Test Data Engineering

Name: Test Data Engineering
Brand: Springer Verlag, Singapore
SKU: 9789811699856
Price: 576.02 PLN
Availability: InStock

Latent Rank Analysis, Biclustering, and Bayesian Network

Autor: Kojiro Shojima

Język:

Oprawa: Twarda

Wydawca: Springer Verlag, Singapore

Dostępność: Dostępna u dostawcy

Wysyłamy za 10-18 dni

576.02 zł

This is the first technical book that considers tests as public tools and examines how to engineer a...

Informacje o książce

Autor

Kojiro Shojima

Język

Angielski

Oprawa

Książka - Twarda

Data wydania

2022

strony

579

EAN

9789811699856

Enbook ID

38574076

Wydawca

Springer Verlag, Singapore

Waga

1063

Wymiary

155 x 235 x 38

Pełny opis

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.

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Test Data Engineering

Latent Rank Analysis, Biclustering, and Bayesian Network

Informacje o książce

Pełny opis

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