|
Showing 1 - 8 of
8 matches in All Departments
Devising tests that evaluate a nation's educational standing and
implement efficacious educational reforms requires a careful
balance among the contributions of technology, psychometrics, test
design, and the learning sciences. Unlike other forms of adaptive
testing, multistage testing (MST) is highly suitable for testing
educational achievement because it can be adapted to educational
surveys and student testing. Computerized Multistage Testing:
Theory and Applications covers the methodologies, underlying
technology, and implementation aspects of this type of test design.
The book discusses current scientific perspectives and practical
considerations for each step involved in setting up an MST program.
It covers the history of MST, test design and implementation for
various purposes, item pool development and maintenance, IRT-based
and classical test theory-based methodologies for test assembly,
routing and scoring, equating, test security, and existing
software. It also explores current research, existing operational
programs, and innovative future assessments using MST. Intended for
psychologists, social scientists, and educational measurement
scientists, this volume provides the first unified source of
information on the design, psychometrics, implementation, and
operational use of MST. It shows how to apply theoretical
statistical tools to testing in novel and useful ways. It also
explains how to explicitly tie the assumptions made by each model
to observable (or at least inferable) data conditions. Winner of
the 2016 AERA Award for Significant Contribution to Educational
Measurement and Research Methodology The 2016 American Education
Research Association (AERA) Div. D award committee for Significant
Contributions to Educational Measurement and Research Methodology
has recognized unanimously this collaborative work advancing the
theory and applications of computerized MST. This annual award
recognizes published research judged to represent a significant
conceptual advancement in the theory and practice of educational
measurement and/or educational research methodology. The 2016 award
was made under the heading: Measurement, Psychometrics, and
Assessment. This collective work, published in 2014 as an edited
volume titled Computerized Multistage Testing: Theory and
Applications, was cited by the committee both for the originality
of the conceptual foundations presented in support of multistage
testing and for arguing persuasively for its potential impact on
the practice of educational measurement.
Devising tests that evaluate a nation's educational standing and
implement efficacious educational reforms requires a careful
balance among the contributions of technology, psychometrics, test
design, and the learning sciences. Unlike other forms of adaptive
testing, multistage testing (MST) is highly suitable for testing
educational achievement because it can be adapted to educational
surveys and student testing. Computerized Multistage Testing:
Theory and Applications covers the methodologies, underlying
technology, and implementation aspects of this type of test design.
The book discusses current scientific perspectives and practical
considerations for each step involved in setting up an MST program.
It covers the history of MST, test design and implementation for
various purposes, item pool development and maintenance, IRT-based
and classical test theory-based methodologies for test assembly,
routing and scoring, equating, test security, and existing
software. It also explores current research, existing operational
programs, and innovative future assessments using MST. Intended for
psychologists, social scientists, and educational measurement
scientists, this volume provides the first unified source of
information on the design, psychometrics, implementation, and
operational use of MST. It shows how to apply theoretical
statistical tools to testing in novel and useful ways. It also
explains how to explicitly tie the assumptions made by each model
to observable (or at least inferable) data conditions. Winner of
the 2016 AERA Award for Significant Contribution to Educational
Measurement and Research Methodology The 2016 American Education
Research Association (AERA) Div. D award committee for Significant
Contributions to Educational Measurement and Research Methodology
has recognized unanimously this collaborative work advancing the
theory and applications of computerized MST. This annual award
recognizes published research judged to represent a significant
conceptual advancement in the theory and practice of educational
measurement and/or educational research methodology. The 2016 award
was made under the heading: Measurement, Psychometrics, and
Assessment. This collective work, published in 2014 as an edited
volume titled Computerized Multistage Testing: Theory and
Applications, was cited by the committee both for the originality
of the conceptual foundations presented in support of multistage
testing and for arguing persuasively for its potential impact on
the practice of educational measurement.
The goal of this guide and manual is to provide a practical and
brief overview of the theory on computerized adaptive testing (CAT)
and multistage testing (MST) and to illustrate the methodologies
and applications using R open source language and several data
examples. Implementation relies on the R packages catR and mstR
that have been already or are being developed by the first author
(with the team) and that include some of the newest research
algorithms on the topic. The book covers many topics along with the
R-code: the basics of R, theoretical overview of CAT and MST, CAT
designs, CAT assembly methodologies, CAT simulations, catR package,
CAT applications, MST designs, IRT-based MST methodologies,
tree-based MST methodologies, mstR package, and MST applications.
CAT has been used in many large-scale assessments over recent
decades, and MST has become very popular in recent years. R open
source language also has become one of the most useful tools for
applications in almost all fields, including business and
education. Though very useful and popular, R is a difficult
language to learn, with a steep learning curve. Given the obvious
need for but with the complex implementation of CAT and MST, it is
very difficult for users to simulate or implement CAT and MST.
Until this manual, there has been no book for users to design and
use CAT and MST easily and without expense; i.e., by using the free
R software. All examples and illustrations are generated using
predefined scripts in R language, available for free download from
the book's website.
The goal of this guide and manual is to provide a practical and
brief overview of the theory on computerized adaptive testing (CAT)
and multistage testing (MST) and to illustrate the methodologies
and applications using R open source language and several data
examples. Implementation relies on the R packages catR and mstR
that have been already or are being developed by the first author
(with the team) and that include some of the newest research
algorithms on the topic. The book covers many topics along with the
R-code: the basics of R, theoretical overview of CAT and MST, CAT
designs, CAT assembly methodologies, CAT simulations, catR package,
CAT applications, MST designs, IRT-based MST methodologies,
tree-based MST methodologies, mstR package, and MST applications.
CAT has been used in many large-scale assessments over recent
decades, and MST has become very popular in recent years. R open
source language also has become one of the most useful tools for
applications in almost all fields, including business and
education. Though very useful and popular, R is a difficult
language to learn, with a steep learning curve. Given the obvious
need for but with the complex implementation of CAT and MST, it is
very difficult for users to simulate or implement CAT and MST.
Until this manual, there has been no book for users to design and
use CAT and MST easily and without expense; i.e., by using the free
R software. All examples and illustrations are generated using
predefined scripts in R language, available for free download from
the book's website.
KE is applied to the four major equating designs and to both Chain
Equating and Post-Stratification Equating for the Non-Equivalent
groups with Anchor Test Design. It will be an important reference
for several groups: (a) Statisticians (b) Practitioners and (c)
Instructors in psychometric and measurement programs. The authors
assume some familiarity with linear and equipercentile test
equating, and with matrix algebra.
This book is aimed at (a) practitioners who need to equate tests- including those with these responsibilities in testing companies, state testing agencies and school districts; (b) statisticians and other research workers interested in the theory behind such work and the use of model based statistical methods of data smoothing in applied work; (c) advanced graduate students in psychometric and measurement programs. While there are other books on test equating, and books of the use of kernel smoothing, no one has published any work on the kernel method of test equating. It is something of a unifying idea in equating and brings together several methods into an organized whole rather than treating them as a group of disparate methods.
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.
This edited volume provides a platform for experts from various
fields to introduce and discuss their different perspectives on the
topic of teamwork and collaborative problem solving. It brings
together researchers in organizational teaming, educational
collaboration, tutoring, simulation, and gaming as well as those
involved in statistical and psychometric process modelling. This
book seeks to channel this expertise towards advances in the
measurement and assessment of cognitive and non-cognitive skills of
individuals and teams.
|
You may like...
Southpaw
Jake Gyllenhaal, Forest Whitaker, …
DVD
R99
R24
Discovery Miles 240
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
Ab Wheel
R209
R149
Discovery Miles 1 490
Sound Of Freedom
Jim Caviezel, Mira Sorvino, …
DVD
R325
R218
Discovery Miles 2 180
|