Item difficulty prediction using computational psychometrics and linguistic algorithms

Authors: Jana Dlouhá, Lubomír Štěpánek, Patrícia Martinková
Publication year: 2022
Publication type: Abstrakt
Periodical: IMPS 2022 International Meeting of the Psychometric Society. Book of Abstracts (Talks, Posters)
Projects: Advances in educational assessment: Analytical support for educational test development
Bibliographic citation: DLOUHÁ, Jana, ŠTĚPÁNEK, Lubomír, MARTINKOVÁ, Patrícia. Item difficulty prediction using computational psychometrics and linguistic algorithms. In: IMPS 2022 International Meeting of the Psychometric Society. Book of Abstracts (Talks, Posters). Bologna: Psychometric Society, 2022, S. 169-169. Dostupné z: https://www.psychometricsociety.org/sites/main/files/file-attachments/imps2022-version-7-abstract.pdf?1656714871
ID: 570048
Online record: View online record

Abstract

ZÁKLADNÍ ÚDAJE: IMPS 2022 International Meeting of the Psychometric Society. Book of Abstracts (Talks, Posters). Bologna: Psychometric Society, 2022. s. 169-169. [IMPS 2022. International Meeting of the Psychometric Society. 11.07.2022-15.07.2022, Bologna]. ABSTRAKT: Item characteristics such as difficulty or discrimination power are typically estimated from data. When little or no data are available at the pre-test, the test developers rely on their experience in how items of different content and wording influence item characteristics. In this work, we explore various item features gathered from text analysis of item wording to predict item difficulty. We illustrate the methods using the English language test of the Czech matura exam.