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Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: Good. It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience.

Soft Cover. Condizione: Good. Ex-library with the usual features. The interior is very clean and tight. Binding is good. Cover shows light wear. 314 pages. Ex-Library.

Paperback. Condizione: Very Good. Illustrated. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Paperback. Condizione: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Condizione: very_good. Book is in very good condition and may include minimal underlining highlighting. The book can also include "From the library of" labels. May not contain miscellaneous items toys, dvds, etc. . We offer 100% money back guarantee and 24 7 customer service.

Condizione: New.

Paperback. Condizione: New. The general theme of this book is to present the applications of artificial intelligence (AI) in test development. In particular, this book includes research and successful examples of using AI technology in automated item generation, automated test assembly, automated scoring, and computerized adaptive testing. By utilizing artificial intelligence, the efficiency of item development, test form construction, test delivery, and scoring could be dramatically increased.Chapters on automated item generation offer different perspectives related to generating a large number of items with controlled psychometric properties including the latest development of using machine learning methods. Automated scoring is illustrated for different types of assessments such as speaking and writing from both methodological aspects and practical considerations. Further, automated test assembly is elaborated for the conventional linear tests from both classical test theory and item response theory perspectives. Item pool design and assembly for the linear-on-the-fly tests elaborates more complications in practice when test security is a big concern. Finally, several chapters focus on computerized adaptive testing (CAT) at either item or module levels. CAT is further illustrated as an effective approach to increasing test-takers' engagement in testing.In summary, the book includes both theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to automate the process of test development. While some of these automation processes have become common practices such as automated test assembly, automated scoring, and computerized adaptive testing, some others such as automated item generation calls for more research and exploration. When new AI methods are emerging and evolving, it is expected that researchers can expand and improve the methods for automating different steps in test development to enhance the automation features and practitioners can adopt quality automation procedures to improve assessment practices.

Paperback. Condizione: New. This book introduces theories and practices for using assessment data to enhance learning and instruction. Topics include reshaping the homework review process, iterative learning engineering, learning progressions, learning maps, score report designing, the use of psychosocial data, and the combination of adaptive testing and adaptive learning. In addition, studies proposing new methods and strategies, technical details about the collection and maintenance of process data, and examples illustrating proposed methods and software are included.Chapters 1, 4, 6, 8, and 9 discuss how to make valid interpretations of results and achieve more efficient instructions from various sources of data. Chapters 3 and 7 propose and evaluate new methods to promote students' learning by using evidence-based iterative learning engineering and supporting the teachers' use of assessment data, respectively. Chapter 2 provides technical details on the collection, storage, and security protection of process data. Chapter 5 introduces software for automating some aspects of developmental education and the use of predictive modeling. Chapter 10 describes the barriers to using psychosocial data for formative assessment purposes. Chapter 11 describes a conceptual framework for adaptive learning and testing and gives an example of a functional learning and assessment system.In summary, the book includes comprehensive perspectives of the recent development and challenges of using test data for formative assessment purposes. The chapters provide innovative theoretical frameworks, new perspectives on the use of data with technology, and how to build new methods based on existing theories. This book is a useful resource to researchers who are interested in using data and technology to inform decision making, facilitate instructional utility, and achieve better learning outcomes.

Condizione: New.

Paperback. Condizione: New. 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.

Condizione: New.

Soft cover. Condizione: New. Language:Chinese.Author:HONG EN ER TONG JIAO YU YAN JIU ZHONG XIN.Binding:Soft cover.Publisher:Beijing Aerospace Press; 1st Edition.

Condizione: New.

Paperback or Softback. Condizione: New. Test Fairness in the New Generation of Large-Scale Assessment. Book.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

paperback. Condizione: New. Language:Chinese.Publisher: East China Normal University Press Pub. Date :1996-11. Pages Number: 370 Publisher: East China Normal University Press Pub. Date :1996-11. how to adapt to life and maintaining mental health (Adaptation to Life) is a preliminary study summary. it is mainly through scientific observation and statistical analysis to prove the objective existence of psychological defense mechanisms. Psychoanalytic theory of the so-called psychological defense mechanisms (defense mechan.

Condizione: New. Enhancing Effective Instruction and Learning Using Assessment Data (Paperback or Softback).

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: New.

paperback. Condizione: New. Ship out in 2 business day, And Fast shipping, Free Tracking number will be provided after the shipment.Paperback Language: Chinese. Macro chapter published national accounting professional and technical qualification examinations exam sprint proposition prediction papers: 2012 mid-level accounting qualifications and economic law closely linked to the outline. the authority to write. based on examination. to reflect the focus of a comprehensive test sites hit rate question the amount of analytical detail.Four Satisfaction guaranteed,or money back.

paperback. Condizione: New. Ship out in 2 business day, And Fast shipping, Free Tracking number will be provided after the shipment.Paperback Language: Chinese. Macro chapter published national accounting professional and technical qualification examinations exam sprint propositional prediction examination papers: the 2012 mid-level qualifications in accounting and financial management closely the outline. the authority to write. based on examination. to reflect the focus of a comprehensive test sites hit rate question the amount of analytical detail.Four Satisfaction guaranteed,or money back.

Condizione: As New. Unread book in perfect condition.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: New. 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.

Condizione: As New. Unread book in perfect condition.