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R. G. Almond, R. J. Mislevy, L. Steinberg, D. Yan, and D. M. Williamson: Bayesian Networks in Educational Assessment

R. G. Almond, R. J. Mislevy, L. Steinberg, D. Yan, and D. M. Williamson: Bayesian Networks in... Tech Know Learn (2019) 24:97–99 DOI 10.1007/s10758-016-9292-x BOOK REVIEW R. G. Almond, R. J. Mislevy, L. Steinberg, D. Yan, and D. M. Williamson: Bayesian Networks in Educational Assessment Springer, 2015 Fengfeng Ke Published online: 5 November 2016 Springer Science+Business Media Dordrecht 2016 1 Authors and Content of the Book Performance-based, diagnostic assessment that captures and scaffolds individual learner’s competency development is a critical element of learning environment design, especially when interactive and complex learning tasks are involved. Yet the design and imple- mentation of evidence-centered diagnostic assessment is challenging because: (a) the design of measurable performance tasks based on the underlying proficiency model is tricky, (b) the recording and coding of the performance data of each learner of a large sample can be complicated and time-consuming in comparison with grading in traditional testing, and hence (c) conducting a real-time diagnosis of the performance data to facilitate dynamic learner support and instructional planning for components of the competency is difficult. The book by Almond and his colleagues helps to address the aforementioned issues by explaining the potential of using graphical models, Bayesian network models in particular, to accumulate observed evidence about the state of proficiency of individual learners. All authors http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Technology, Knowledge and Learning" Springer Journals

R. G. Almond, R. J. Mislevy, L. Steinberg, D. Yan, and D. M. Williamson: Bayesian Networks in Educational Assessment

"Technology, Knowledge and Learning" , Volume 24 (1) – Nov 5, 2016

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References (3)

Publisher
Springer Journals
Copyright
Copyright © 2016 by Springer Science+Business Media Dordrecht
Subject
Education; Learning and Instruction; Mathematics Education; Educational Technology; Science Education; Creativity and Arts Education
ISSN
2211-1662
eISSN
2211-1670
DOI
10.1007/s10758-016-9292-x
Publisher site
See Article on Publisher Site

Abstract

Tech Know Learn (2019) 24:97–99 DOI 10.1007/s10758-016-9292-x BOOK REVIEW R. G. Almond, R. J. Mislevy, L. Steinberg, D. Yan, and D. M. Williamson: Bayesian Networks in Educational Assessment Springer, 2015 Fengfeng Ke Published online: 5 November 2016 Springer Science+Business Media Dordrecht 2016 1 Authors and Content of the Book Performance-based, diagnostic assessment that captures and scaffolds individual learner’s competency development is a critical element of learning environment design, especially when interactive and complex learning tasks are involved. Yet the design and imple- mentation of evidence-centered diagnostic assessment is challenging because: (a) the design of measurable performance tasks based on the underlying proficiency model is tricky, (b) the recording and coding of the performance data of each learner of a large sample can be complicated and time-consuming in comparison with grading in traditional testing, and hence (c) conducting a real-time diagnosis of the performance data to facilitate dynamic learner support and instructional planning for components of the competency is difficult. The book by Almond and his colleagues helps to address the aforementioned issues by explaining the potential of using graphical models, Bayesian network models in particular, to accumulate observed evidence about the state of proficiency of individual learners. All authors

Journal

"Technology, Knowledge and Learning"Springer Journals

Published: Nov 5, 2016

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