A new person‐fit method based on machine learning in CDM in education

Autor:	Zhemin Zhu, David Arthur, Hua‐Hua Chang
Rok vydání:	2022
Předmět:	Machine Learning Statistics and Probability Arts and Humanities (miscellaneous) Research Design Educational Status Humans Computer Simulation Educational Measurement General Medicine General Psychology
Zdroj:	British Journal of Mathematical and Statistical Psychology. 75:616-637
ISSN:	2044-8317 0007-1102
Popis:	Cognitive diagnosis models have become popular in educational assessment and are used to provide more individualized feedback about a student's specific strengths and weaknesses than traditional total scores. However, if the testing data are contaminated by certain biases or aberrant response patterns, such predictions may not be accurate. The current research objective is to develop a new person-fit method that is based on machine learning and improves the functionality of existing person-fit methods. Various simulations were designed under three aberrant conditions: cheating, sleeping and random guessing. Simulation results showed that the new method was more powerful and effective than previous methods, especially for short-length tests.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97010714048e53e74c86ffc403c1286b https://doi.org/10.1111/bmsp.12270 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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