| Dewey Class |
300.727 |
| Title |
Test Data Engineering ( EBook) : Latent Rank Analysis, Biclustering, and Bayesian Network / / by Kojiro Shojima. |
| Author |
Shojima, Kojiro |
| Other name(s) |
SpringerLink (Online service) |
| Edition statement |
1st ed. 2022. |
| Publication |
Singapore : : Springer Nature Singapore : : Imprint: Springer, , 2022. |
| Physical Details |
XXII, 579 p. 242 illus., 216 illus. in color. : online resource. |
| Series |
Behaviormetrics: Quantitative Approaches to Human Behavior 2524-4035 ; ; 13 |
| ISBN |
9789811699863 |
| Summary Note |
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. .: |
| Contents note |
Concept of Test Data Engineering -- Test Data and Item Analysis -- Classical Test Theory -- Item Response Theory -- Latent Class Analysis -- Biclustering -- Bayesian Network Model. |
| Mode of acces to digital resource |
Digital reproduction.- |
| Cham : |
| Springer International Publishing, |
| 2022. - |
| Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format. |
| System details note |
Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users). |
| Internet Site |
https://doi.org/10.1007/978-981-16-9986-3 |
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