Title Test data engineering : latent rank analysis, biclustering, and Bayesian network / Kojiro Shojima

Published Singapore : Springer, [2022] ©2022

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Description 1 online resource (xxii, 579 pages) : illustrations (chiefly color) Series Behaviormetrics - quantitative approaches to human behavior ; volume 13 Behaviormetrics ; v. 13. Contents Concept of Test Data Engineering -- Test Data and Item Analysis -- Classical Test Theory -- Item Response Theory -- Latent Class Analysis -- Biclustering -- Bayesian Network Model Summary This is the first technical book that considers tests as public tools and examines how to engineer and process test data, extract the structure within the data to be visualized, and thereby make test results useful for students, teachers, and the society. The author does not differentiate test data analysis from data engineering and information visualization. This monograph introduces the following methods of engineering or processing test data, including the latest machine learning techniques: classical test theory (CTT), item response theory (IRT), latent class analysis (LCA), latent rank analysis (LRA), biclustering (co-clustering), and Bayesian network model (BNM). CTT and IRT are methods for analyzing test data and evaluating students abilities on a continuous scale. LCA and LRA assess examinees by classifying them into nominal and ordinal clusters, respectively, where the adequate number of clusters is estimated from the data. Biclustering classifies examinees into groups (latent clusters) while classifying items into fields (factors). Particularly, the infinite relational model discussed in this book is a biclustering method feasible under the condition that neither the number of groups nor the number of fields is known beforehand. Additionally, the local dependence LRA, local dependence biclustering, and bicluster network model are methods that search and visualize inter-item (or inter-field) network structure using the mechanism of BNM. As this book offers a new perspective on test data analysis methods, it is certain to widen readers perspective on test data analysis. Bibliography Includes bibliographical references Notes Print version record Subject Data mining. Information visualization. Educational tests and measurements -- Data processing Bayesian statistical decision theory. Cluster analysis. Data Mining Análisis de datos Sistemas de visualización de la información Decisión bayesiana, Teoría de la Análisis de conglomerados Bayesian statistical decision theory Cluster analysis Data mining Educational tests and measurements -- Data processing Information visualization Estadística bayesiana. Anàlisi de conglomerats. Mineria de dades. Tests i proves en educació. Processament de dades. Visualització de la informació. Genre/Form Electronic books Llibres electrònics. Form Electronic book ISBN 9789811699863 9811699860 9788981169985 8981169985

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