2021

Computational Psychometrics: New Methodologies for a New Generation of Digital Learning and Assessment

With Examples in R and Python

Editors:

(view affiliations)
  • 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

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eBook
USD 119.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-74394-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD 159.99
Price excludes VAT (USA)

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-x
  2. Conceptualization

    1. Front Matter

      Pages 7-7
    2. Virtual Performance-Based Assessments

      • Jessica Andrews-Todd, Robert J. Mislevy, Michelle LaMar, Sebastiaan de Klerk
      Pages 45-60
    3. Knowledge Inference Models Used in Adaptive Learning

      • Maria Ofelia Z. San Pedro, Ryan S. Baker
      Pages 61-77
  3. Methodology

    1. Front Matter

      Pages 79-79
    2. Concepts and Models from Psychometrics

      • Robert J. Mislevy, Maria Bolsinova
      Pages 81-107
    3. Bayesian Inference in Large-Scale Computational Psychometrics

      • Gunter Maris, Timo Bechger, Maarten Marsman
      Pages 109-131
    4. A Data Science Perspective on Computational Psychometrics

      • Jiangang Hao, Robert J. Mislevy
      Pages 133-158
    5. Supervised Machine Learning

      • Jiangang Hao
      Pages 159-171
    6. Unsupervised Machine Learning

      • Pak Chung Wong
      Pages 173-193
    7. Advances in AI and Machine Learning for Education Research

      • Yuchi Huang, Saad M. Khan
      Pages 195-208
    8. Time Series and Stochastic Processes

      • Peter Halpin, Lu Ou, Michelle LaMar
      Pages 209-230
    9. Social Networks Analysis

      • Mengxiao Zhu
      Pages 231-244
    10. Text Mining and Automated Scoring

      • Michael Flor, Jiangang Hao
      Pages 245-262

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.

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

Editors and Affiliations

  • Duolingo and EdAstra Tech, LLC, Newton, USA

    Alina A. von Davier

  • Educational Testing Service, Princeton, USA

    Robert J. Mislevy, Jiangang Hao

About the editors

 

Bibliographic Information

Buying options

eBook
USD 119.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-74394-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD 159.99
Price excludes VAT (USA)